Decentralized optimal controller design for multimachine power systems using successive approximation approach

In this paper, we propose to develop algorithmically and implement a nonlinear decentralized optimal control for multimachine power systems, based on a successive approximation approach for designing the optimal controller with respect to quadratic performance index. The advantage of this approach is to transform the high order coupling nonlinear two-point boundary value (TPBV) problem into a sequence of linear decoupling TPBV problem, which uniformly converges to the optimal control for nonlinear interconnected large scale systems. We apply this approach to a 3-machine power system which generators are strongly nonlinear interconnected, and containing possible uncertainties on the parameters. We demonstrate clearly via advanced simulations that this approach brings better performances than other decentralized controller, improving effectively transient stability of these power systems in few iterative sequences for different cases of perturbations.     

Application of non-dominated sorting genetic algorithm-II technique for optimal FACTS-based controller design

Design of an optimal controller requires optimization of multiple performance measures that are often noncommensurable and competing with each other. Design of such a controller is indeed a multi-objective optimization problem. Non-dominated sorting in genetic algorithms-II (NSGA-II) is a popular non-domination based genetic algorithm for solving multi-objective optimization problems. This paper investigates the application of NSGA-II technique for the design of a flexible AC transmission system (FACTS)-based controller. The design objective is to improve the stability of the power system with minimum control effort. The proposed technique is applied to generate Pareto set of global optimal solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Further, a detailed analysis on the selection of control signals (both local and remote signals) on the effectiveness of the proposed controller is carried out and simulation results are presented under various loading conditions and disturbances to show the effectiveness and robustness of the proposed approach.     

权变激励与有效绩效评价系统设计研究

知识工作背景下,传统以控制为导向的绩效评价系统明显不适应当前快速反应和工作自主要求的组织策略。而现有研究拘泥于结构化设计,缺乏对不同情境下绩效评价有效性的探讨。本文借鉴传统机制设计理论,在满足员工参与约束、激励相容约束的基础上引入承诺约束,构建了一个权变而动态的激励－绩效模型,并据此分离出三种情境下的最优绩效评价系统模式。本文证明,社会控制及自我约束与绩效控制之间存在反向替代关系,在企业与知识员工之间建立关系型心理契约,通过愿景引导、文化熏陶、自我主导等手段,能够深度挖掘员工创造力、组织凝聚力、团队协同力等隐性价值驱动因素,促进企业在更高层面上创造价值。     

Linear controller analysis and design for systems with input hystereses nonlinearities

An output feedback control analysis and design framework for linear systems with input hystereses nonlinearities is developed. Specifically, by transforming the hystereses nonlinearities into dissipative input-output dynamical operators, dissipativity theory is used to analyze and design linear controllers for systems with hysteretic actuators. The overall framework guarantees partial asymptotic stability of the closed-loop system; that is, asymptotic stability with respect to part of the closed-loop system state associated with the plant and the controller. Furthermore, the remainder of the state associated with the hysteresis dynamics is shown to be semistable; that is, solutions of the hysteretic system converge to Lyapunov stable equilibrium points determined by the system initial conditions.     

Robust sensor fault estimation scheme for satellite attitude control systems

The present paper proposes two new schemes of sensor fault estimation for a class of nonlinear systems and investigates their performances by applying these to satellite control systems. Both of the schemes essentially transform the original system into two subsystems (subsystems 1 and 2), where subsystem-1 includes the effects of system uncertainties, but is free from sensor faults and subsystem-2 has sensor faults but without any uncertainties. Sensor faults in subsystem-2 are treated as actuator faults by using integral observer based approach. The effects of system uncertainties in subsystem-1 can be completely eliminated by a sliding mode observer (SMO). In the first scheme, the sensor faults present in subsystem-2 are estimated with arbitrary accuracy using a SMO. In the second scheme, the sensor faults are estimated by designing an adaptive observer (AO). The sufficient condition of stability of the proposed schemes has been derived and expressed as a linear matrix inequality (LMI) optimization problem and the design parameters of the observers are determined by using LMI techniques. The effectiveness of the schemes in estimating sensor faults is illustrated by considering an example of a satellite control system. The results of the simulation demonstrate that the proposed schemes can successfully estimate sensor faults even in the presence of system uncertainties.     

Robust actuator fault diagnosis scheme for satellite attitude control systems

In this paper, a robust actuator fault diagnosis scheme is investigated for satellite attitude control systems subject to model uncertainties, space disturbance torques and gyro drifts. A nonlinear unknown input observer is designed to detect the occurrence of any actuator fault. Subsequently, a bank of adaptive unknown input observers activated by the detection results are designed to isolate which actuator is faulty and then estimate of the fault parameter. Fault isolation is achieved based on the well known generalized observer strategy. The simulation on a closed-loop satellite control system with time-varying or constant actuator faults in the form of additive and multiplicative unknown dynamics demonstrates the effectiveness of the proposed robust fault diagnosis strategy.     

General value iteration based single network approach for constrained optimal controller design of partially-unknown continuous-time nonlinear systems

In this paper, a novel iterative approximate dynamic programming scheme is proposed by introducing the learning mechanism of value iteration (VI) to solve the constrained optimal control problem for CT affine nonlinear systems with utilizing only one neural network. The idea is to show the feasibility of introducing the VI learning mechanism to solve for the constrained optimal control problem from a theoretical point of view, and thus the initial admissible control can be avoided compared with most existing works based on policy iteration (PI). Meanwhile, the initial condition of the proposed VI based method can be more general than the traditional VI method which requires the initial value function to be a zero function. A general analytical method is proposed to demonstrate the convergence property. To simplify the architecture, only one critic neural network is adopted to approximate the iterative value function while implementing the proposed method. At last, two simulation examples are proposed to validate the theoretical results.     

Sliding mode controller design for MIMO nonlinear systems: A novel power rate reaching law approach for improved performance

This paper proposes a novel approach to the design of reaching law based on Sliding Mode Controller (SMC) for multi input multi output (MIMO) non-linear systems so as to overcome the drawbacks associated with conventional reaching law based SMC design strategies. The modification is proposed with an aim to completely eliminate chattering, to ensure control inputs within admissible limits and to guarantee fast response when SMC is used. Modification to conventional power rate reaching law is the point of interest here in order to ensure complete elimination of chattering. Two different modifications to power rate reaching law are presented which incorporate control constraints during controller design so that admissible control input limits are not exceeded. The first modified method ensures limited control effort as well as complete chattering free response, but does not improve the reaching characteristics. So a second adaptive modification to power rate reaching law is also presented here. This method ensures fast reaching to the sliding surface along with properties of complete elimination of chattering and bounded control inputs. However, as in power rate reaching law these modified methods retain the limitation of not possessing robustness properties. The method is applied to a three degree of freedom robotic arm which is typically a non-linear MIMO system. The ability of the presented method to satisfy attributes, viz., chattering free operation, bounded control inputs and fast response is compared with the performance of various reaching law methods available in the literature. The performance of the proposed method is validated through simulation studies on the robotic arm example.     

Exponential stability analysis and controller design of fuzzy systems with time-delay

Takagi-Sugeno (T-S) fuzzy models can provide an effective representation of complex nonlinear systems with a series of linear input/output submodels in terms of fuzzy sets and fuzzy reasoning. In this paper, the T-S fuzzy model approach is extended to the stability analysis and controller design for nonlinear systems with time delays. An improved stability condition is proposed by introducing adjustable parameters into the Lyapunov-Krasovskii functional. Stabilization approach for fuzzy state feedback is also presented. Sufficient conditions for the existence of fuzzy feedback gain are derived through the numerical solution of a set of obtained linear matrix inequalities (LMIs). Compared with the existing methods in the literature, the proposed approach has less conservatism and both the sizes of delay and its derivative are involved in the criterion. The dynamical performance of the system can be adjusted by changing the adjustable parameters. Finally, two examples are given to show the effectiveness of the proposed approach.     

Adaptive controller design for spacecraft formation flying using sliding mode controller and neural networks

A spacecraft formation flying controller is designed using a sliding mode control scheme with the adaptive gain and neural networks. Six-degree-of-freedom spacecraft nonlinear dynamic model is considered, and a leader–follower approach is adopted for efficient spacecraft formation flying. Uncertainties and external disturbances have effects on controlling the relative position and attitude of the spacecrafts in the formation. The main benefit of the sliding mode control is the robust stability of the closed-loop system. To improve the performance of the sliding mode control, an adaptive controller based on neural networks is used to compensate for the effects of the modeling error, external disturbance, and nonlinearities. The stability analysis of the closed-loop system is performed using the Lyapunov stability theorem. A spacecraft model with 12 thrusts as actuators is considered for controlling the relative position and attitude of the follower spacecraft. Numerical simulation results are presented to show the effectiveness of the proposed controller.     

Tracking controller design for random nonlinear benchmark system

This paper considers a benchmark system consisting of a rolling ball and a moving car in the oscillating surroundings. By using the Lagrange law, the dynamic model without disturbance is first constructed, then according to the relative motion principle, random oscillation of surroundings is transformed into the random noises in the constructed Lagrange equation. The special structure of the quasi-lower triangle of Lagrange equation motivates us to pay more attention to the vectorial backstepping technique. By selecting an appropriate Lyapunov-like function, a tracking controller with tunable parameters is designed such that all signals of the closed-loop system are bounded and track error can be made arbitrarily small.     

Control Lyapunov function optimal sliding mode controllers for attitude tracking of spacecraft

The attitude tracking control problem for a rigid spacecraft using two optimal sliding mode control laws is addressed. Integral sliding mode (ISM) control is applied to combine the first-order sliding mode with optimal control and is applied to quaternion-based spacecraft attitude tracking maneuvres with external disturbances and an uncertainty inertia matrix. For the optimal control part the control Lyapunov function (CLF) approach is used to solve the infinite-time nonlinear optimal control problem, whereas the Lyapunov optimizing control (LOC) method is applied to solve the finite-time nonlinear optimal control problem. The second method of Lyapunov is used to show that tracking is achieved globally. An example of multiaxial attitude tracking maneuvres is presented and simulation results are included to demonstrate and verify the usefulness of the proposed controllers.     

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system

This article presents a novel tuning design of Proportional-Integral-Derivative (PID) controller in the Automatic Voltage Regulator (AVR) system by using Cuckoo Search (CS) algorithm with a new time domain performance criterion. This performance criterion was chosen to minimize the maximum overshoot, rise time, settling time and steady state error of the terminal voltage. In order to compare CS with other evolutionary algorithms, the proposed objective function was used in Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms for PID design of the AVR system. The performance of the proposed CS based PID controller was compared to the PID controllers tuned by the different evolutionary algorithms using various objective functions proposed in the literature. Dynamic response and a frequency response of the proposed CS based PID controller were examined in detail. Moreover, the disturbance rejection and robustness performance of the tuned controller against parametric uncertainties were obtained, separately. Energy consumptions of the proposed PID controller and the PID controllers tuned by the PSO and ABC algorithms were analyzed thoroughly. Extensive simulation results demonstrate that the CS based PID controller has better control performance in comparison with other PID controllers tuned by the PSO and ABC algorithms. Furthermore, the proposed objective function remarkably improves the PID tuning optimization technique.     

Actuator failure compensation and attitude control for rigid satellite by adaptive control using quaternion feedback

The attitude control problem of a rigid satellite with actuator failure uncertainties and external disturbance is addressed using adaptive control method. A discontinuous adaptive failure compensation controller, using unit quaternion and angular velocities feedback, is designed to accommodate the external disturbance and actuator failures which are uncertain in time instants, values and patterns. A common approximate function is used to avoid system chattering caused by such discontinuous control laws. The parameters of external disturbance and failure uncertainties are estimated directly by adaptive laws, and the desired stability and output tracking properties of the adaptive control system are analyzed. Finally, simulation results of a rigid satellite with six reaction wheels are presented to illustrate the performance of the proposed adaptive actuator failure compensation scheme.     

Decentralized sampled-data fuzzy controller design for a VTOL UAV

This study focuses on a sampled-data fuzzy decentralized tracking control problem for a quadrotor unmanned aerial vehicle (UAV) under the variable sampling rate condition. To this end, the overall dynamics of the quadrotor is expressed as a decentralized Takagi–Sugeno (T–S) fuzzy model interconnected with each other. Although the proposed decentralized control technique divides the overall UAV control system into attitude and position subsystems, the stability of the entire control system is guaranteed. Besides, in this paper, the model uncertainty, interconnection, and reference trajectory are considered as disturbances acting on the tracking error. To attenuate these disturbances, a novel sampled-data tracking control design technique is derived based on a linear reference model to be tracked and the time-dependent Lyapunov–Krasovskii functional (LKF). By doing so, both the stability of the tracking error dynamics and the minimization of tracking performance are guaranteed. Also, the proposed tracking control design method is derived as a linear matrix inequality (LMI)-based optimal problem. Finally, a simulation example is provided to demonstrate the effectiveness and feasibility of the proposed design methodology.