Combined indirect and direct method for adaptive fuzzy output feedback control of nonlinear system

A novel control method for a general class of nonlinear systems using fuzzy logic systems (FLSs) is presertted. Indirect and direct methods are combined to design the adaptive fuzzy output feedback controller and a high-gain observer is used to estimate the derivatives of the system output. The closed-loop system is proven to be semiglobally uniformly ultimately bounded. In addition, it is shown that if the approximation accuracy of the fuzzy logic system is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussion.     

Decentralized adaptive fuzzy control of time-delayed interconnected systems with unknown backlash-like hysteresis

The problem of decentralized adaptive fuzzy control for a class of time-delayed interconnected nonlinear systems with unknown backlash-like hystersis is discussed. On the basis of the principle of variable structure control （VSC） and by using the fuzzy systems with linear adjustable parameters that are used to approximate plant unknown functions, a novel decentralized adaptive fuzzy control strategy with a supervisory controller is developed. A general method, which is modeled the backlash-like hysteresis, is proposed and removes the assumption that the boundedness of disturbance, and the slope of the backlash-like hystersis are known constants. Furthermore, the interconnection term is supposed to be pth-order polynomial in time-delayed states. In addition, the plant dynamic uncertainty and modeling errors are adaptively compensated by adjusting the parameters and gains on-line for each subsystems. By theoretical analysis, it is shown that the closed-loop fuzzy control systems are globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.     

Simultaneous anti-windup synthesis for linear systems subject to actuator saturation

A synthesis method for global stability and performance of input constrained linear systems, which uses a linear outputfeedback controller and a static anti-windup compensator is investigated. Different from the traditional two-step anti-windup design procedure, the proposed method synthesizes all controller parameters simultaneously. Sufficient conditions for global stability and minimizing the induced L2 gain are formulated and solved as a linear matrix inequalities(LMIs) optimization problem, which also provides an opportunity to search for a better performance tradeoff between the linear controller and the anti-windup compensator.The well-posedness of the close-loop system is also guaranteed.Simulation results show the effectiveness of the proposed method.     

AN INTELLIGENT CONTROL SYSTEM BASED ONRECURRENT NEURAL FUZZY NETWORK AND ITS APPLICATION TO CSTR

In this paper, an intelligent control system based on recurrent neural fuzzy network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural fuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neural network based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradient information ρy/ρu for optimizing the parameters of controller.Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller. Moreover, recursive predictive error algorithm (RPE) is im-plemented to construct RNNM on line. Lastly, in order to evaluate the performance of the proposed control system, the presented control system is applied to continuously stirred tank reactor (CSTR). Simulation comparisons, based on control effect and output error,with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC),are conducted. In addition, the rates of convergence of RNNM respectively using RPE algorithm and gradient learning algorithm are also compared. The results show that the proposed control system is better for controlling uncertain and nonlinear processes.     

Delay-dependent robust H∞ control of convex polyhedral uncertain fuzzy systems

The robust H∞ control problem for a class of uncertain Takagi-Sugeno fuzzy systems with timevarying state delays is studied. The uncertain parameters are supposed to reside in a polytope. Based on the delay-dependent Lyapunov functional method, a new delay-dependent robust H∞ fuzzy controller, which depends on the size of the delays and the derivative of the delays, is presented in term of linear matrix inequalities （LMIs）. For all admissible uncertainties and delays, the controller guarantees not only the asymptotic stability of the system but also the prescribed H∞ attenuation level. In addition, the effectiveness of the proposed design method is demonstrated by a numerical example.     

A Novel Robust Adaptive Fuzzy Controller

For a class of continuous-time nonlinear system, a novel robust adaptive fuzzy controller is proposed by using of Lyapunov method. It is proven that the control algorithm is globally stable, the output tracking-error can convergence to a domain of zero under the assumptions. As a result, the system controlled has stronger robustness for disturbance and modeling error.     

H_∞ Control for Nonlinear Stochastic Markov Systems with Time-Delay and Multiplicative Noise

This paper is concerned with the H_∞ control problem for a class of nonlinear stochastic Markov jump systems with time-delay and system state-, control input-and external disturbancedependent noise. Firstly, by solving a set of Hamilton-Jacobi inequalities(HJIs), the exponential mean square H_∞ controller design of delayed nonlinear stochastic Markov systems is presented. Secondly,by using fuzzy T-S model approach, the H_∞ controller can be designed via solving a set of linear matrix inequalities(LMIs) instead of HJIs. Finally, two numerical examples are provided to show the effectiveness of the proposed design methods.     

Robust H∞ output feedback controller design for uncertain discrete-time switched systems via switched Lyapunov functions

The H∞ output feedback control problem for uncertain discrete-time switched systems is reasearched. A new characterization of stability and H∞ performance for the switched system under arbitrary switching is obtained by using switched Lyapunov function.Then,based on the characterization,a linear matrix inequality (LMI)approach is developed to design a switched output feedback controller which guarantees the stability and H∞ performance of the closed-loop system.A numerical example is presented to demonstrate the application of the proposed method.     

Adaptive neural control for a class of uncertain stochastic nonlinear systems with dead-zone

The problem of adaptive stabilization is addressed for a class of uncertain stochastic nonlinear strict-feedback systems with both unknown dead-zone and unknown gain functions.By using the backstepping method and neural network(NN) parameterization,a novel adaptive neural control scheme which contains fewer learning parameters is developed to solve the stabilization problem of such systems.Meanwhile,stability analysis is presented to guarantee that all the error variables are semi-globally uniformly ultimately bounded with desired probability in a compact set.The effectiveness of the proposed design is illustrated by simulation results.     

Novel active fault-tolerant control scheme and its application to a double inverted pendulum system

On the basis of the gain-scheduled H∞ design strategy, a novel active fault-tolerant control scheme is proposed. Under the assumption that the effects of faults on the state-space matrices of systems can be of affine parameter dependence, a reconfigurable robust H∞ linear parameter varying controller is developed. The designed controller is a function of the fault effect factors that can be derived online by using a well-trained neural network. To demonstrate the effectiveness of the proposed method, a double inverted pendulum system, with a fault in the motor tachometer loop, is considered.     

Neural network-based H∞ filtering for nonlinear systems with time-delays

A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed. Firstly, neural networks are employed to approximate the nonlinearities. Next, the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion （LDI）. Finally, based on the LDI model, a neural network-based nonlinear filter （NNBNF） is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality （LMI） constraints. Compared with the existing nonlinear filters, NNBNF is time-invariant and numerically tractable. The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.     

Neural network based adaptive sliding mode control of uncertain nonlinear systems

The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode control (SMC). Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. In addition to keeping the stability and robustness properties of the SMC, the neural network-based adaptive sliding mode controller exhibits perfect rejection of faults arising during the system operating. Simulation studies are used to illustrate and clarify the theoretical results.     

STAP method based on atomic norm minimization with array amplitude-phase error calibration

In this paper,a space-time adaptive processing(STAP)method is proposed for the airborne radar with the array amplitude-phase error considered,which is based on atomic norm minimization(ANM).In the conventional ANM-based STAP method,the influence of the array amplitude-phase error is not considered and restrained,which inevitably causes performance deterioration.To solve this problem,the array amplitude-phase error is firstly estimated.Then,by pre-estimating the array amplitude-phase error information,a modified ANM model is built,in which the array amplitude-phase error factor is separated from the clutter response and the clutter covariance matrix(CCM)to improve the estimation accuracy of the CCM.To prove that the atomic norm theory is applicable in the presence of the array amplitude-phase error,the clutter sparsity is analyzed in this paper.Meanwhile,simulation results demonstrate that the proposed method is superior to the state-of-the-art STAP method.Moreover,the measured data is used to verify the effectiveness of the proposed method.     

Adaptive fuzzy controller for a class of strict-feedback nonaffine nonlinear systems

A robust adaptive fuzzy control scheme is presented for a class of strict-feedback nonaffine nonlinear systems with modeling uncertainties and external disturbances by using a backstepping approach.Fuzzy logic systems are employed to approximate the unknown parts of the desired virtual controls,and the approximation errors of fuzzy systems are only required to be norm-bounded.The function tanh(·) is introduced to avoid problems associated with sgn(·).The tracking error is guaranteed to be uniformly ultimately bounded with the aid of an additional adaptive compensation term.Chua’s circuit system and R o¨ssler system are presented to illustrate the feasibility and effectiveness of the proposed control technique.     

Robust H∞ control for networked control systems

The robust H∞ control for networked control systems with both stochastic network-induced delay and data packet dropout is studied. When data are transmitted over network, the stochastic data packet dropout process can be described by a two-state Markov chain. The networked control systems with stochastic network-induced delay and data packet dropout are modeled as a discrete time Markov jump linear system with two operation modes. The sufficient condition of robust H∞ control for networked control systems stabilized by state feedback controller is presented in terms of linear matrix inequality. The state feedback controller can be constructed via the solution of a set of linear matrix inequalities. An example is given to verify the effectiveness of the method proposed.     

Adaptive Robust Dead-Zone Compensation Control of Electro-Hydraulic Servo Systems with Load Disturbance Rejection

A backstepping method based adaptive robust dead-zone compensation controller is proposed for the electro-hydraulic servo systems(EHSSs) with unknown dead-zone and uncertain system parameters.Variable load is seen as a sum of a constant and a variable part.The constant part is regarded as a parameter of the system to be estimated real time.The variable part together with the friction are seen as disturbance so that a robust term in the controller can be adopted to reject them.Compared with the traditional dead-zone compensation method,a dead-zone compensator is incorporated in the EHSS without constructing a dead-zone inverse.Combining backstepping method,an adaptive robust controller(ARC) with dead-zone compensation is formed.An easy-to-use ARC tuning method is also proposed after a further analysis of the ARC structure.Simulations show that the proposed method has a splendid tracking performance,all the uncertain parameters can be estimated,and the disturbance has been rejected while the dead-zone term is well estimated and compensated.     

Fuzzy Static Output Feedback H_∞ Control for Nonlinear Systems Subject to Parameter Uncertainties

This study is concerned with the stabilization issue of nonlinear systems subject to parameter uncertainties. An interval type-2 T-S fuzzy model is used to represent the nonlinear systems subject to parameter uncertainties. An interval type-2 fuzzy static output feedback controller is designed to synthesize the interval type-2 T-S fuzzy systems. The membership-function-dependent stability conditions are derived by utilizing the information of upper and lower membership functions. The proposed stability conditions are presented in the form of linear matrix inequalities(LMIs). LMI-based stability conditions for interval type-2 fuzzy static output feedback H_∞ control synthesis are also developed.Several simulation examples are given to show the superiority of the proposed approach.     

Robust decentralized H∞ control of multi-channel systems with norm-bounded parametric uncertainties

A robust decentralized H∞ control problem for uncertain multi-channel systems is considered. The uncertainties are assumed to be time-invariant, norm-bounded, and exist in both the system and control input matrices. The dynamic output feedback is mainly dealt with. A necessary and sufficient condition for the uncertain multi-channel system to be stabilized robustly with a specified disturbance attenuation level is derived based on the bounded real lemma, which is reduced to a feasibility problem of a nonlinear matrix inequality (NMI). A two-stage homotopy method is used to solve the NMI iteratively. First, a decentralized controller for the nominal system with no uncertainty is computed by imposing structural constraints on the coefficient matrices of the controller gradually. Then the decentralized controller is modified, again gradually, to cope with the uncertainties. On each stage, a variable is fixed alternately at the iterations to reduce the NMI to a linear matrix inequality (LMI). A given example shows the efficiency of this method.     

H_∞-based fault detection for nonlinear networked systems with random packet dropout and probabilistic interval delay

The fault detection problem for the nonlinear networked control system(NCS) with packet dropout and delay is investigated.A nonlinear stochastic system model is proposed to account for the NCS with random packet dropout and networkinduced non-uniformly distributed time-varying delay in both from sensor to controller(S/C) and from controller to actuator(C/A).Based on the obtained NCS model,employing an observer-based fault detection filter as the residual generator,the addressed fault detection problem is converted into an auxiliary nonlinear H∞ control problem.Then,with the help of Lyapunov functional approach,a sufficient condition for the desired fault detection filter is constructed in terms of certain linear matrix inequalities,which depend on not only the delay interval but also the delay interval occurrence rate and successful packet communication rate.Especially,a trade-off phenomenon between the maximum allowable delay bound and successful data packet transmission rate is found,which is typically resulted from the limited bandwidth of communication networks.The effectiveness of the proposed method is demonstrated by a simulation example.     

FBFN-based adaptive repetitive control of nonlinearly parameterized systems

An adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on the fuzzy basis function network （FBFN）. The parameters of the fuzzy rules are tuned with adaptive schemes. To attenuate chattering effectively, the discontinuous control term is approximated by an adaptive PI control structure. The bound of the discontinuous control term is assumed to be unknown and estimated by an adaptive mechanism. Based on the Lyapunov stability theory, an adaptive repetitive control law is proposed to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, which avoid the nonlinear parameterization from entering into the adaptive repetitive control, the controller singularity problem is solved. The proposed approach does not require an exact structure of the system dynamics, and the proposed controller is utilized to control a model of permanent-magnet linear synchronous motor subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method.