GA-based intelligent digital redesign of fuzzy-model-based controllers

Intelligent digital redesign involves converting a continuous-time fuzzy-model-based controller into an equivalent discrete-time counterpart for the digital control of continuous-time nonlinear systems by using the Takagi-Sugeno (TS) fuzzy models. In this paper, the authors present a new global state-matching intelligent digital redesign method for nonlinear systems by using genetic algorithms (GAs). More precisely, the intelligent digital redesign problem is converted to an equivalent optimization problem, and then GAs are adopted to find a solution. The search space, in which each problem variable is defined for GAs, are systematically obtained by the interval arithmetic operations. The proposed method results in global matching of the states of the analogously controlled system with those of the digitally controlled system while the conventional intelligent digital redesign method does not. The Chen's chaotic system is used as an illustrative example to show the effectiveness and the feasibility of the developed method. The proposed method provides a new approach for the digital redesign of a class of fuzzy-model-based controllers.     

Hybrid suboptimal control of multi-rate multi-loop sampled-data systems

A hybrid state-space control scheme for suboptimal digital control of a cascaded continuous-time system using dual rate sampling is presented. First, an optimal regional-pole placement technique is utilized to find an optimal state-feedback control law for a subsystem connected in the inner loop of the overall system. Next, the designed analogue control law is converted into an equivalent fast-rate digital control law using the recently developed digital redesign technique. Then, the digitally redesigned subsystem is converted into an equivalent continuous-time model. As a result, the overall continuous-time model can be formulated from the converted analogue subsystem and the rest of the analogue subsystems to be designed. Moreover, the optimal regional-pole placement technique is applied again to the overall continuous-time model in order to obtain the overall analogue state-feedback control law. Finally, the digital redesign technique is employed again to convert the overall analogue control law obtained to an equivalent slow-rate digital control law. For practical implementations of the developed digital control laws with various sampling rates, the existing ideal state reconstructor method is redeveloped to construct the ideal discrete-lime states using multi-rate input-output data. A practical semi-active terminal homing missile is used as an illustrative example to demonstrate the proposed design method.     

Optimal digital controller and observer design for multiple time-delay transfer function matrices with multiple input–output time delays

In this article, we address the optimal digital design methodology for multiple time-delay transfer function matrices with multiple input–output time delays. In our approach, the multiple time-delay analogue transfer function matrix with multiple input–output time delays is minimally realised using a continuous-time state-space model. For deriving an explicit form of the optimal digital controller, the realised continuous-time multiple input–output time-delay system is discretised, and an extended high-order discrete-time state-space model is constructed for discrete-time LQR design. To derive a low-order optimal digital observer for the multiple input–output time-delay system, the multiple time-delay state obtained from the multiple time-delay outputs is discretised. Then, the well-known duality concept is employed to design an optimal digital observer using the low-order discretised multiple input time-delay system together with the newly discretised multiple time-delay state. The proposed approach is restricted to multiple time-delay systems where multiple time delays arise only in the input and output, and not in the state.     

OBSERVER-BASED HYBRID CONTROL OF SAMPLED-DATA UNCERTAIN SYSTEM WITH INPUT TIME DELAY

This paper proposes a new digital redesign method for determining the hybrid controller of a continuous-time system with input time delay using an observer-based digital controller. The proposed method together with the genetic algorithms is used to determine: (1) the interval digital model of a continuous-time uncertain system with input time delay, (2) the interval digital redesign control law and (3) the interval digital observer of the original continuous-time uncertain observer with input time delay. Moreover, the result is less conservative than those obtained by the existing interval methods. A discrete-time observer is built by the original continuous-time observer with input-time-delay control law and a predictor such that the estimated states of the redesigned discrete-time observer closely match those of the original continuous-time observer with input time delay not only at sampling instants but also the behavior of the system state during the sampling interval is optimal by minimizing the hybrid performance index. The digitally redesigned observer-based controller can closely match the states of the digitally redesigned uncertain sampled-data system with those of the original continuous-time observer-based controlled uncertain system with input time delay.     

Effective digital implementation of fuzzy control systems based on approximate discrete-time models

This paper addresses an effective digital implementation of fuzzy control systems via an intelligent digital redesign (IDR) approach. The purpose of IDR is to effectively convert an existing continuous-time fuzzy controller to an equivalent sampled-data fuzzy controller in the sense of the state-matching. The authors show that, under reasonable assumptions, the IDR based on the exact discrete-time models can be reduced to the IDR based on the approximate discrete-time models. The state-matching error between the closed-loop trajectories is carefully analyzed using the integral quadratic functional approach. The estimation of the state-matching error is presented using the linear matrix inequality (LMI) techniques. The problem of designing the sampled-data fuzzy controller to minimize the estimation as well as to guarantee the stability is formulated and solved as the convex optimization problem with LMI constraints. It is also shown that the resulting sampled-data fuzzy controller recovers the performance of the continuous-time fuzzy controller as the sampling period approaches zero. A numerical example is used to demonstrate the effectiveness of the proposed design technique.     

A universal digital motion controller design for servo positioning mechanisms in industrial manufacturing

A parameterized design of universal motion controller is proposed in discrete-time domain using composite nonlinear control approach for high-performance servo mechanisms in industrial automation. First, the model of servo mechanisms is converted into discrete-time state-space form, and a linear control law is designed, consisting of state feedback, reference feed-forward and disturbance compensation. Next, a nonlinear control law is constructed to smoothly modulate the closed-loop damping as the system output approaches the reference. To estimate the unmeasurable velocity and disturbance, a reduced-order extended-state observer is adopted. The final controller is a combination of the above three parts and is fully parameterized in some fundamental tuning parameters. The controller was applied to a permanent magnet synchronous motor (PMSM) drive, which usually serves as the actuator for high-performance motion control systems. After MATLAB simulation, experimental test using a digital signal processing board was conducted, to verify the effectiveness of the proposed design.     

Model-based periodic event-triggered stabilization for continuous-time stochastic nonlinear systems

In this paper, we investigate a model-based periodic event-triggered control framework for continuous-time stochastic nonlinear systems. In this framework, an auxiliary approximate discrete-time model of stochastic nonlinear systems is constructed in the controller module, which is utilized not only to design a discrete-time controller but also as a state predictor within trigger intervals. This discrete controller design approach, the strategy of state prediction, and the periodic detection strategy for the trigger rule not only provide a manner of more direct and easier implementation on the digital platform but also effectively reduce the communication load while a satisfactory control performance is maintained. Additionally, the mean-square exponentially stabilization for continuous-time stochastic nonlinear systems is achieved, in which a guideline for determining the maximum admissible sampling period is provided and the periodic event trigger rule is designed. The final numerical simulation also supports the effectiveness of our proposed framework.     

Robust fuzzy control of nonlinear systems with parametricuncertainties

Addresses the robust fuzzy control problem for nonlinear systems in the presence of parametric uncertainties. The Takagi-Sugeno (T-S) fuzzy model is adopted for fuzzy modeling of the nonlinear system. Two cases of the T-S fuzzy system with parametric uncertainties, both continuous-time and discrete-time cases are considered. In both continuous-time and discrete-time cases, sufficient conditions are derived for robust stabilization in the sense of Lyapunov asymptotic stability, for the T-S fuzzy system with parametric uncertainties. The sufficient conditions are formulated in the format of linear matrix inequalities. The T-S fuzzy model of the chaotic Lorenz system, which has complex nonlinearity, is developed as a test bed. The effectiveness of the proposed controller design methodology is finally demonstrated through numerical simulations on the chaotic Lorenz system     

Multirate Robust Digital Control for Fuzzy Systems With Periodic Lyapunov Function

Sampled-data control which is capable of stabilizing general nonlinear systems is of great current interest. In this paper, the Takagi–Sugeno (TS) fuzzy model is used to represent the nonlinear plant. The paper is primarily concerned with designing digital controllers for the TS fuzzy continuous-time model to stabilize the closed-loop system. In the problem formulation, we only assume that the sampled values at a sampling rate of$(1/T_s)$are available for control. Within the sampling intervals, the fuzzy controller uses the sampled data at the sampling instants to fire a fuzzy rule and generate a digital control action series. This digital control action is then fed into the nonlinear system through a zero-order-holder. In this paper, two kinds of digital controllers are designed: Multirate and single-rate digital controllers. Within a sampling interval, the single-rate controller is static, while the multirate controller is periodically time-varying, i.e., the control action is switched at a small switching period$T$. Clearly, for the single-rate case, this switching period$T$is equal to the sampling period$T_s$. This paper presents a design procedure for the multirate fuzzy controller with the single-rate control as a special case. The results are formulated as linear matrix inequalities. Numerical example shows the effectiveness of the proposed design procedures.     

输出时滞双率采样系统的鲁棒迭代学习控制

针对一类带有输出时滞的单输入单输出双率采样系统,提出一种鲁棒迭代学习控制算法.首先,利用提升技术将带有输出时滞的双率采样系统转化为无时滞形式的慢速率采样的状态空间模型,并基于二维(2D)系统理论,将迭代学习控制过程转化为等价2D模型;然后利用线性矩阵不等式(LMI)技术,给出确保系统稳定的充分条件和鲁棒控制器设计方法;最后,通过3层液位贮槽系统的液位控制仿真验证所提出方法的可行性和有效性.     

Design and analysis of optimal controller for fuzzy systems with input constraint

In this paper, we present a design method of the optimal and robust controller subject to the constraint on control inputs for continuous-time Takagi-Sugeno (TS) fuzzy systems. In order to establish this design method, we consider an optimal and robust control problem for nonlinear dynamic systems. For this problem, we present an analytic way which can provide the optimal controller for nonlinear dynamic systems by the dynamic programming approach and the inverse optimal approach. Moreover, we analyze the robustness property of the proposed optimal controller with respect to a class of input uncertainties by the passivity approach. Then, based on the theoretical results presented in this paper, we formulate the design problem of the optimal and robust controller with input constraint for continuous-time TS fuzzy systems as the semidefinite programming problem, and find the controller by solving it. The usefulness of the proposed approach is illustrated by considering the three-axis attitude stabilization problem of rigid spacecraft.     

Discretization and event triggered digital output feedback control of LPV systems

This paper investigates the problem of discretization and digital output feedback control design for continuous-time linear parameter-varying (LPV) systems subject to a time-varying networked-induced delay. The proposed discretization procedure converts a continuous-time LPV system into an equivalent discrete-time LPV system based on an extension of the Taylor series expansion and using an event-based sampling. The scheduling parameters are continuously measured and modeled as piecewise constant. A new transmission of the measured output to the controller is triggered by significant changes in the parameters, yielding time-varying transmission intervals. The obtained discretized model has matrices with polynomial dependence on the time-varying parameters and an additive norm-bounded term representing the discretization residual error. A two step strategy based on linear matrix inequality conditions is then proposed to synthesize a digital static scheduled output feedback control law that stabilizes both the discretized and the LPV model. The conditions can also be used to provide robust (i.e., independent of the scheduling parameter) static output feedback controllers. The viability of the proposed design method is illustrated through numerical examples.     

A review of: “Actes du Colloque ELABORATION ET JUSTIFICATION DES MODELES”, edited by P. Delattre and M. Thellier. Maloine S.A., Paris, 1979, 2 volumes, 748 pp.

This paper utilizes trapezoidal rule together with Genetic Algorithm (GA) to convert a continuous-time system with input and state delays to an equivalent discrete-time one. A new method has been proposed to construct the hybrid control of sampled-data system with state and input delays via digital redesign which transforms the control law of a continuous-time system with state and input delays into an equivalent one of a sampled-data system so that the states of the digitally controlled sampled-data system closely match those of the originally well-designed continuous-time system for a relatively longer sampling period. An example is given to demonstrate that the proposed digital redesign is superior to the existing ones under a longer sampling period.     

The equivalent discrete-time optimal control problem for time-varying continuous-time systems with white stochastic parameters

The transformation into discrete-time equivalents of digital optimal control problems, involving continuous-time linear systems with white stochastic parameters, and quadratic integral criteria, is considered. The system parameters have time-varying statistics. The observations available at the sampling instants are in general nonlinear and corrupted by discrete-time noise. The equivalent discrete-time system has white stochastic parameters. Expressions are derived for the first and second moment of these parameters and for the parameters of the equivalent discrete-time sum criterion, which are explicit in the parameters and statistics of the original digital optimal control problem. A numerical algorithm to compute these expressions is presented. For each sampling interval, the algorithm computes the expressions recursively, forward in time, using successive equidistant evaluations of the matrices which determine the original digital optimal control problem. The algorithm is illustrated with three examples. If the observations at the sampling instants are linear and corrupted by multiplicative and/or additive discrete-time white noise, then, using recent results, full and reduced-order controllers that solve the equivalent discrete-time optimal control problem can be computed.     

State-space forms for higher-order discrete-time models

This paper presents a fundamental study of the connection between continuous- and discrete-time systems. Provided is a definition for discrete-time models, that is discrete-time systems with a continuous-time counterpart, whose order can be higher than that of the continuous-time system. This definition is based on a comparison in a certain sense on the time responses of continuous- and discrete-time systems. A theorem is presented for relating the higher-order discrete-time models to their continuous-time counterparts, which is an extension of a previous theorem for models with order equal to that of the continuous-time system. State-space forms are derived for models obtained through the use of a certain class of hold elements and through the use of mapping models, and these discrete-time systems are shown to be valid according to the definition. Special cases are models obtained using first-order and slewer hold devices, whose convergence to a continuous-time counterpart has not been shown mathematically before, and mapping models corresponding to two-step linear multi-step methods, which have not previously presented in the state-space form. The derived state-space forms provide a convenient way to implement these models for purposes of analysis, design, and implementation of discrete-time systems and finds applications in such areas as digital signal processing, digital simulation, and digital control.     

GMV control of non-linear continuous-time systems including common delays and state-space models

A non-linear generalized minimum variance control law is proposed for the control of non-linear continuous-time multivariable systems with common delays on input and output channels. The quadratic cost index involves both error and control signal costing terms. The solution for the control law is obtained using a non-linear operator representation of the plant and a linear state-equation model for the disturbance and reference models. The reference and disturbance models are represented by linear subsystems. However, the plant model can be in a very general non-linear operator form, which could involve state-space, transfer operators or non-linear function look up tables. The structure of the system and criterion is chosen so that a simple controller structure and solution is obtained. The controller obtained is simple to implement, particularly in one form, which might be considered to be a state-space version of a non-linear Smith predictor. The results are related to those for discrete-time systems but the presence of the transport delay terms complicates the solution rather more in the continuous-time case.     

Takagi-Sugeno模糊连续系统的模糊采样控制

研究了T-S模糊连续系统的模糊采样控制问题．利用广义系统的描述方法、Lyapunov-Krasovikii泛函以及线性矩阵不等式(LMI)方法,建立了LMIs形式的依赖于采样时间间隔的模糊采样镇定条件,同时给出了模糊采样控制律的设计方法．所设计的模糊采样控制律可以镇定T-S模糊系统．而且,当连续时间模糊控制律可以镇定T-S模糊系统时,对于足够小的采样时间间隔,带有同样增益矩阵的模糊采样控制律也可以镇定T-S模糊系统．最后,通过两个仿真实例说明了所给方法的有效性．     

<Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> digital redesign for LTI systems

The so-called digital redesign (DR) is a sampled-data (SD) controller design method where an analogue controller is designed firstly, and then transformed to an approximately equivalent digital controller in the sense of state-matching. In this approach, the SD controller is designed by reducing the discrepancy between the discrete-time (DT) counterpart of the closed-loop SD control system and the continuous-time (CT) closed-loop system. In this paper, we develop a DR strategy for CT linear time-invariant systems. More specifically, H _∞ norm of the error dynamic system between the CT and DT plants is minimized for the optimal state-matching performance at every sampling point. The design problem is formulated as linear matrix inequalities which can be efficiently solved by using convex optimization techniques. Finally, an example is given to illustrate the effectiveness of the proposed method.     

Fuzzy-chaos hybrid controller for controlling of nonlinear systems

A novel concept for controlling of nonlinear systems using chaos and fuzzy model-based regulators is presented. In the control of such systems, we employ two phases, the first of which uses open-loop control forming a chaotic attractor or using chaotic inherent features in a system itself. Once the system states reach a predefined convex domain, open-loop control is cut off and a fuzzy model-based controller is employed under state feedback control in the second phase. The relaxed stability conditions and linear matrix inequalities (LMIs)-based design for a fuzzy regulator is introduced to construct a fuzzy attractive domain, in which a global solution is obtained so as to achieve the desired stability condition of the closed-loop system. The proposed controller architecture has been tested using three nonlinear systems: the Henon map, the Lorenz attractor, and a two-link manipulator. The simulation results show the effectiveness of the proposed controller     

Discrete-time optimal fuzzy controller design: global conceptapproach

Proposes a systematic and theoretically sound way to design a global optimal discrete-time fuzzy controller to control and stabilize a nonlinear discrete-time fuzzy system with finite or infinite horizon (time). A linear-like global system representation of a discrete-time fuzzy system is first proposed by viewing such a system in a global concept and unifying the individual matrices into synthetic matrices. Then, based on this kind of system representation, a discrete-time optimal fuzzy control law which can achieve a global minimum effect is developed theoretically. A nonlinear two-point boundary-value-problem (TPBVP) is derived as a necessary and sufficient condition for the nonlinear quadratic optimal control problem. To simplify the computation, a multi-stage decomposition of the optimization scheme is proposed, and then a segmental recursive Riccati-like equation is derived. Moreover, in the case of time-invariant fuzzy systems, we show that the optimal controller can be obtained by just solving discrete-time algebraic Riccati-like equations. Based on this, several fascinating characteristics of the resultant closed-loop fuzzy system can easily be elicited. The stability of the closed-loop fuzzy system can be ensured by the designed optimal fuzzy controller. The optimal closed-loop fuzzy system can not only be guaranteed to be exponentially stable, but also stabilized to any desired degree. Also, the total energy of system output is absolutely finite. Moreover, the resultant closed-loop fuzzy system possesses an infinite gain margin, i.e. its stability is guaranteed no matter how large the feedback gain becomes. An example is given to illustrate the proposed optimal fuzzy controller design approach and to demonstrate the proven stability properties