ABSTRACT. In this talk, target/source localization by a network of binary sensors under various imperfections are studied. Detailed analysis and mathematical modeling of imperfect binary sensors are presented which include sensor failures of two types, uncertainty and heterogeneity in trigger thresholds, presence of noise and non-radial symmetry of sensing ranges. In particular, effects of outliers and ways to reduce/eliminate the effects are discussed. Numerical algorithms with convergence and asymptotic normality results are derived. Further a number of interesting insights are provided.

ABSTRACT. A method based on two passes of 3D region growing and morphological reconstruction for segmenting pulmonary airway tree from computed tomography (CT) chest scans is presented to solve the problem of leakage and under-segmentation caused by the partial volume effect and motion artifact. Firstly, the first pass of 3D region growing with optimal threshold range is used to extract the rough airway. Then, three location maps of possible distal bronchi are located by using the grayscale morphological reconstruction on axial, coronal and sagittal slices respectively. Finally, on basis of rough airway extracted in first pass of 3D region growing, the second pass of 3D region growing constrained by the three location maps is implemented to obtain the completed airway. 25 clinical CT scans with thickness between 0.75 mm and 2 mm were used to test the proposed method by recording the number of tracheal branches of each order, the total number of tracheal branches and the average number of branches. Up to 12 generations of bronchi and average 156 branches were detected in the experiment which proves that our adaptive and automated method can segment the pulmonary airway with a better performance.

ABSTRACT. In order to enhance the problem solving skills of students majoring in wind energy and power engineering, a novel TRIZ based strategy is developed for teaching the wind turbine control module. By utilizing the TRIZ theory, the key control parameters for wind turbines are identified and comprehensively analyzed, and the contradictions among various wind turbine control objectives are extracted, based on which solving methods are developed. This strategy can help to strengthen the appreciation and application of wind turbine control knowledge for the students majoring in wind energy and power engineering.

ABSTRACT. This article describes experiments that explore the possibility of using an optical tracking device input to remotely control dual arm robots. We propose using the Leap Motion controller as an alternative to using joysticks, this allows for more intuitive 6-DOF control where only one hand is needed to control each robot arm. We affixed two end effectors to a Baxter research robot, a standard electric gripper and an AR10 robotic hand. The standard electric gripper was controlled via pinch gestures and all five fingers and thumb were controlled on the AR10 robotic hand using finger movement from the Leap. Some simple tests were performed and the results indicate that in lifting standard objects the standard electric gripper had a higher success rate. The main problem with the hand appeared to be due to the absence of any touch or force feedback to the operator, as the user became more comfortable with the system the better became their performance. This leads us to believe that the use of a robotic hand could be improved with a training program.

ABSTRACT. The increasing penetration of wind generation impacts the reliability and stability of power systems. This paper investigates and analyses the effect of PID controller parameters on the inertial response of a doubly fed induction generator (DFIG) wind turbine to support the frequency control of a power system in the event of sudden power changes. The goal of this review is to extensively determine the effect of PID parameters to compare and set a benchmark so that an adaptive control strategy can be developed for frequency regulation. A conventional inertial controller algorithm using the rate of change of frequency (RoCoF) and frequency deviation loops was investigated whilst the contribution of the DFIG to system inertial response and frequency control are examined. The paper considers the influence of supplementary inertial control loop parameters on the inertial response and power system frequency. The results indicate that the DFIG inertial controller scheme is able to provide appropriate frequency support.

ABSTRACT. This paper presents a novel technique for obstacle avoidance and target location using autonomous underwater vehicles. The proposed method uses the concept of bidirectional artificial potential fields in order to cooperatively avoid obstacles whilst travelling to a desired location. A fluid-like formation is presented whereby the vehicles are assigned a separation distance which they adhere to when not in the process of obstacle avoidance. This distance is free of angular constraints which allows a more flexible formation than traditional approaches. Although cooperative in nature, the proposed strategy allows all the vehicles to be independently guided by the overall potential field. This technique is useful even when other vehicles fail. Both clockwise and anticlockwise fields are simultaneously created around obstacles, and used by the vehicles to ensure cooperative avoidance around the obstacles. The proposed technique could be used for a number of applications such as mapping/exploration/surface inspection to name a few. Simulation results have been conducted for various scenarios and show the method to be effective.

ABSTRACT. This paper presents a relatively inexpensive, laboratory-based hardware system for the purposes of illustrating the concept of control in an undergraduate engineering laboratory. The proposed system links a simple fan and balance beam hardware to the Matlab and Simulink software on a standard PC using the Arduino Uno microcontroller board as the interface. This results in an affordable experimental setup that is easily implemented and ideally suited to standard engineering hardware laboratories. Current solutions are relatively expensive and are unsuitable for the typical space available at individual student stations in a standard hardware laboratory. This paper outlines, in detail, the combined software and hardware solution that allows the balance beam to be controlled through Matlab and Simulink. An example laboratory that uses the balance beam system to demonstrate PID control, along with student feedback on the use of this system, is also presented within.

ABSTRACT. An enormous increase in the number of internet users which will tend to rise further, cluster-based web servers (CBWS) are experiencing a dramatic increase in web traffic. Round-robin load-balancing algorithm (RLBA), is one of the most widely used for distributing loads among the web servers due to its simplicity. However, in the case of non-uniform web traffic, RLBA load distribution is inefficient. In this paper, we have developed novel approaches to optimize the RLBA with the necessary procedures. We propose Adaptive RLBA (ARLBA) and Predictive RLBA (PRLBA). We validate the effectiveness of algorithms through simulation results. Server load correlation and load variance are used as the performance measuring parameters. ARLBA and PRLBA outperform RLBA in all the cases. ARLBA performs slightly better than MRR and vice versa in case of PRLBA.

ABSTRACT. The simulation and experimental platform is very important to the research of NCS(Networked Control System). A novel Simulink based NCS simulation and experimental platform is presented in this paper. Taking advantage of cross-compilation on cloud side techniques, the storage space of user's computer is saved meanwhile the details of developed S-function is masked to users and the developer's benefits are protected. To support the NCS experiments, multiple cloud server is used to transmit the communication package to obtain the desired time delay and packet loss rate by the real network environment. A multiple process based time synchronization method is presented to synchronize the clocks in the NCS experiments. The proposed time synchronization is accurate and stable to implement the desired simulations and experiments. As an application example, a wheeled mobile robot remote path tracking control simulation and experiment are achieved on the proposed platform.

ABSTRACT. This paper proposes an Improved Online Self-organizing Dynamic Fuzzy Neural Network (IOSDFNN) for nonlinear dynamic system identification. The IOSDFNN is a five-layered network, which features coalescence between Takagi-Sugeno-kang (TSK) fuzzy architecture and dissymmetrical Gaussian functions (DGFs) as membership functions. The partitioning made by DGF with the flexibility and dissymmetry of left and right widths in the input space is more flexible and therefore results in a parsimonious FNN with high performance under the online learning. We apply two criteria for rule generation: system error and ε-completeness reflecting both a performance and sample coverage of an existing rule base. During the parameters estimation phase, we adjust the Gaussian centers according to adjustment of widths. Parameters in premise and consequents are adjusted online based on the ε-completeness of fuzzy rules and Kalman Filter (KF) approach, respectively. The error reduction ratio (ERR) method is used as pruning strategies. Simulation studies demonstrate the presented algorithm is superior in terms of performance of approximation and generalization.

ABSTRACT. In many applications, the use of slender and light flexible structures has increased due to the requirement of more efficient structures. One objective of this work is to generate an approximated model of a two flexible-link robot for control purposes, which includes rotational actuators, piezoelectric actuators, and different kinds of sensors (acceleration and deformation). The model is obtained under a classical mechanics approach: Lagrange Euler energy balance. The model of the actuators is also included. Some parts of the resulting model involving integral terms are calculated using symbolic programming software, whereas other parts are implemented and calculated dynamically during simulation. The model of the two flexible-link robot is simplified for the controller calculation. The resulting model is simulated in Matlab/Simulink. The second objective is to develop a MIMO joint tracking and active vibration sliding mode controller. The values required for the implementation of the controller are obtained from the formulated models. Experimental results show the effectiveness of the proposed controllers.

ABSTRACT. This paper presents an energy management system to minimize the total combined operational and emission costs of a hybrid microgrid by optimal scheduling of the operating time of distributed generators and power exchange between the battery storage system and the utility grid. The impacts of unit commitment on the optimal operation of the hybrid microgrid are evaluated by formulating and solving the optimization problem using mixed integer quadratic programming technique. Both grid-connected and isolated modes on the hybrid microgrid, which comprises of a combination of distributed generators, renewable energy resources, storage batteries, and a variety of loads, are investigated. Realistic constraints and battery degra-dation are also factored into the optimization. The results show comprehensively that incorporation of the unit commitment into the optimization problem typically reduces the total operational and emission costs for both isolated and grid-connected modes. In addition, it helps to increase the penetration of renewable energy resources with microgrid.

ABSTRACT. The task of epilepsy diagnosing in medicine by classification of electroencephao-graph (EEG) signals is considered. Since an EEG signal has a large number of dimensions as an input sample vector, many previous classification methods have been proposed as hybrid frameworks, which are structurally complex and computationally expensive. In this paper, convolutional neural network (CNN) is used to realize feature extraction and classification simultaneously. The scheme of CNN is adopted to overcome the curse of dimensionality. Meanwhile, the accelerated proximal gradient method is used to increase the training ratio. Experimental results show that the proposed method achieves ideal accuracy of epilepsy diagnosis and converges faster than CNNs based on traditional gradient back propagation.

ABSTRACT. Decision making for lane change manoeuvre is of practical importance to guarantee a smooth, efficient and safe operation for autonomous driving. It is, however, challenging. On one hand, the behaviours of ego vehicle and adjacent vehicles are dependent and interactive. On the other hand, the decision should strictly guarantee safety during the whole process of lane change with uncertain and incomplete information in a dynamic and cluttered environment. To this end, the concept of Receding Horizon Control (RHC) is integrated into game theory in conjunction with reachability analysis tool, resulting in RHC based game theory. Specifically, the decision of each game relies on not only uncertain information at current step but also the future information calculated by reachability analysis. The decision is repeatedly made with the advent of new information using the concept of RHC. As a result, safety can be guaranteed during the whole process of lane change in a dynamic environment. Case study is conducted to demonstrate the advantages of the proposed approach. It is shown that the proposed RHC based game theory approach incorporating uncertain information can provide a safer and real-time decision.

ABSTRACT. a flight controller system is essential for any unmanned aerial vehicle (UAV) to efficiently execute a flight mission with or without disturbance. Use of advance modern control techniques guarantees efficient flight performance for most UAV platforms than would classical control approaches. Modern flight control techniques such as dynamic inversion, model predictive control, H-infinity, to name a few have been used to design controllers for UAV systems. This paper will focus on the design and application of an optimal linear controller using a multi-objective particle swarm optimization algorithm for tuning of the weighting matrices. The controller will be designed to optimally track UAV lateral-directional trajectories. The PSO algorithm optimizes the selection of tuning matrices necessary to obtain state-feedback control gains for tracking of any input sequence.

ABSTRACT. Recently, minimum entropy control methods has been successfully used as an information theoretic criterion for non-Gaussian stochastic systems. In this paper, a new single neuron control strategy for nonlinear and non-Gaussian unknown stochastic systems has been proposed in the framework of information theory. Firstly, instead of minimum error entropy criterion, the survival information potential (SIP) criterion, where the randomness of control input is also considered, is formulated. Then, based on the single neuron controller structure, the optimal controller parameters are obtained so that the randomness and magnitude of the closed-loop tracking error is made as small as possible. Finally, this control approach is applied to control the temperature of Organic Rankine Cycle (ORC) processes and encouraging results have been obtained.

ABSTRACT. This paper proposes strategies for operating cost optimisation of a multi-energy source building. The optimisation is based on a day-ahead forecast of building energy usage. The building in question is powered by multiple energy sources including a wind turbine, a photovoltaic system, a lead-acid battery system, and the national power grid. The optimisation method presented in this paper is a genetic algorithm. This algorithm uses the energy demand of the building, energy supplied from the wind turbine and photovoltaic system, and real-time electricity pricing to optimise the operating timetables for the batteries. Simulation results demonstrated that daily operating costs can be reduced by up to 32 % using the genetic algorithm with a fixed charge/discharge rate, and by as much as 56 % when variable charge/discharge rates are employed, in comparison to a standard decision-based strategy.

ABSTRACT. Yangtze River is the world's busiest inland waterway. Ships need to be guided when passing through controlled waterways based on their trajectory predictions. Inaccurate predicted trajectories lead to non-optimal traffic signalling which may cause significant traffic jam. For the existing intelligent traffic signalling systems (ITSSs), ships are supposed to travel exactly along the mid-line of the Yangtze River, which has caused many problems and issues. Over the last few years, traffic data have been growing exponentially, and we have gone into big data ear. This trend allows us to predict the ship travel trajectories using big historical data. In this paper, the historical trajectories are grouped by the popular K-Means clustering algorithm firstly, and then Artificial Neural Network (ANN) models are built using the above clustering results and other known factors (i.e. ship speed, loading capacity, self-weight, maximum power and water level) to predict the ships' trajectories. The experimental results show that the developed model is in good agreement with the actual data with more than 70\% accuracy. It will also help to generate the optimal traffic commands for Yangtze River traffic control.

ABSTRACT. This paper addresses the problem of reconstructing the height of the sea-surface proximal to marine vessels, based upon a finite set of point-wise in space height samples from onboard light detection and ranging (LiDAR) sensors. This is a necessary precursor to developing the autopilot systems for next generation unmanned surface vehicles (USVs) that can efficiently navigate through rough seas, based upon limited sensory information of the surrounding sea-surface. The technical challenges are twofold. Firstly, the sea-surface dynamics are highly complex, posing a significant challenge to the use of model-based estimation techniques. Secondly, the measurements of the sea-surface are spatially irregular, sparse, and time-varying owing to the effects of dynamic wave-shadowing. As a significant first step, we show how the challenge of sea-surface reconstruction can be posed as a matrix completion problem whose solution is model-free and is merely reliant on a low-rank property stemming from the bandwidth-limited nature of ocean wave spectra. Validation tests are conducted on ocean surfaces generated from Elfouhaily spectra, with synthetic sensor data generated from geometric intersection of LiDAR beams with each surface. The results demonstrate remarkably good recovery of the large matrices that store sea-surface height data, using fewer than 3% of their sampled entries. In addition, results are presented that demonstrate the robustness of the matrix completion technique to random sample loss.

ABSTRACT. The memristor was originally defined as one of the fundamental electrical elements which provided the vacant connection between charge and flux. So far, most of the research has concentrated on the unique features of the individual devices. And the overall behavior of the multiple memristive systems has not been fully studied. Especially, the lack of corresponding fault diagnosis method for complex memristor circuits makes all the existing applications based on the multiple memristor circuits unstable and shaky. In this paper, the composite properties of multiple memristor circuits are further investigated. Then a neotype parametric fault diagnosis approach for memristive networks is presented using the doublet generator, sensitivity method and fuzzy math method, which offers huge benefits in terms of accuracy and time consumption.

ABSTRACT. In order to simulate a micro-gravity environment for a three-axis air-bearing spacecraft simulator, an automatic mass balancing subsystem is developed. A non-orthogonal slider distribution scheme is proposed to avoid the intrinsic center-of-gravity shift involved in the mass balancing process. The simulator kinematics and dynamics are analyzed, and the relationship between the attitude change and the disturbance torque caused by the center-of-gravity shift is deduced. The recursive least square method is adopted for the centroid deviation estimation. The centroid deviation is then decomposed to the displacements of the sliders. As a result, the simulator could be auto-balanced by controlling the sliders’ motions. The proposed mass-balancing algorithm is implemented, and the effectiveness is validated by experiments, which could achieve the final centroid deviation within the range of 5 μm.

ABSTRACT. Measurements based on inertial sensors are increasingly applied in daily life. The biggest problem plaguing such sensors is its large errors. In typical environments of portable applications, most calibrating equipments and methodologies designed for general inertial navigation systems are no longer suitable. Therefore, new calibration and compensation methods tailored to portable applications need to be explored. This paper makes a survey of the recent studies in this field, describing typical solutions to portable calibration and compensation of inertial measurement errors, with comparisons of their advantages and limitations when possible.

ABSTRACT. Permanent magnet linear motors (PMLMs) are widely used in many high precision servo motion platforms, and lithography is a typical application. The reticle stage macro movement subsystem of a lithography machine is a 3-DOF coupling system in X-Y direction which requires carefully modelling and control to achieve high precision. This paper proposes a new modeling method for the 3-DOF PMLM driven coupling system. The modeling method considers the rotation angle of linear motors in X direction to solve the coupling problem and to achieve ultraprecision tracking performance with millimeter accuracy. Furthermore, a 3-DOF sliding mode controller is designed to control the proposed model. Finally, the effectiveness of this modeling and control strategy is demonstrated via S-curve tracking simulation, and both the performance of the master-slave with feedforward control structure and the main-main with feedforward control structure are compared. The simulation results show that the tracking errors of PMLMs both in X and Y directions are less than 3 μm, and the rotation angle is less than 25 μrad.

ABSTRACT. This paper presents a distributed optimal control method for stable and economical operation of active distribution network (ADN). The method incorporates a distributed constraint optimization problem (DCOP) model with distributed simulated annealing (DSAN) algorithm based on potential game. Moreover, the distributed optimal strategy is developed on the basis of multi-agent system, and the distributed power flow calculation method is utilized to perform the power flow calculation among agents. The performance verification of the distributed optimal control strategy and the comparison with the centralized method are conducted in IEEE 39-bus system with distributed generations (DGs). Finally, the simulation cases are presented and discussed to demonstrate the effectiveness and applicability of the proposed optimal method for ADN.

ABSTRACT. This paper presents a multi-period unit commitment active and reactive power optimal management for microgrids under different market policies. The overall optimization problem is formulated by mixed integer quadratic programming by taking into considerations the environmental costs and the battery degradation cost against a comprehensive set of constraints. A typical low-voltage microgrid, which comprises of distributed generators, renewable energy resources, storage battery, and varieties of loads, are employed to implement and examine the proposed approach. The microgrid is comprehensively in-vestigated with both grid-connected and isolated modes under minimum operation and emission cost and maximum overall profit policies. The results have revealed that the charging and discharging operations of storage battery typically reduce the total operation and emission costs and hence maximize overall profit, even considering the battery degradation.

ABSTRACT. This research furthers the development of a closed-form solution to the angles-only initial relative orbit determination problem for close-in proximity operations when the camera offset from the vehicle center-of-mass allows for range observability. Emphasis is placed on developing modified optimal IROD solution in the context of the Tshcauner-Hempel orbital relative motion equations, closed-form analytic estimates error and covariance and performance analysis through systematic nonlinear Monte Carlo simulation of typical rendezvous missions. A two-body Monte Carlo simulation system is used to evaluate the performance of the closed-form relative state estimation algorithms. The sensitivity of the solution accuracy to spacecraft trajectories, camera offset, camera accuracy, attitude knowledge, and the time-interval between measurements is presented and discussed.

ABSTRACT. To attenuate the effects of inertia uncertainty and external disturbance of combined spacecraft on attitude control accuracy and stability, a composite control law by combining nonlinear disturbance observer (NDO) and backstepping sliding mode control is proposed. In this paper, the nonlinear disturbance observer (NDO) is added to observe the unknown inertia and external disturbance, and asymptotic stability of the backstepping sliding mode control with NDO is demonstrated. The performance of the proposed attitude control for combined spacecraft is discussed, and the simulation results demonstrate the effectiveness and feasibility of the proposed controller.

ABSTRACT. Abstract—Fixed-time controller features an upper bound of settling time, which does not depend on the initial states of control system. In view of that nearly all the existing fixed-time control methods are based on the terminal sliding mode technique, the problem of fixed-time attitude stabilization of a rigid spacecraft is investigated based on a homogeneous method in this paper. Numerical simulations are performed to illustrate the effectiveness of the proposed fixed-time control scheme in the spacecraft attitude control system.

ABSTRACT. The main goal of this work is to examine the performance analysis of attitude determination and estimation algorithms applied to Low Earth Orbit Satellite in term of execution time and accuracy. This performance analysis could be useful to designer to choose from an attitude determination or attitude estimation or attitude observer algorithms. Least Squares, Q-Method, TRIAD, Extended Kalman Filter, Sliding ModeObserver are performed during the analysis performance. Numerical analysis clearly indicates that the Sliding Mode Observer is the more logical choice in terms of time consuming and accuracy.

ABSTRACT. To reach and keep tracking the desired Mars landing trajectory rapidly under various uncertainties and unexpected wind, a power reaching law based sliding mode tracking guidance algorithm is proposed in this paper. The proposed guidance law is composed of three exponential terms, its main improvement is that it can ensure that the lander track the reference trajectory in finite time without chattering. Theoretical proofs are presented to prove its existence, reachability and stabilization characteristics. Compared with existing exponential reaching law, single power reaching law and double power reaching law, simulation results show the effectiveness of the proposed guidance law with a typical Mars powered descent landing scenario