ABSTRACT. China has abundance of mineral resources such as magnesite, hematite and bauxite, which constitute a key component of its economy. The relatively low grade, and the widely varying and complex compositions of the raw extracts, however, pose difficult processing challenges including specialized equipment with excessive energy demands. The energy intensive furnaces together with widely uncertain features of the extracts form hybrid complexities of the system, where the existing modeling, optimization and control methods have met only limited success. Currently, the mineral processing plants generally employ manual control and are known to impose greater demands on the energy, while yielding unreasonable waste and poor operational efficiency. The recently developed Cyber-Physical System (CPS) provides a new key for us to address these challenges. The idea is to make the control system of energy intensive equipment into a CPS, which will lead to a smart optimization control system.
This talk presents the syntheses and implementation of a smart optimization control system for energy-intensive equipment under the framework of CPS. The proposed smart optimization control system consists of three main functions: (i) process control; (ii) setpoint optimization control; and (iii) fault diagnosis and self-recovery control. The key in realizing the above functions is the algorithm structure which is able to integrate control, optimization, fault diagnosis and self-recovery control together seamlessly.  This talk introduces the algorithm structure for integrated implementation of setpoint optimization control, process control and fault diagnosis and self-recovery control.
Hardware and software platform of smart optimization control system for energy-intensive equipment is then briefly introduced, which adopts embedded control system, wireless network and industrial cloud. It not only realizes the functions of computer control system using DCS (PLS), optimization computer and fault diagnosis computer, but also achieves the functions of mobile and remote monitoring for industrial process. 
Then, using fused magnesium furnace as an example, a hybrid simulation system for smart optimization control system for energy-intensive equipment developed by our team is introduced. The results of simulation experiments show the effectiveness of the proposed method that integrates the process control, setpoint optimization control and fault diagnosis and self-recovery control in the framework of CPS.
The industrial application of the proposed smart optimization control system is also discussed. It has been successfully applied to the largest magnesia production enterprise in China, resulting in great returns. Finally, future research on the smart optimization control system is outlined.

ABSTRACT. The present study introduces a real-time control algorithm for applications involving energy maximisation and subject to technological limitations. In particular, the development of controllers for wave energy converters (WECs) is challenging, since immersed WECs are subject to fluid-interaction forces, generating unusual terms in the system dynamic equations, such as radiation force or excitation forces.

Various control strategies were recently developed and more advance control algorithms are currently applied to WEC models, such as model predictive control (MPC) [1], or pseudospectral optimal control [2]. Such control strategies, capable of maximizing the energy production while ensuring path constraints are observed, are more realistic and applicable in real conditions which involve technological limitation aspects. Pseudospectral control presents a good balance between computational time and solution accuracy. Pseudospectral optimal control was introduced in the wave energy field in [3], dealing with fixed period control problems.

This paper presents a receding horizon type control, based on the pseudospectral approach. Dealing with irregular waves on a fixed control horizon, the presented control needs to work with non-periodic functions, implying the change of the basis functions involved in the description of the state and control variables, compared to the fixed-period problem. In particular, a basis consisting of half-range Chebyshev Fourier (HRCF)functions is shown to be an ideal choice. The receding horizon control is applied to a generic heaving buoy WEC and compared with a standard MPC algorithm, showing good comparative performance and encouraging computational characteristics.

ABSTRACT. The effectiveness of two discrete control strategies, Latching and Declutching, has been examined for an Oscillating Wave Surge Converter using numerical simulations. A simplified hydrodynamic model of a submerged oscillating plate and a simple damper PTO were used to model the forces acting on the device. As expected from looking at the avaialble literature, both Latching and Declutching control returned a significant increase in power in the appropriate frequency regions with an unlimited PTO stroke length. However, when realistic position limitations and damping ratios were introduced, no significant practical advantage could be found in implementing either control strategy for this particular device configuration.

ABSTRACT. In order to predict future observations of a noise-driven system, we have to find a model that exactly or at least approximately describes the behavior of the system so that the current system state can be recovered from past observations. However, sometimes it is very difficult to model a system accurately, such as real ocean waves. It is therefore particularly interesting to analyze ocean wave properties in the time-domain using autoregressive moving average (ARMA) models. Two ARMA/AR based models and their equivalent state space representations will be used for predicting future ocean wave elevations, where unknown parameters will be determined using linear least squares and auto-covariance least squares algorithms. Compared to existing wave prediction methods, in this paper (i) an ARMA model is used to enhance the prediction performance, (ii) noise covariances in the ARMA/AR model are computed rather than guessed and (iii) we show that, in practice, low pass filtering of historical wave data does not improve the forecasting results.

ABSTRACT. In recent years the control of Wave Energy Conversion (WEC) systems has been a challenging research topic. A 1/50th scale buoy system has been constructed at the University of Hull for modelling, verification and control. This study focuses on the time-varying state space model development of this direct-drive WEC with a tubular permanent magnet linear generator (TPMLG). A new form of control is developed, with (i) an adaptive impedance tuning system based on a mechanical-electrical analogue, and (ii) position and current tracking for power maximisation, physical constraint realisation and generator dynamic linearisation. Simulation results of regular and irregular waves indicate this control strategy can achieve an acceptable energy conversion efficiency.

ABSTRACT. This paper investigates the use of field weakening to improve the electrical power capture of a wave-to-wire wave energy conversion system. The system consists of a Linear Permanent Magnet Generator (LPMG) as the power take off, which produces a physical PTO force that is controlled by a machine side voltage source converter (VSC). The electrical power is then maximised using Model Predictive Control (MPC), in accordance with the systems constraints. Field weakening is then included in the systems optimisation, where this paper shows how field weakening can increase the PTO force range, hence, increasing feasibility and decreasing the chances of permanent damage during wave energy periods of low DC-link voltage.

ABSTRACT. We address the design of the Power Takeoff (PTO) device of a wave energy conversion system through direct optimisation of the parameters of a mechanical network according to an optimisation criterion linked to power absorption performance. The results are illustrated through simulations and the behaviours of different PTO realisations are compared.

ABSTRACT. This paper presents the design of an inclination- and azimuth-hold controllers and their subsequent stability and performance analysis for directional drilling tools as typically used in the oil industry. Using an input transformation developed in earlier work that partially linearizes and decouples the plant dynamics of the directional drilling tool, a bilinear model of the directional drilling tool is developed and is used as the basis for Bilinear PI controller design. Results for a transient simulation of the proposed BPI controller are presented and compared with that of the PI controller of the earlier work. It is presented that BPI controller gives more consistent responses over a broader operating range compared to the PI controller. In addition, the effect of time delay on the feedback measurements with respect to the stability and performance is investigated in the simulations.

ABSTRACT. This paper exploits the idea of integrating the outputs of several optical flow sensors (OFSs) and a micro-electro-mechanical systems (MEMS) inertial measurement unit (IMU). Several optical flow sensors are fixed on an unmanned aerial vehicle (UAV). Their locations and orientations are different so as to detect the optical flow from different directions. By fusing the optical flow information and inertial navigation system (INS) measurements using an extended Kalman filter (EKF), the INS errors can be estimated and compensated. The integration of optical flow sensors and MEMS-based IMU provides a small size, low cost and self-contained scheme for UAVs navigation at low height above the ground in GPS denied environments. The preliminary simulation results presented in this article illustrate the effectiveness of the proposed integrated navigation scheme.

ABSTRACT. The dynamics of a robotic hand is highly nonlinear and needs nonlinear feedback control strategies for its functionality. One of the highly promising and rapidly developing techniques for nonlinear, optimal, feedback control is based on the State Dependent Differential Riccati Equation (SD-DRE), also commonly called SDRE. In this technique, an analytical approach is used to transform the original nonlinear differential Riccati equation to a linear differential Lyapunov equation that can be solved in closed form at each time step in real time. In the present case of optimal tracking problems, it is necessary to formulate an additional State Dependent Differential Vector Equation (SD-DVE) to be simultaneously solved with the SD-DRE. This paper presents a unique application of the SD-DRE and SD-DVE strategies for optimal tracking of the robotic hand. Simulations with the finite-horizon optimal tracking controller for a two-link thumb of a robotic hand are given to support the effectiveness of the proposed technique.

ABSTRACT. This paper studies leader-following consensus of a class of heterogeneous multi-agent systems composed of first-order and second-order agents with delayed and intermittent communications. We propose a distributed consensus algorithm based on the delayed and intermittent information of neighboring agents. Some sufficient conditions are obtained to guarantee the consensus of heterogeneous multi-agent systems in terms of bilinear matrix inequalities (BMIs). Meanwhile, the relationship between communication duration and time delay over each control period is sought out. Finally, the effectiveness of the main results is illustrated by numerical simulations.

ABSTRACT. This paper presents a survey of different control methods deployed for spacecraft attitude control with a focus on strengths, weaknesses and opportunities for improvement. Common methods discussed include Sliding Mode Control (SMC), Unscented Kalman Filter (UKF) and Adaptive Control. Common challenges are the disturbances and uncertainties in space and produced by the spacecraft. This overview is used as a foundation for proposing useful research directions for developing improved control methods for the spacecraft attitude and orientation, with core requirements being low cost, robustness, precision, high efficiency and low computational load. The paper also summarises key actuation types used for satellite orientation, magnetics actuator and reaction wheels actuator, as the control strategy deployed, robustness and performance have strong links to the actuation.

ABSTRACT. The ability of autonomous vehicles to successfully replace human drivers depends on their capability to plan safe, efficient and usable paths in dynamically evolving traffic scenarios. This challenge gets more difficult when the autonomous vehicle has to drive through complex scenarios such as intersections that demand interactive behaviour between vehicles. Many autonomous vehicle demonstrations over the last few decades have highlighted the limitations in the current state-of-the-art of path planning solutions. They have been found to be inefficient and sometimes unsafe when tackling interactively demanding scenarios. The generic path planning solutions consists of three planners, a “global path planner”, a “behaviour planner” and a “local path planner. In this paper we establish that the “behaviour planner” is the limitation of a successful path planning solution, after reviewing the individual planners and the associated solutions. In this paper a new adaptive tactical behaviour planner is proposed to overcome the limitations. This planner is motivated by how expert human drivers behave in interactive scenarios, and is made up of a three modules architecture. The paper describes the individual modules, and also highlights how they play a part in the overall behaviour selection for the autonomous vehicle. The paper is concluded by a discussion on how this proposed planner generates safe and efficient behaviours in complex dynamic traffic scenarios by considering a case of a roundabouts not controlled by traffic signals.

ABSTRACT. This paper extends efficient robust model predictive control (MPC) approaches for linear parameter varying (LPV) systems to tracking scenarios. A dual mode approach is used and future information about target changes is included in the optimisation tracking problem. The controller guarantees recursive feasibility by adding an artificial target as an extra degree of freedom. Convergence to admissible targets is ensured by constructing a robustly invariant set to track any admissible target. The efficacy of the proposed algorithm is demonstrated by $MATLAB$ simulations.

ABSTRACT. This work proposes the use of Laguerre function in Predictive Functional Control (PFC) to produce well-posed decision making. The constant control input assumption of a classical PFC is replaced with the Laguerre polynomial and the steady state input of a system. With this slight modification, better consistency between model predictions and an actual system behaviour is achieved. In addition, the effectiveness of desired closed-loop time constant as PFC tuning parameter becomes more significant. The coding and tuning processes of the proposed approach are very straightforward and in line with the key selling points of PFC.

ABSTRACT. This paper presents a new variable gain robust state observer for a class of uncertain linear systems. The robust state observer proposed in this paper is composed of fixed and variable gain matrices. In this paper, LMI-based sufficient conditions for the existence of the proposed robust state observer are given. Finally, a simple illustrative example is included.

ABSTRACT. Discrete linear repetitive processes operate over a subset of the upper-right quadrant of the 2D plane. They arise in the modeling of physical processes and also the existing systems theory for them can be used to effect in solving control problems for other classes of systems, including iterative learning control design. This paper uses a version of Kalman-YakubovichPopov (KYP) Lemma to develop new linear matrix inequality (LMI) based stability conditions and an output control law design algorithm in the presence of polytopic uncertainty in the process model. The new algorithm results in a static output feedback control law that ensures robust stability along the pass and meets the control requirements over finite frequency ranges. A numerical example to illustrate the application of the new design algorithm concludes the paper.

ABSTRACT. Frequency domain based design methods are investigated for the design and tuning of fractional-order PID for scalar applications. Since Ziegler-Nichol’s tuning rule and other algorithms cannot be applied directly to tuning of fractional-order controllers, a new algorithm is developed to handle the tuning of these fractional-order PID controllers based on a single frequency point just like Ziegler-Nichol’s rule for inter order PID. Critical parameters of the system are obtained at the ultimate point and the controller parameters are calculated from these critical measurements to meet design specifications. Thereafter, fractional order is obtained to meet a specified robustness criteria which is the phase-invariability against gain variations around the phase cross-over frequency. Results are simulated on second –order plus dead time plant to demonstrate both performance and robustness.

ABSTRACT. The main objectives of this paper are to study the impact of disturbances in a natural gas processing train in the upstream oil & gas fields and to validate a representative model which can be used for developing/testing a swift and anticipatory control system. The impact of two different causes of process disturbances on a gas phase train comprising three main processes connected in series is presented. The paper provides answers about how feed disturbances, and process unit malfunctions effect series connected processes, and more specifically Gas Sweetening, Gas Dehydration, and Hydrocarbon Dew-Pointing units.

ABSTRACT. The linearised Navier-Stokes equations form the basis of many models used in the design of feedback controllers for fluid flows. This set of coupled, partial differential algebraic equations (PDAEs) presents several numerical problems upon spatial discretisation, and whilst these problems are relatively well understood in the computational fluid dynamics (CFD) community, little attention has been paid to their system theoretic model analogies. This paper considers the problem known in the fluid dynamics community as the `checkerboard instability', and reconciles current understanding of this phenomenon from a systems' theory perspective, whereby frequency domain analysis elegently explains the phenomenon. We report work in progress on deriving a discretised version of the linearised Navier-Stokes equations that avoids this problem.

ABSTRACT. PISim is a new piece of software that is being developed to support teaching and learning in process control. The ideas behind PISim are discussed and the results of two tests of the alpha build (with industrial participants and as part of a scheduled class) are presented. The effect of the test results on the direction of future development is also considered.

ABSTRACT. Many modern marine craft rely on guidance, navigation and control systems in order to operate safely and efficiently. However, testing of such systems via sea-trials can be a costly and time-consuming exercise. Boat dynamics simulators offer a quicker and cheaper alternative for control system validation. In this paper, we present the Sheffield Wave Environment Model (SWEM), a sea-surface simulation environment which can be used in conjunction with a boat dynamics simulator. SWEM models the effects of ocean swell, gusting local wind, surface current and finite water depth on sea-surfaces. This paper outlines the physical models used in SWEM as well as providing a tutorial on its functionality.

ABSTRACT. This investigation assesses the potential fuel saving benefits that could be gained through wind optimised flight trajectories. This question is posed on a 3 dimensional fixed flight network consisting of discrete waypoints which is representative of the size of Europe. The optimisation implements Dijkstra’s shortest path algorithm to compute the minimum fuel burn route through a network and compares this to the fuel burn for the shortest distance route. Particular effort is applied to testing the repeatability and robustness of the results. This is achieved through a sensitive analysis based on a number of identified model parameters relating to the setup of the flight network. The results of this study show fuel savings between 1.0% – 10.3%, and suggest that the benefits of wind optimal flight trajectories are significant.

ABSTRACT. This paper describes an Intelligent Foresight (IF) method for improving the response time of a UAV routing optimisation, using the optimal solution of a smaller size scenario, as a warm-start for the Ant Colony Optimisation. The method aims to predict the revised routing and allocation of UAVs in the event of a new task arising. The new task location is not known: solutions are calculated for a range of possible locations. Therefore, a smart way of sampling and tessellating the plane is introduced to distinguish the areas where the solution will not change if that task is added. This hybrid optimisation was created to offer near-optimal solutions, under the condition that its computational time would be less than an exact method would require to solve only one scenario. The IF method was tested for a variety of scenarios and benchmarked against the Gurobi software. The results showed that the IF offers a good approximation of how the solution will change with its computational time remaining approximately the same, regardless the size or the complexity of the scenarios solved.

ABSTRACT. Begin Robotics is a successful open online course developed at the University of Reading, run on the FutureLearn platform, for which around 25,000 participants have enrolled in its first three runs. Whilst it is aimed at introducing robotics and the associated subjects of cybernetics, artificial intelligence, control and haptics to Key Stage 3 pupils, it has been taken by other groups from around the world. This paper discusses how Control Engineering is introduced in an accessible way, and how it has used in undergraduate degrees.

ABSTRACT. Multiple agents - sometimes referred to as swarm of agents - and their control have been seeking interest significantly over the course of recent years. Their ability to move in desired formations and perform synchronized task has been the key arena in their development. In this research work, design of the continuous sliding mode controller for the output regulation of a multi agent system is studied. The idea of using conditional servocompensators for improving transient performance while achieving steady-state accuracy was introduced in the literature and has been shown to be a useful tool to achieve regulation of minimum phase nonlinear systems. We extend the use of the same approach to a class of multi-agent systems comprising of finite number of agents. The control scheme presented in this work is based on sliding mode control technique and incorporates a conditional servocompensator. Closed-loop analysis under the proposed control scheme for the multi-agent system is provided. Simulation results in the form of output trajectories of individual agents and error convergence are provided to illustrate the discussed approach.

ABSTRACT. In this paper, the security issue is investigated for networked control systems (NCSs) where the physical plant is controlled by a remote observer-based controller. The communication channel from system measurement to remote control centre is vulnerable to attacks from malicious adversaries. Here, false data injection (FDI) attacks are considered. The aim is to find the so-called insecurity conditions under which the NCS is insecure in the sense that there exist FDI attacks that can bypass the anomaly detector but still destabilize the overall system. In particular, a new necessary and sufficient condition for the insecurity is derived when the communication channel is compromised by the adversary. Moreover, a specific algorithm is proposed with which the NCS is shown to be insecure. A simulation example is utilized to demonstrate the usefulness of the proposed conditions/algorithms in the secure control problem.

ABSTRACT. This paper presents an approach for output feedback distributed model predictive control for dynamically coupled linear, time-invariant systems sharing constraints. Controllers use output feedback tube model predictive control to regulate their respective systems and reject disturbances arising from mutual disturbance and estimation errors arising from measurement noise. Moreover, controllers share predicted trajectories in order to check satisfaction of shared constraints on inputs and/or states, and take remedial action only when necessary. The result is an algorithm that achieves guaranteed recursive feasibility and stability, despite the two sources of coupling, without iteration or negotiation between controllers.

ABSTRACT. A large-scale interconnected system consists of large numbers of coupled subsystems. This coupling gives rise an important problem of integrating the designs of fault estimation (FE) and fault-tolerant control (FTC) in the presence of unexpected faults. This paper proposes an integrated FE/FTC design for large-scale interconnected systems with uncertain nonlinear interactions and unknown bounded actuator faults. A decentralized FTC controller is developed to guarantee the robust stability of the overall interconnected system, using the state/fault estimates obtained simultaneously by a decentralized unknown input observer. The observer and controller gains are solved simultaneously using a single-step linear matrix inequality (LMI) formulation. The performance effectiveness of the proposed design is demonstrated by applying it to the stabilization of a 3-machine power system.

ABSTRACT. This work presents significant developments in Networked Control Systems based on PID, Internal Model Control and Smith Predictor algorithms. The main purpose of this research paper is to study the performance and robustness offered by these control design methods in handling the challenging control problem encountered with systems subject to time-varying delays and dropouts. It is expected that proposed design methods achieve design requirements such as margins of robustness, performance criterions and stability conditions while the simplicity and flexibility of the controller are preferred. Performance of these controllers is evaluated and extensive simulations of these methods are presented using Matlab TrueTime toolbox.

ABSTRACT. This paper proposes an interval observer design for a large class of uncertain LTI systems. The proposed methodology estimates simultaneously the upper and lower bounds of unmeasurable states and unknown inputs. The initial model is transformed into a singular model representation without unknown input reducing the conservatism related to the propagation of uncertainties in set membership approach. An example is proposed to demonstrate the efficiency of the method.

ABSTRACT. In modern manufacturing systems, the failure of machine tools may cause unexpected system breakdown and bring about tremendous financial losses. With an effective tool condition monitoring (TCM), unnecessary downtime for maintenance can be reduced. Unfortunately, machine tool dynamics are complex, and the accurate relationship between monitoring signals and the tool health states is difficult to describe. In this work, the aim of tool condition monitoring is to estimate and predict the unobserved degree of the tool wear on-line by using the observed raw monitoring sensors. We take a Bayesian nonparametric approach to construct the relationship between raw force signals and the dynamic tool wear accumulation process. Using a Dirichlet process prior over mixture weights, we learn an infinite health state mixture model from training data to describe the continuous wear accumulation process. The nonparametric nature of our model allows control of the model size and self-adaption of the model parameters, and the use of Bayesian method significantly prevents under-fitting and avoids over-fitting. To validate the effectiveness of our model, the proposed approach is applied on the real data from a high-speed CNC milling machine cutters.

ABSTRACT. In this paper, an operator based robust nonlinear fault tolerance system design for fault signal to the sensor of MIMO microreactor system with Peltier devices is proposed by using robust right coprime factorization approach. In details, first, a mathematical model of the microreactor is described. Next, an operator based nonlinear feedback tracking control system is given. By compensating the effects of uncertainties and unknown interference of the controlled object, detection of the fault signal becomes more clearly. After finishing the compensation of the effects of uncertainties and unknown interferences, fault signal detection works. The fault signal of control system is analyzed by using two sorts of operators. The effectiveness of the proposed design scheme is confirmed by experimental results.

ABSTRACT. This paper presents a novel approach to the diagnosis of blade faults in an electric thruster motor of unmanned underwater vehicles (UUVs) under stationary operating conditions. The diagnostic approach is based on the use of discrete wavelet transforms (DWT) as a feature extraction tool and a dynamic neural network (DNN) for fault classification. The DNN classifies between healthy and faulty conditions of the trolling motor by analyzing the stator current and vibration signals. To overcome feature redundancy, which affects diagnosis reliability, the Orthogonal Fuzzy Neighborhood Discriminant Analysis (OFNDA) approach is found to be the most effective. Four faulty conditions were analyzed under laboratory conditions, and the results obtained from real-time simulation demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults faster and more accurately.

ABSTRACT. In order to effectively identify industrial process faults, an improved Fisher discriminant analysis (FDA) method,referred to as the statistics local Fisher discriminant analysis (SLFDA), is proposed for fault classification. For mining statistics information hidden in process data, statistics pattern analysis is firstly applied to transform the original measured variables into the corresponding statistics, including second-order and higher-order ones. Furthermore, considering the local structure characteristics of fault data, local FDA (LFDA) is performed which computes the discriminant vectors by modifying the optimization objective with local weighting factor. Simulation results on the benchmark Tennessee Eastman process show that the proposed SLFDA has a better fault classification performance than the FDA and LFDA methods.

ABSTRACT. Main movement of a galvano mirror is the rolling motion. However, since the structure of galvano mirror is cantilever, a mirror is vertically vibrated against mirror reflective surface. This paper considers the suppression of the above vibration, which is called pitching vibration. The mirror is attached at the end of a motor using a coupling with screws. The pitching vibration is related to the coupling rigidity between the mirror and the motor shaft. To suppress the pitching vibration, material of screws is exchanged from metal to plastic and any visco-elastic materials are inserted on the coupling.

ABSTRACT. In the application of hydraulic continuous rotation motors used in valve control electro-hydraulic servo system, the long distance between the electro-hydraulic servo valves and the hydraulic motors caused long pipeline effect, which results in a decreased natural frequency and delayed dynamic response of the hydraulic system. This paper presents mathematical model and characteristic analysis of the dynamic characteristic of the system, including the long pipeline effect. Based on the mathematical model and analysis of the system, a control strategy combining PIDD2 and feed-forward of velocity and acceleration is proposed. We compared the traditional PID control method with the above-mentioned control method for the electro-hydraulic servo system in the simulation environment AMESim. The experiment results show that the PIDD2 control with feed-forward of velocity and acceleration reduced the time delay and stability margin decline of the system response to some extent, and improved the dynamic performance of the system.

ABSTRACT. A Hollow fiber supported liquid membrane (HFSLM) unit has been widely studied to separate metal ions at very low concentration with high selectivity because it combines the process of extraction and stripping into a single stage. Therefore, the HFSLM unit has been suitably applied for the recovery of low concentrated palladium iron from wastewater released by flexible printed circuit board industry. This research presents the modeling of the palladium ions recovery via HFSLM and applicability of a model predictive controller (MPC) to achieve high concentration of palladium ions. It was found that the final predicted and actual concentrations of palladium ions are in good agreement; sum squares of errors in palladium ions is of 6.67. Percentages of palladium ions recovery are about 96.85. In addition, simulation results show that the MPC controller provides good control response and gives better control performance than a PID controller does in both normal and parameter mismatch cases.

ABSTRACT. This paper develops a distributed model predictive control strategy for the atmospheric and vacuum distillation tower, which constitutes a key distillation process involved in refining petroleum. When considering an MPC implementation, computational complexity can be reduced and flexibility improved if the system is first decomposed into multiple smaller dimensional subsystems. Optimally exploiting the functionality and structural characteristics of the modern computer networks available in the industry, a novel distributed predictive control algorithm is developed for the atmospheric and vacuum tower system, which is assumed to be part of a wider system comprised of a number of sub-systems connected in series. For each subsystem, given the availability of mutual communication channels between subsystems and by using an iterative calculation approach, it will be seen that Nash optimality can be achieved. A low-cost solution that is readily implementable online is seen to achieve the control objective. The effectiveness of the approach presented in the paper is validated by the results of nonlinear simulation experiments.

ABSTRACT. In this paper, a universal control scheme is investigated for a class of nonlinear systems with unknown parameters and nonlinearities like friction, dead-zone, etc. First of all, an adaptive finite-time update law is developed to estimate the unknown parameters by introducing the auxiliary filtered variables for the system states and regressor matrix. Then, a universal controller is proposed based on the finite-time parameter estimation to guarantee the globally asymptotic stability of the system. Finally, a motor servo control system with nonlinear friction is given as an example to show the effectiveness of the proposed method.

ABSTRACT. The milling process is the most used machining operation for finishing complex parts. The vibrations often impact the results, sometimes generating very important cost because of the lack of quality for high value parts. In fact, the phenomenon of chatter that is associated with unstable vibration in machining can cause accelerated wear and eventually rupture of both the cutting tool and the machine tool. This is the main factor limiting productivity, accuracy and surface finish of machined parts. Consequently, industrial requirements in terms both of productivity and accuracy need the development of innovative solutions. The Active Control is one of the most promising ways. This article will deal with the active control of a vibrating beam considered as an accurate enough model of a long slender rotary tool, following two control strategies: the H∞ compensator and the LQG one.

ABSTRACT. This paper addresses the energy management of hybrid vehicles using an economically-oriented model predictive control (EMPC) approach. A control modelling methodology is proposed based on considering the power flows that can be applied to the management of any hybrid vehicle configuration. Then, the proposed EMPC approach is formulated and the control objectives are formulated in terms of a multi-objective cost function. The proposed EMPC is illustrated in a hybrid vehicle that has a PEM fuel cell, a supercapacitor, a battery and a regenerative brake.

ABSTRACT. This paper shows the benefits of using pseudo-spectral (PS) methods for the optimal control of a three-body hinge-barge device. Two different control formulations are derived based on different representations of the dynamic model of the device: the differential and algebraic equations (DAE) formulation, and the ordinary differential equations (ODE) formulation. Wave-tank tests are carried out in order to validate the DAE and ODE models against experimental data. For control design, PS methods show significant improvements in terms of absorbed power with respect to an optimal damping strategy.

ABSTRACT. This paper presents an alternative control to maintain the air-fuel-ratio (AFR) of port-injected spark ignition (SI) engines at certain value, i.e. stoichiometric value, to improve the fuel economy. We first reformulate the AFR regulation problem as a tracking control for the injected fuel mass flow rate, which can simplify the control synthesis when the fuel film dynamics are taken into account. The unknown engine parameters and dynamics can be lumped as an unknown signal, and then compensated by incorporating the unknown input observer into the control design. Only the measurable air mass flow rate through throttle, manifold pressure and temperature, and the universal exhaust gas oxygen (UEGO) sensor are utilized. Simulations based on a mean-value engine model (MVEM) illustrate that the proposed control can achieve satisfactory transient and steady-state performance with strong robustness when the engine is operated in varying speed conditions.

ABSTRACT. In this study, U-model based control system design methodology is expanded into application of wind energy conversion systems, which includes formulisation of system into a standard control oriented model and generalisation of the power maximization controller design procedure for such nonlinear dynamic energy systems. In addition, the designed closed loop system having pre-designed linear system performances, such as stability, dynamic and steady-state characteristics plus reference input tracking, is easily understood for applications. To demonstrate the proposed procedure, a practical wind energy conversation system is selected with set of turbine parameters and external conditions. Numerical simulation studies provide bench tests and a user-friendly step by step procedure for the readers/users with interest in their ad hoc applications.

ABSTRACT. This paper studies the optimal control of a sea wave energy converter (WEC). A linear optimal controller is designed for the WEC to maximise the energy output. Rather than regulating states and control inputs as a conventional optimal controller normally does, the proposed optimal controller aims to maximise the energy output. The maximum output admissible set method is employed to estimate the feasibility of the proposed linear optimal controller when the input and state constraints need to be considered for safe operation of the WEC.

ABSTRACT. In this paper, a multiple model predictive control (MMPC) strategy is applied into an organic Rankine cycle (ORC) based waste heat energy conversion system (WHECS). The rotating speed of the pump and the shaft torque of the expander are manipulated simultaneously to provide the optimal (suboptimal) evaporating pressure and superheating temperature for nonlinear WHECSs under different operating conditions. Simulation results confirm the efficacy of the proposed control scheme.