ABSTRACT. Wave energy aspires to play a part in the mix of renewable energy solutions addressing the world’s increased energy demand, the limited fossil fuel supply and the concerns about greenhouse gas emissions and climate change. However, limited progress has been achieved in the commercial deployment of wave energy systems, mainly due to the relatively high cost of wave energy. Since the ocean is a challenging environment for the deployment of energy harvesting technology, with consequently high capital and maintenance costs, it is vital that the utility of deployed technology is maximised. Optimisation and control have a significant part to play in the drive to make wave energy economic and this keynote talk explores the challenges of applying control technology to wave energy systems. The talk will offer some background to wave energy systems and present aspects where control and optimisation can make potential improvements. In addition to the more obvious application of control to maximise energy capture, wave forecasting methods are also required to address non-causality in the control solution, while a strong interaction between the control algorithm and the optimal wave device energy geometry, and optimal array layout, is also shown.

ABSTRACT. In this paper, we present a model based design approach to the development of a semi-autonomous control system for an inspection drone. The system is tasked with maintaining a set distance from the target being inspected and a constant relative pose, allowing the operator to manoeuvre the drone around the target with ease. It is essential that the robustness of the autonomous behaviour be thoroughly verified prior to actual implementation, as this will involve the flight of a large multi-rotor drone in close proximity to a solid structure. By utilising the Robotic Operating System to communicate between the autonomous controller and the drone, the same Simulink model can be used for numerical coverage testing, high fidelity simulation, offboard execution and final executable deployment.

ABSTRACT. The Furuta pendulum (rotary pendulum) is an example of a complex nonlinear oscillator. In this paper, we demonstrate how MATLAB® and Simulink® can be used for rapid prototyping of control strategies. A mathematical model of the system is derived using Euler-Lagrange equations. The benefits and drawbacks of using physical modelling tools and traditional mathematical modelling techniques are discussed. A model of the pendulum with LEGO® MINDSTORMS hardware is used to validate the mathematically derived control strategies and the simulation models.

ABSTRACT. A Symbiotic relationship between robots is the- oretically developed. It is characterised by sharing sensory information and tightly coordinating operational logic by taking care of each other’s needs during missions. The system is characterised by an intertwined reasoning system while having separate conditioning and execution of plans to achieve subgoals to support each other. The results are illustrated on strong operational inter-dependence of a rover and a drone through shared logical inference. The drone uses the rover as a landing pad and the rover uses the drone to complements its sensor system by information gathering. There is a GitHub library provided in association with the demonstration for generic use of adding cameras and cooperation logic to a AR.Drone 2.0 and a KUKA youBot system. The benefits of symbiotic relationship are quantitatively evaluated on the demonstration example.

ABSTRACT. The root-locus method is often employed when controller is designed to find its gains. It is usually used to determine only one design parameter. However most controllers for industrial applications have more than one controller gain. For example PID controller has three controller gains, P, I, and D. Thus the root-locus technique cannot complete the design of controller with more than one gain and it is required an extension method of the conventional root-locus technique to design controller with more than one parameter. This paper proposes an extended root-locus method for controller with two parameters and Matlab is used as computation tool to show the effectiveness of our method by solving examples numerically. As a result we present how to find two controller gain parameters based on this extension of root-locus method. Note that the extended root-locus idea can be applied to controller design problems of multiple parameters.

ABSTRACT. The main impact of this work involves integration of electric vehicles (EVs) with power system for the purpose of voltage stability enhancement. It focuses on the development of new invention for integration of electric vehicles into distribution system, which is evolving to be micro grid due to the associated uncertainties of renewable integration. In this context, EV’s will be studied as load and most importantly as sources of power regulation to the distribution network. Electric vehicles will play a vital role in the operation of the distribution system. Also, Electric vehicles has been addressed to help avoiding voltage instability at the critical voltage operating conditions.

ABSTRACT. We present a feedback control scheme for a hybrid bidirectional interlinking converter of an AC/DC microgrid. The output voltage and current are measured which allow us to design a suitable control for the power flow. We proposes a robust droop control strategy to cater the uncertain voltage and frequency droop caused by load variations. The proposed droop controller takes into account the unmodeled load dynamics as well as the power switching transients between AC/DC microgrids. A conventional current controller followed by a sliding mode control are able to maintain the power stability in different operating conditions. Simulation results are presented which show the performance of the proposed control scheme.

ABSTRACT. The Model Predictive Controller is designed for a 5MW variable-speed pitch-regulated wind turbine for three operating points – below rated wind speed, just above rated wind speed, and above rated wind speed. At each operating point, the controllers are designed based on two different linear models of the same wind turbine to investigate the impact of using different control design models (i.e. the model used for designing a model-based controller) on the control performance.

ABSTRACT. This paper proposes a distributed load frequency control (LFC) scheme for a deregulated power system, that can give acceptable dynamic performance and good constraint handling capabilities. Firstly, a 4-area deregulated benchmark system, where each control area has a different rated capacity, is developed. The benchmark system includes measured (contracted) and unmeasured (uncontracted) disturbances. A non-cooperative distributed model predictive control (DMPC) algorithm is developed and tested on the benchmark system. The DMPC scheme is developed to operate using output feedback, where distributed observers using local measurements supply local controllers with state and unmeasured disturbance estimations. The DMPC here, unlike other noncooperative schemes, is simple and devoid of extensive offline parameter tuning. Some comparisons and discussions are provided between the DMPC and alternative model predictive control schemes.

ABSTRACT. One major challenge with integrating photovoltaic (PV) systems into the grid is that its power generation is intermittent and uncontrollable due to the variation in solar radiation. An accurate PV power forecasting is crucial to the safe operation of the grid connected PV power station. In this work, a combined model with three different PV forecasting models is proposed based on a rough set method. The combination weights for each individual model are determined by rough set method according to its significance degree of condition attribute. The three different forecasting models include a past-power persistence model, a support vector machine (SVM) model and a similar data prediction model. The case study results show that, in comparison with each single forecasting model, the proposed combined model can identify the amount of useful information in a more effective manner.

ABSTRACT. Demand side management (DSM) is a key concept for smart grid. In order to achieve DSM, network stress information needs to be provided to the users, which normally requires two-way communication between users and an upper level control centre. Implementing such communication in large scale power networks will be both costly and introduces problems with security and privacy of data. On-load tap changes (OLTC) are used to adjust the voltage at the users' side in response to network conditions. By detecting tap changes, an indication of the level of network stress can be provided to users without communicating with a control centre, which provides an economical and efficient measure for network stress. This paper presents a tap change detection algorithm based on Hilbert-transform phase-locked loops, Q-R decomposition windowed recursive least square algorithm and a hypothesis test, which can distinguish tap changes made by an OLTC transformer from the fluctuation in voltage caused by changes in loads. In this paper, the tap change detection algorithm is illustrated and the results for both simulated input and real-time data are presented and discussed.

ABSTRACT. With a large number of wind farms integrated into power systems, the load characteristics of the power systems will vary, resulting in the inapplicability of the traditional load model. Thus, it is of great importance to describe the load characteristics of power systems by taking the impact of the integration of the wind power into consideration. This paper first discusses the influence of wind power integration on the original load model. Wind speed, which has the most direct impact on the output of the wind turbine, is introduced as a new variable for load modelling. By combing the voltage variation of the system, a static equivalent load model is proposed in this paper, wherein, the structure of the proposed load model is also discussed. Measurement-based load Modelling with large amount of measurements requires to process a large number of measured data. This paper utilizes the density based clustering algorithm (DBSCAN) to mine the core data, and reconstructs the surface of the load characteristic based on the mined core data. A static equivalent load model with wind power integration is then established. Compared with the model that is modeled directly without data processing, the proposed load model is more accurate.

ABSTRACT. Abstract—Rough set is an effective tool to analyse and process the imprecise, inconsistent and incomplete information. Attribute reduction is the core content of rough set theory. Because of the objective of rough set data processing, it is sensitive to the noise data. Especially for large data sets processing, the decision rules obtained based on traditional rough set algorithms exhibit incompatibility and the decision information became variation. The paper proposed an improved heuristic attribute reduction algorithm and discussed the structure of heuristic information method. Using the properties of information entropy as the measure, the attribute significance is determined. By extending the concept of maximum distribution attribute reduction, the paper proposed the optimal maximum distribution attribute reduction algorithm based on attribute significance. Finally, based on the flame stability judgement of digital image, the improved algorithm is verified to be effective.

ABSTRACT. Multipath is the dominant error source for high-accuracy positioning systems. It is significant for eliminating the multipath error and improving the positioning accuracy to estimate multipath parameters. The existing multipath estimation algorithms are usually designed for Gaussian noise, and their performances degrade dramatically in non-Gaussian noise. To solve the problem, a multipath estimation algorithm based on kernel minimum error entropy filter (KMEEF) is proposed. In KMEEF, the minimum error entropy (MEE) criterion instead of mean square error (MSE) criterion is applied, which is not limited by the Gaussian and linearity assumption. According to the stochastic information gradient (SIG) method, an optimal filer gain matrix is obtained by minimizing the error entropy. Furthermore, the learning rate is suggested by a convergence analysis. The simulation results show the effectivity of the proposed algorithm for multipath estimation.

ABSTRACT. An SIR epidemic model is mathematically analyzed while obtaining its equilibrium points and analyzing their stability. A feedback-infection dependent vaccination control law is proposed and studied. Finally, the mathematical model is applied to study the evolution of the common influenza in the Autonomous Community of the Basque Country (Spain). The infection has been parametrized using experimental data published by the medical services. With the obtained parameters different simulations are made to visualize the effects of a proportional vaccination in the number of infected individuals. It is seen that the vaccination decreases the overshoots compared to the vaccination-free case.

ABSTRACT. Predictive control strategies for building heating and cooling systems have been proposed as an energy efficient alternative to traditional strategies. The performance of such strategies is highly dependent on the underlying system models used. In an effective strategy, these models used are required to be accurate enough for informative predictions to be made yet simple enough to be used within a numerical optimization problem. Identification of such models from measured data may not be trivial in the presence of a large amount of unmeasured disturbance.

In this paper, methods for deriving low-order zone models in the presence of unknown disturbances are considered. A high-order RC-network representing the complexity of a building is used to generate data for the identification process. An estimate of the disturbance affecting each zone of the network is first developed using Kalman filtering. Disturbances common to several zones are isolated by a spatial filtering process using principal component analysis. The new disturbance estimates are then included in the model identification formulation. The models and disturbance estimates are refined through several iterations of the process. Significantly improved prediction accuracy is shown to result when the disturbance estimates are incorporated.

ABSTRACT. In-situ characterisation of thermocouple sensors is a challenging problem. Recently the authors presented a blind characterisation technique based on the cross-relation method of blind identification. The method allows in-situ identification of two thermocouple probes, each with a different dynamic response, using only sampled sensor measurement data. While the technique offers certain advantages over alternative methods, including low estimation variance and the ability to compensate for noise induced bias, the robustness of the method is limited by the multimodal nature of the cost function. In this paper, a normalisation term is proposed which improves the convexity of the cost function. Further, a normalisation and bias compensation hybrid approach is presented that exploits the advantages of both normalisation and bias compensation. It is found that the optimum of the hybrid cost function is less biased and more stable than when only normalisation is applied. All results were verified by simulation.

ABSTRACT. This paper considers the identification problems of Hammerstein Box-Jenkins systems based on the hierarchical identification and maximum likelihood principle. After decomposing the Hammersetin Box-Jenkins systems into two subsystems, a maximum likelihood least squares based iterative algorithm is derived to interactively estimate the parameters of the two subsystems. The simulation results show that the proposed algorithm can effectively estimate the parameters of Hammerstein Box-Jenkins systems.

ABSTRACT. Bond graph (BG) models are widely used to display various fields of a physical system and their interconnection. In this paper, a BG pseudo-junction structure for non-linear and non-conservative systems is proposed. This BG pseudo-junction structure has an inner structure that satisfies energy conservation properties and a multiport-coupled dissipative field that determines the physical realisability of the system. Properties of the dissipative field like passivity are highlighted by the proposed BG pseudo-junction structure. The results are illustrated through examples.

ABSTRACT. To enhance the performance of the tracking property , this paper presents a novel control algorithm for a class of linear dynamic stochastic systems with unmeasurable states, where the enhanced performance loop is established based on Kalman filter. Without changing the existing closed loop of PI controller, the compensative controller is designed to minimize the variances of tracking errors using the estimated states and the propagation of state variances. Moreover, the stability of the closed-loop systems has been analyzed in the mean-square sense. A simulated example is included to show the effectiveness of the proposed control algorithm, where encouraging results have been obtained.

ABSTRACT. The trajectory tracking control problem for a class of n-degree-of-freedom (n-DOF) rigid robot manipulators is studied in this paper. A novel adaptive radial basis function neural network (RBFNN) control is proposed in discrete time for multiple-input multiple-output (MIMO) robot manipulators with nonlinearity and time-varying uncertainty. The high order discrete-time robot model is transformed to facilitate digital implementation of controller, and the output-feedback form is derived to avoid potential noncausal problem in discrete time. Furthermore, the desired controller based on RBFNN is designed to compensate for effect of uncertainties, and the RBFNN is trained using tracking error, such that the stability of closed-loop robot system has been well guaranteed, the high-quality control performance has been well satisfied. The RBFNN weight adaptive law is designed and the semi-global uniformly ultimate boundedness (SGUUB) is achieved by Lyapunov based on control synthesis. Comparative simulation studies show the proposed control scheme results in supreme performance than conventional control methods.

ABSTRACT. In this paper, a new adaptive control framework for linear systems in which the matched uncertainty can be linearly parameterized is introduced to guarantee the global exponential stability of reference tracking error and parameter convergence error without requiring restrictive persistent excitation condition. The framework uses time histories of control input and system signals to construct least-squares problem based on recorded data. Then, unique solution to least-squares problem is computed, and assigned as pre-selected value in well-known σ-modification term. Such indirect use of recorded data matrices results in globally exponential convergence of tracking error and parameter convergence error provided that the recorded matrices satisfies the simple rank condition. The proofs are given by Lyapunov stability theorem, and the results are illustrated with simulations.

ABSTRACT. In order to acquire better performance and minimize the trace error, the friction mechanism of cam-rotor vane motor(CRVM) was analyzed. As a novel friction description, LuGre friction model and its parameter had a few limitations because this model is only the friction description of a fixed position. Based on lots of friction experience and the characteristic of CRVM, a changeable maximum dynamic and static friction model which is based on LuGre model, was proposed and identified. Using structure invariance principle, a feedforward compensator was designed and verified. Experience results show that the control strategy designed by the new friction model not only works well for rejecting torque trace error, but improve the tracking performance of the CRVM.

ABSTRACT. This paper presents a general framework based on the RST structure for performing fair comparisons between deterministic and stochastic digital linear controllers. Due to the chosen RST formulation, it can be shown that any linear Single-Input Single-Output controller may be designed as a Generalized Minimum Variance Controller, i.e. as a stochastic controller. This in particular reduces the variance of the control signal and in turn leads to a better output characteristic. The method is applied to the controllers of an exemplary Single-Machine Infinite Bus system, namely the AVR and governor, where it is shown that the voltage and power regulation for the non-stochastic and the GMVC are similar whereas the control signal given by the latter is much smother than for the former, even using the same set of gains for both.

ABSTRACT. The paper considers a distributed Linear Quadratic Regulator (LQR) design framework for a network of identical dynamically decoupled multi-agent systems. It is known that in this case a stabilizing distributed controller for the network can be obtained by solving a centralized LQR problem whose size depends on the maximum vertex degree of the graph. A systematic method is presented for computing the performance loss of various distributed control configurations relative to the performance of the centralized controller. A procedure is developed for analyzing the performance loss for general distributed control configurations and state-space directions. It is also shown that by removing a single link we can always define a control configuration for which there is no performance loss, provided the initial state of the aggregate system lies in a particular direction of state-space which is identified. The results are illustrated by an exhaustive analysis of the network consisting of six identical agents.

ABSTRACT. With rapid urbanization all over the world, the traffic congestion problem is becoming increasing severe. In particular, many metropolitan areas suffer from the problem of network gridlock in peak hours, which is mainly attributed to the limited street space as well as inefficient intersection signal control to cope the excessive demand. In light of this, in this paper we propose a model to describe the risk of gridlock on a network and develop an intersection control strategy to mitigate such risk. The intersection control is formulated within a decentralized agent-based framework, casting it as a dynamic portfolio management problem. Some properties of the proposed control strategy are discussed, along with numerical experiments demonstrating the performance on a grid network.

ABSTRACT. A Hybrid Electric Vehicle longitudinal dynamics model for the control of energy management is developed. The model is implemented using Simulink and consists of a transitional vehicle speed input parameterized by, for example, the New European Driving Cycle. It is a backward looking model in that engine and motor on/off states are determined by the controller, dependent on wheel torque requirements and output targets. The objective of the simulation is to calculate tractive effort and resistance forces to determine longitudinal net vehicle force at the road. This article addresses model development and initial investigations of its dynamic behaviour in order to establish appropriate energy management strategies for the Hybrid Electric system. In particular, All Wheel Drive, Front Wheel Drive and Rear Wheel Drive drivetrain architectures are tested to determine minimum fuel usage.

ABSTRACT. A sliding mode controller is proposed for the robust control of both the position and attitude of a quadrotor. The design is tested in simulation. The performance is compared with a proportional plus derivative controller. The robustness of the design is also tested for parametric uncertainty. The effect of disturbances is also investigated. It is found that the sliding mode controller provides good performance, robustness and disturbance rejection.

ABSTRACT. The automotive industry is continuously developing technologies and strategies for increasing the efficiency in fuel consumption and reducing the emission of pollutants. The variable valve timing (VVT) system provides such a solution for internal combustion engines. Researches in this area are mainly devoted both to improved layouts and to new operation control techniques. To this aim, modeling, identification and validation are important preliminary steps. This paper focuses on the modelling of an electro-actuated solenoid, fast-acting valve, which is a key component of the VVT system. The valve is used to regulate the pressure that commands the lift of engine intake valves. An identification method is applied to represent the valve dynamics in a complete VVT model. The results are compared to the output from an analytical model and to real data available in certain operating conditions.

ABSTRACT. Chebfun is an extension to MATLAB that enables problems with continuous functions and operators to be solved with simple, concise coding and near machine accuracy. Thus it has potential to be useful in solving optimal control problems. Its capabilities for this are explored in this paper and a direct method for solving optimal control problems using chebfuns is described. The approaches are demonstrated with several test cases from flight guidance and aeronautics, namely the minimum time-to-climb problem, the intercept problem and the trajectory planning problem.

ABSTRACT. Model-in-the-Loop (MiL) testing is a method in which the test object is split into a physical part and a simulated part, and these are connected with interfaces to form a combined physical-numerical system. The challenge of generating a MiL test is that, firstly, because of the limited dynamic response of the actuators, the test results may be inaccurate, and secondly, because of the high frequency noise introduced by the sensors to the closed-loop system, it may be difficult to design a compensator for the actuator response, while stabilizing the closed-loop system at the same time. In this paper, a MiL system is designed using a small hydraulic robot arm. The problems with the MiL test without any compensator is shown with experimental results. The effectiveness of a 1st order phase lead compensator and an inverse model compensator are validated in the experiment. For systems which can be approximated by linear time-invariant models, it is proposed that compensator design is a linear optimization problem balancing emulation error with noise amplification. Thus, a new method of designing the compensator for MiL testing based on H-infinity optimization is presented.

ABSTRACT. A new distributed MPC algorithm for the regulation of dynamically coupled subsystems is presented in this paper. At each time instant, the control action is computed via two robust controllers working in a nested fashion. The inner controller provides local reference trajectories computed on a fully decentralized framework. The outer controller uses this information to take into account the effects of the dynamic coupling and implement a distributed control action. The tube-based approach to robustness is employed. A supplementary constraint is included in the outer optimization problem to provide recursive feasibility of the overall controller.

ABSTRACT. This paper considers the problem of robust estimation and constrained model predictive control (MPC). The paper deals with a discrete linear time-invariant system affected by additive bounded disturbances, whose states are measurable, but not directly accessible. In order to improve the control performance, a state estimator is desirable. The design problem of an observer based on zonotopes to estimate the system states of the uncertain system is addressed. Then, the min-max MPC optimisation problem formulation based on the designed robust observer as a quadratic program (QP) is described. An efficient implementation of the proposed robust observer-based control algorithm that can be solved by a standard QP is validated by simulation through the regulator problem of a cart pendulum system.

ABSTRACT. In this paper, the problem of control algorithm design for a class of nonlinear two-input and two-output (TITO) networked control systems (NCSs) with non-Gaussian random time delays is investigated, where a general non-linear auto-regressive moving average with exogenous model is used to describe the plant. Due to the non-Gaussian random time delays involved in the systems, it is insufficient to obtain a satisfactory optimal control algorithm by only controlling the expected value of the tracking errors. The Renyi entropies of the tracking errors and control inputs are adopted to characterize the randomness of the closed-loop system. The formulations of the probability density functions (PDFs) of the tracking errors and control inputs are deduced. By minimizing the new performance index, a recursive optimal control algorithm is obtained. Furthermore, the local stability condition of the closed-loop systems is established. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.

ABSTRACT. This paper proposes a regressor-free adaptive feedback-linearization control technique that does not require a model approximation or a regressor matrix. Adaptation in the proposed feedback process is acquired through an update law involving adjustment of less control parameters as compared to existing controllers. Under the given constraints, the closed-loop asymptotic stability of the proposed control law is verified using Lyapunov techniques. The proposed controller is compared with existing adaptive controllers on a two degree-of-freedom robot manipulator. Based on the new adaptive technique, the model parameters of the robotic arm are identified using adequate excitation trajectories. The proposed adaptive technique was validated through simulations and experiments.

ABSTRACT. In this paper and its companion paper [1], a stable leader-following formation control for multi-agent systems with obstacle collision avoidance with orientation control is considered. We propose a cascaded distributed control law that uses information about the angle and distance between agents to achieve a cycle-free persistent formation. First, we provide a control scheme with collision avoidance for groups of agents with a single integrator model. We show the asymptotic stability of the formation under a gradient control law. Second, the proposed control law includes a distance-angle-based controller for shape stabilization and displacement-based formation control of certain leader agents to control the direction of the whole formation. Simulation results are presented to illustrate the proposed approach.

ABSTRACT. In this paper and its companion paper [1], multi-agent formation control with obstacle collision avoidance for angle-distance-based formations with orientation control is considered. We propose an approach that uses information about the angle and distance between agents to achieve a leader-follower formation. First, we provide cascaded control laws with collision avoidance for groups of agents with unicycle models and prove the asymptotic stability of the formation. The non-holonomic unicycle model includes the vehicle dynamics. Second, the proposed control law is combined with displacement-based formation control. The shape of the formation is controlled by distance-angle-based formation control and the orientation of a group of agents converges to the desired orientation by a displacement-based control law. Simulation results are presented to illustrate the proposed controllers.

ABSTRACT. The paper presents a cooperative scheme for controlling arbitrary formations of low speed experimental UAVs based on a distributed LQR design methodology. Each UAV acts as an independent agent in the formation and its dynamics are described by a 6-DOF (degrees of freedom) nonlinear model. This is linearized for control design purposes around an operating point corresponding to straight flight conditions and simulated only for longitudinal motion. It is shown that the proposed controller stabilizes the overall formation and can control effectively the nonlinear multi-agent system. Also, it is shown via numerous simulations that the system provides reference tracking and that is robust to environmental disturbances such as nonuniform wind gusts acting on a formation of four UAVs and to the loss of communication between two neighbouring UAVs.

ABSTRACT. In this paper, a experimental result of the trajectory tracking control of 2-link robot manipulator is presented. A nonlinear system has a linear time-varying approximate model around some desired trajectory. This implies that the trajectory tracking control problem is to design a linear time varying controller to stabilize this approximate model. This is very basic and classical idea, but, the design of linear time varying controller is not necessarily simple. The authors proposed a simple design procedure for the disdrete linear time varying pole placemet controller and the discrete linear time varying observer. In this paper, the discrete time varying observer based pole placementtechnique is applied to an actual 2-link robot manipulator, and, show the experimental results.

ABSTRACT. In the present study, the relationship between the Harmonic Balance Method (HBM) and the Nonlinear Output Frequency Response Functions (NOFRFs) approach when analysing the output response of nonlinear systems is investigated, showing that the output response described by the NOFRFs is the minimum solution of that from the HBM. After that, the convergent range of the NOFRFs is determined based on the proposed relationship, indicating that, outside this range, the validity of the NOFRFs method should be taken into account when used to represent the output response of nonlinear systems. Moreover, the effect of system damping on the convergence of the NOFRF approach is studied. The results imply that a nonlinear system with appropriate linear or nonlinear damping may have no convergence issue when being analysed using a NOFRFs-based method.

ABSTRACT. This paper proposes conditions for nonlinear systems where both the vector field and its Jacobian are rational with respect to the states and the nonlinearity. Conditions for studying stability and computing L2 gain bounds for such systems are detailed in terms of convex optimisation problem which use sum of squares programming. Specific conditions are given for two common nonlinear systems, when the nonlinearity is a logarithm and when it is an arctangent. The proposed method is compared to the Popov and circle methods from absolute stability in a numerical example and it is shown that the proposed method outperforms both of these classical results.

ABSTRACT. An optimal adaptive compensation control scheme is proposed for a class of multi-input multi-output (MIMO) affine nonlinear systems with actuator failures. Considering stuck actuators and partial effectiveness failures, an adaptive dynamic programming method is adopted by using neural network to approximate the cost function. It adjust the weights of the neural network by using an online adaptive algorithm. An adaptive parameter adjustment law is designed to estimate the actuator failure coefficients. The proposed optimal adaptive compensation law can guarantee that the closed-loop system with actuator failures is stable and that the given reference signals are effectively tracked. Simulation results demonstrate the effectiveness of the proposed method.

ABSTRACT. The output feedback tracking control problem is studied for a class of uncertain nonlinear systems with actuator failures. An adaptive observer is designed to reconstruct immeasureable state information of the system, and an observer-based adaptive fault-tolerant control (FTC) strategy is developed recursively by backstepping methods, neural networks (NNs), FTC theory and the dynamic surface control (DSC) technique. The proposed strategy is only dependent on output information, and there is no requirement for accurate parameters of the system. In theory, the stability of the closed-loop system is proven that all signals are uniformly ultimately bounded and the control scheme can force the tracking error converge to a small neighborhood of the origin.