A STABLE ON‐LINE SELF‐TUNING OPTIMAL PID CONTROLLER FOR A CLASS OF UNKNOWN SYSTEMS

In this paper, a novel self‐tuning method of optimal PID control laws is proposed for both continuous‐time systems and discrete‐time systems. The controlled plant is assumed to be unknown except the system order (or system delay) and the direction of transmitting control input. Through the minimization of PID gains subject to the Lyapunov stability based reaching condition, the tuning of the three PID control gains is transformed to solve the inequality constraint optimization problem. An unknown SISO nonlinear system subject to a unit step input, and the tracking control problem of the piezoelectric actuator (PZA) with unknown dynamics are simulated. The simulation results show that the excellent tracking performance can be achieved.     

Global sampled‐data output‐feedback stabilization for nonlinear systems with unknown measurement sensitivity

This paper investigates the problem of global output‐feedback stabilization by sampled‐data control for nonlinear systems with unknown measurement sensitivity. By employing the technique of output‐feedback domination, a sampled‐data output‐feedback control law together with a sampled‐data state observer is explicitly constructed. By an exquisite selection of both the domination gain and sampling period, the resultant control law is a globally asymptotic stabilizer even in the presence of unknown measurement sensitivity. The novelty of this paper is the development of a distinct approach which can tackle the problem of output‐feedback stabilization for the nonlinear systems with unknown measurement sensitivity.     

Adaptive NN output‐feedback decentralized stabilization for a class of large‐scale stochastic nonlinear strict‐feedback systems

In this paper, the decentralized adaptive neural network (NN) output‐feedback stabilization problem is investigated for a class of large‐scale stochastic nonlinear strict‐feedback systems, which interact through their outputs. The nonlinear interconnections are assumed to be bounded by some unknown nonlinear functions of the system outputs. In each subsystem, only a NN is employed to compensate for all unknown upper bounding functions, which depend on its own output. Therefore, the controller design for each subsystem only need its own information and is more simplified than the existing results. It is shown that, based on the backstepping method and the technique of nonlinear observer design, the whole closed‐loop system can be proved to be stable in probability by constructing an overall state‐quartic and parameter‐quadratic Lyapunov function. The simulation results demonstrate the effectiveness of the proposed control scheme. Copyright © 2010 John Wiley & Sons, Ltd.     

Discrete‐Time Super‐Twisting Guidance Law with Actuator Faults Consideration

This paper proposes a robust fault‐tolerant guidance law against unknown maneuvering targets based on discrete‐time sliding mode control theory. To address this problem, a time‐delay observer is designed to estimate the lumped disturbance, which includes target maneuver as well as actuator faults. A robust discrete‐time guidance law is then synthesized based on the discrete‐time super‐twisting algorithm. Due to the principle of the super‐twisting algorithm, the presented guidance law is a naturally chattering‐free formulation. Detailed stability analysis shows that the line‐of‐sight angular rate under the proposed guidance law can be stabilized in a small region around zero. Simulation results are also provided to verify the effectiveness of the proposed approach.     

Observer‐based finite‐time output‐feedback controller for DC‐DC buck converters with unknown load variations

The output voltage regulation problem of a DC‐DC buck converter is investigated in this paper via an observer‐based finite‐time output‐feedback control approach. Considering the effects of unknown load variations and the case without current sensor, by using the technique of adding a power integrator and the idea of nonseparation principle, a finite‐time voltage regulation control algorithm via dynamic output feedback is designed. The main feature of the designed observer and controller does not need any load's information. Theoretically, it is proven that the output voltage can reach the desired voltage in a finite time under the proposed controller. The effectiveness of the proposed control method is illustrated by numerical simulations and experimental results.     

Global adaptive stabilization for high‐order uncertain time‐varying nonlinear systems with time‐delays

This paper focuses on the adaptive stabilization problem for a class of high‐order nonlinear systems with time‐varying uncertainties and unknown time‐delays. Time‐varying uncertain parameters are compensated by combining a function gain with traditional adaptive technique, and unknown multiple time‐delays are manipulated by the delicate choice of an appropriate Lyapunov function. With the help of homogeneous domination idea and recursive design, a continuous adaptive state‐feedback controller is designed to guarantee that resulting closed‐loop systems are globally uniformly stable and original system states converge to zero. The effectiveness of the proposed control scheme is illustrated by the stabilization of delayed neural network systems. Copyright © 2016 John Wiley & Sons, Ltd.     

Third‐order sliding mode fault‐tolerant control for satellites based on iterative learning observer

The attitude fault‐tolerant control problem for a satellite with reaction‐wheel failures, uncertainties, and unknown external disturbances is investigated in this paper. Firstly, an iterative learning observer (ILO) is proposed to achieve fault detection, isolation, and estimation. Secondly, based on the ILO, a third‐order sliding mode controller is proposed to stabilize the satellite attitude rapidly under unknown external disturbances and reaction‐wheel faults. Thirdly, the asymptotically stability of the ILO and the third‐order sliding mode controller is proved by using the Lyapunov stability theory. Finally, simulation results demonstrate that the proposed control scheme is more effective and feasible by comparing with other fault‐tolerant control approach.     

Observer‐based decentralized adaptive control for large‐scale pure‐feedback systems with unknown time‐delayed nonlinear interactions

This paper presents an approximation design for a decentralized adaptive output‐feedback control of large‐scale pure‐feedback nonlinear systems with unknown time‐varying delayed interconnections. The interaction terms are bounded by unknown nonlinear bounding functions including unmeasurable state variables of subsystems. These bounding functions together with the algebraic loop problem of virtual and actual control inputs in the pure‐feedback form make the output‐feedback controller design difficult and challenging. To overcome the design difficulties, the observer‐based dynamic surface memoryless local controller for each subsystem is designed using appropriate Lyapunov‐Krasovskii functionals, the function approximation technique based on neural networks, and the additional first‐order low‐pass filter for the actual control input. It is shown that all signals in the total controlled closed‐loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin. Finally, simulation examples are provided to illustrate the effectiveness of the proposed decentralized control scheme. Copyright © 2013 John Wiley & Sons, Ltd.     

Event‐Triggered Consensus of Nonlinear Multi‐Agent Systems with Unknown External Disturbance

This paper addresses the consensus problem of nonlinear multi‐agent systems with unknown external disturbance. First, a distributed observer is proposed to estimate the state and unknown disturbance of each agent simultaneously. Then, a novel event‐triggered control scheme based on the agent state estimation and disturbance estimation is proposed. Unlike the existing strategies, our event‐triggered conditions depend on agent state estimation and disturbance estimation, which are more effective and practical. Under this observer and control strategy, some sufficient conditions are derived to ensure the consensus of the multi‐agent system with unknown external disturbance. Moreover, the Zeno‐behavior of triggering time sequences is also excluded. Finally, a simulation example is given to verify the theoretical analysis.     

Adaptive Neural Dynamic Surface Control For Nonlinear Pure‐Feedback Systems With Multiple Time‐Varying Delays: A Lyapunov‐Razumikhin Method

This paper focuses on the problem of adaptive neural control for a class of uncertain nonlinear pure‐feedback systems with multiple unknown time‐varying delays. The considered problem is challenging due to the non‐affine pure‐feedback form and the unknown system functions with multiple unknown time‐varying delays. Based on a novel combination of mean value theorem, Razumikhin functional method, dynamic surface control (DSC) technique and neural network (NN) parameterization, a new adaptive neural controller which contains only one parameter is developed for such systems. Moreover, The DSC technique can overcome the problem of ‘explosion of complexity’ in the traditional backstepping design procedure. All closed‐loop signals are shown to be semi‐globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. Two simulation examples are given to verify the effectiveness of the proposed design.     

Design of functional interval observers for non‐linear fractional‐order systems

This paper considers the problem of designing functional interval observers for a class of non‐linear fractional‐order systems with bounded uncertainties. First, interval observers for linear functions of the state vector of the considered system are designed. Then, conditions for the existence of such interval observers are established and an effective algorithm for computing unknown observer matrices is provided in this paper. Finally, numerical examples and simulation results are given to illustrate the effectiveness of the proposed design method.     

A complete parameterization of clf‐based input‐to‐state stabilizing control laws

Sontag's formula proves constructively that the existence of a control Lyapunov function implies asymptotic stabilizability. A similar result can be obtained for systems subject to unknown disturbances via input‐to‐state stabilizing control Lyapunov functions (ISS‐clfs) and the input‐to‐state analogue of Sontag's formula. The present paper provides a generalization of the ISS version of Sontag's formula by completely parameterizing all continuous ISS control laws that can be generated by a known ISS‐clf. When a simple inner‐product constraint is satisfied, this parameterization also conveniently describes a large family of ISS controls that solve the inverse‐optimal gain assignment problem, and it is proved that these controls possess Kalman‐type gain margins. Copyright © 2004 John Wiley & Sons, Ltd.     

Comparison of image reconstruction by using near‐field and far‐field data for an imperfect conductor

Image reconstruction by using near‐field and far‐field data for an imperfectly conducting cylinder is investigated. A conducting cylinder of unknown shape and conductivity scatters the incident wave in free space and the scattered near and far fields are measured. By using measured fields, the imaging problem is reformulated into an optimization problem and solved by the genetic algorithm. Numerical results show that the convergence speed and final reconstructed results by using near‐field data are better than those obtained by using far‐field data. This work provides both comparative and quantitative information. © 2001 John Wiley & Sons, Inc. Int J RF and Microwave CAE 11: 69–73, 2001.     

Adaptive and Robust Stabilization of Feedforward Nonlinear Systems Driven by Symmetric and Non‐symmetric Dead‐zone Inputs

The stabilization of feedforward nonlinear systems subject to hard‐input nonlinearities is a challenging problem due to the presence of input uncertainties. This paper deals with adaptive control of a class of feedforward nonlinear systems driven by unknown dead‐zone inputs. The unknown dead‐zone input nonlinearity is assumed to be either symmetric or non‐symmetric. The control design is based on the combination of the invariant‐manifold stabilization technique with the classical adaptive and robust compensation methods. Simulation results showed that the presence of the dead‐zone inputs in the system dynamics can be handled even for arbitrary large dead‐zone parameters.     

Composite stratified anti‐disturbance control for a class of MIMO discrete‐time systems with nonlinearity

Anti‐disturbance control and estimation problem are investigated for nonlinear system subject to multi‐source disturbances. The disturbances classified model is proposed based on the error and noise analysis of priori knowledge. The disturbance observers are constructed separately from the controller design to estimate the disturbance with partial known information. By integrating disturbance‐observer‐based control with discrete‐time sliding‐mode control (DSMC), a novel type of composite stratified anti‐disturbance control scheme is presented for a class of multiple‐input–multiple‐output discrete‐time systems with known and unknown nonlinear dynamics, respectively. Simulations for a flight control system are given to demonstrate the effectiveness of the results compared with the previous schemes. Copyright © 2011 John Wiley & Sons, Ltd.     

Stochastic Optimal Design for Unknown Linear Discrete‐Time System Zero‐Sum Games in Input‐Output form Under Communication Constraints

In this paper, stochastic optimal strategy for unknown linear discrete‐time system quadratic zero‐sum games in input‐output form with communication imperfections such as network‐induced delays and packet losses, otherwise referred to as networked control system (NCS) zero‐sum games, relating to the H_∞ optimal control problem is solved in a forward‐in‐time manner. First, the linear discrete‐time zero sum state space representation is transformed into a linear NCS in the state space form after incorporating random delays and packet losses and then into the input‐output form. Subsequently, the stochastic optimal approach, referred to as adaptive dynamic programming (ADP), is introduced which estimates the cost or value function to solve the infinite horizon optimal regulation of unknown linear NCS quadratic zero‐sum games in the presence of communication imperfections. The optimal control and worst case disturbance inputs are derived based on the estimated value function in the absence of state measurements. An update law for tuning the unknown parameters of the value function estimator is derived and Lyapunov theory is used to show that all signals are asymptotically stable (AS) and that the estimated control and disturbance signals converge to optimal control and worst case disturbances, respectively. Simulation results are included to verify the theoretical claims.     

A low‐complexity design for distributed containment control of networked pure‐feedback systems and its application to fault‐tolerant control

This paper addresses a low‐complexity distributed containment control problem and its extension to fault‐tolerant control for networked nonlinear pure‐feedback systems under a directed graph. The multiple dynamic leaders are neighbors of only a subset of the followers described by completely non‐affine multi‐input multi‐output pure‐feedback dynamics. It is assumed that all followers' nonlinearities are heterogeneous and unknown. The proposed containment controller is implemented by using only error surfaces integrated by performance bounding functions and does not require any differential equations for compensating uncertainties and faults. Thus, compared with the previous containment control approaches for multi‐agent systems with unknown non‐affine nonlinearities, the distributed containment control structure is simplified. In addition, it is shown that the proposed control scheme can be applied to the fault‐tolerant containment control problem in the presence of unexpected system and actuator faults, without reconstructing any control structure. It is shown from Lyapunov stability theorem that all followers nearly converge to the dynamic convex hull spanned by the dynamic leaders and the containment control errors are preserved within certain given predefined bounds. Copyright © 2016 John Wiley & Sons, Ltd.     

Adaptive Composite Fuzzy Dynamic Surface Control for Electro‐Hydraulic‐System,with Variable‐Supply‐Pressure

This paper presents a novel adaptive composite fuzzy dynamic surface controller for a variable‐supply‐pressure electro‐hydraulic‐system in the presence of unknown nonlinear friction effects. To avoid analytic calculation, command filters are utilized to produce certain virtual controllers and their derivatives. A fuzzy logic system is designed to approximate and compensate the unknown nonlinear friction influences of the electro‐hydraulic‐system. To achieve a precise approximation, the prediction error of a designed serial‐parallel estimation model and the compensated tracking error are both used to develop the composite adaptive law. Comparative simulation and experimental results are obtained to verify the effectiveness of the proposed control method.     

DELAY‐DEPENDENT ROBUST CONTROL FOR UNCERTAIN SINGULAR TIME‐DELAY SYSTEMS

The problem of delay‐dependent robust stabilization for uncertain singular time‐delay systems is investigated in this paper. The parameter uncertainty is assumed to be norm‐bounded and possibly time‐varying, while the time delay considered here is assumed to be constant but unknown. A delay‐dependent condition is presented for a singular time‐delay system to be regular, impulse free, and stable, based on which robust stability analysis and the robust stabilization problem are studied. An explicit expression for the desired state‐feedback control law is also given. The obtained results are formulated in terms of linear matrix inequalities (LMIs), which involve no decomposition of the system matrices. Some numerical examples are given to show the efficiency of the theoretical conditions.     

Worst‐case optimal control for an electrical drive system with time‐delay

This paper considers an optimal control developed for an electrical drive system with a DC motor. Since it is a linear control system with input time‐delay subject to unknown but bounded disturbances, we construct a worst‐case feedback control policy, which can guarantee that, for all admissible uncertain disturbances, the real system state should be in a prescribed neighborhood of a desired value at the given final time, and the cost functional takes the best guarantee value. The worst‐case feedback control policy is allowed to be corrected at a given set of correction points between the initial and the final time, which is equivalent to solving a (m‐1)‐level min‐max problem. Since the min‐max problem at each stage does not yield a simple analytical solution, construction of the optimal policy is computationally prohibitive. This is why we consider an approximate control policy which is more convenient for computation. The simulation results illustrate that this proposed approach is feasible. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society