分散混杂自适应模糊H∞控制器及其在自动化公路系统中的应用

本文针对一类SISO不确定非线性大系统,提出了一种混杂间接和直接自适应分散模糊H∞控制器.通过组合模糊系统和H∞跟踪技术开发的分散自适应模糊控制算法避免了控制设计中含有的符号函数.两种自适应模糊控制器的组合消除了它们各自均不能够同时融合被控对象知识与控制知识的局限.闭环大系统被证明是稳定的,且具有H∞跟踪性能.该算法应用于自动化公路系统中车辆的纵向跟随控制,仿真结果表明混杂自适应模糊H∞控制系统的跟踪性能更好而相应的控制幅值却更小.     

Decentralised output-feedback control design for switched fuzzy large-scale systems

This paper investigates the output-feedback control design problem for a class of switched Takagi–Sugeno fuzzy large-scale systems with non-measurable premise variables. The considered fuzzy large-scale systems consist of several interconnected subsystems with different switching modes and there exists the asynchronous switching between the system switching modes and the controller switching modes. A decentralised state observer is proposed to estimate the unmeasured states, and a new output-feedback decentralised control scheme is developed by using the state observers and the switching functions. Based on the theory of Lyapunov stability and average dwell-time methods, the sufficient conditions of ensuring the switched control system stability are proposed and proved, which are formulated in the form of linear matrix inequalities. An applicable example is provided to show the effectiveness of the obtained theoretical results.     

关联模糊大系统的保性能控制器设计: LM I 方法

考虑具有参数不确定性的连续时间关联模糊大系统的保性能控制问题.基于LMI方法,给出了该不确定性模糊大系统分散状态反馈保性能控制器的设计方案.所设计的分散状态反馈保性能控制器,不但使得该不确定性模糊大系统闭环渐近稳定,而且使其二次型性能指标的上界最小.     

一类大系统的分散自适应模糊滑模控制

研究了一类具有未知函数控制增益的非线性大系统的分散模糊控制问题.基于滑模 控制原理和模糊集理论,提出了一种分散自适应模糊控制器的设计方法.通过理论分析,证明 了分散自适应模糊控制系统是全局稳定的,跟踪误差可收敛到零的一个领域内.     

Expert supervision and control of a large-scale plant

The process industry and the cement industry in particular, is rapidly realising the importance of expert systems for the control of large-scale production processes which have hitherto defied solution by conventional methods. Where the operational environment of a process industry is subject to vagueness and uncertainty, then expert control offers new opportunities for increased production, fuel economy, and enhanced product quality.This paper outlines a large-scale expert supervision and control system which was developed as part of a long-term project to apply advanced concepts of CIM to a cement production plant. The system comprises a cluster of nine expert subsystems using fuzzy logic, four of which are arranged in a multilayer architecture to synergistically control a rotary kiln and cooler complex. Experience with the real-time expert system since 1985, when it was first commissioned, has resulted in increases of the order of 4–5% in productivity and energy reduction as well as reduced plant maintenance over conventional manual control.     

Adaptive fuzzy decentralised control for stochastic nonlinear large-scale systems in pure-feedback form

This paper is concerned with the problem of adaptive fuzzy decentralised output-feedback control for a class of uncertain stochastic nonlinear pure-feedback large-scale systems with completely unknown functions, the mismatched interconnections and without requiring the states being available for controller design. With the help of fuzzy logic systems approximating the unknown nonlinear functions, a fuzzy state observer is designed estimating the unmeasured states. Therefore, the nonlinear filtered signals are incorporated into the backstepping recursive design, and an adaptive fuzzy decentralised output-feedback control scheme is developed. It is proved that the filter system converges to a small neighbourhood of the origin based on appropriate choice of the design parameters. Simulation studies are included illustrating the effectiveness of the proposed approach.     

不确定非线性系统的模糊鲁棒??∞ 跟踪控制

针对一类不确定非线性系统,提出一种新的模糊鲁棒H∞跟踪控制方案。应用模糊T-S模型表征非线性系统,系统不确定性通过模糊逻辑系统消除。由线性矩阵不等式和自适应律给出了模糊控制器存在的一个充分条件。基于Lyapunov稳定性理论,模糊控制方案在所有闭环信号最终一致有界下保证了期望的H∞跟踪性能。两连杆机械臂的仿真结果表明了该方案的可行性。     

Decentralized adaptive tracking control of nonaffine nonlinear large-scale systems with time delays

This paper addresses the problem of decentralized tracking control of large-scale systems with uncertain nonaffine nonlinear isolated subsystems and nonlinear interconnections with time-varying delays. Based on Lyapunov-Krasovskii functional approach and implicit function theorem, a delay-independent decentralized tracking controller is proposed. Due to functional approximation capability of fuzzy logic systems (FLS), neither strict structure restrictions on the isolated subsystems nor a priori knowledge of the strong interconnections with time-varying delays is required in our control design. Furthermore, transient performance of the resulting closed-loop system is also addressed under an analytical framework. Finally, two numerical examples are provided to show the effectiveness of the proposed controller.     

Decentralised direct adaptive output feedback fuzzy controller for a class of large-scale nonaffine nonlinear systems and its applications

A novel cooperation-based decentralised direct adaptive fuzzy control via output feedback is developed for a class of large-scale nonaffine uncertain nonlinear systems using a direct adaptive fuzzy approach in this article. Under assumption that all the controllers share their prior information about the subsystem reference models, the interconnections between subsystems are relaxed to arbitrarily strong nonlinearities without matching conditions. The assumption on input gains is extended from typical positive constants to highly nonlinear functions. The feedback and adaptation mechanisms require neither the typical observation error filtering nor the famous strictly positive-real condition. Based on Lyapunov's direct method, the tracking errors of the closed-loop large-scale system are guaranteed to converge to tunable neighbourhoods of the origin. The proposed algorithm is applied to controlling two mechanical large-scale systems and simulation results substantiate its effectiveness.     

Fuzzy control of complex systems

 One of the biggest challenges of any control paradigm is being able to handle large complex systems. A system may be called large-scale or complex, here, if its dimension (order) is so high and its model (if available) is nonlinear, interconnected with uncertain information flow such that classical techniques of control theory cannot easily handle the system. From a control theoretical point of view, fuzzy logic has been intermixed with all the important aspects of systems theory - modeling, identification, analysis, stability, synthesis, filtering, and estimation. However, the application of fuzzy control to large-scale complex systems is not a trivial task by any means. For such systems the size of the rule base in a typical fuzzy control architecture will be nearly infinite. In this paper an attempt is made to break some new ground on the applications of fuzzy control to complex systems. A new rule base reduction approach is suggested to manage large inference engines. Notions of rule hierarchy and sensor data fusion are introduced and combined to achieve system’s goals. The technique has been implemented on an SGS Thomson W.A.R.P. chip for an inverted pendulum with wine-balancing application.     

Adaptive fuzzy decentralised fault-tolerant control for nonlinear large-scale systems with actuator failures and unmodelled dynamics

This paper discusses the adaptive fuzzy decentralised fault-tolerant control (FTC) problem for a class of nonlinear large-scale systems in strict-feedback form. The systems under study contain the unknown nonlinearities, unmodelled dynamics, actuator faults and without the direct measurements of state variables. With the help of fuzzy logic systems identifying the unknown functions and a fuzzy adaptive observer is designed to estimate the unmeasured states. By using the backstepping design technique and the dynamic surface control approach and combining with the changing supply function technique, a fuzzy adaptive FTC scheme is developed. The main features of the proposed control approach are that it can guarantee the closed-loop system to be input–to-state practically stable, and also has the robustness to the unmodelled dynamics. Moreover, it can overcome the so-called problem of ‘explosion of complexity’ existing in the previous literature. Finally, simulation studies are provided to illustrate the effectiveness of the proposed approach.     

不确定时滞关联大系统的全局稳定模糊容错控制

研究了一类带有时变时滞的不确定非线性关联大系统的自适应模糊容错控制问题.用有界的参考信号代换模糊逼近器输入中的未知时滞信号,使得控制器的设计与应用不再依赖于时滞假设条件,使得控制器的设计和控制方法的应用更为方便.容错反推控制技术和自适应技术相结合来处理代换误差和逼近误差.所提出的方案能有效补偿所有4种类型的执行器故障,同时还可保证闭环系统的全局稳定性.仿真结果进一步验证了本文方法的有效性.     

Adaptive fuzzy decentralized control for nonlinear large-scale systems based on high-gain observer

An adaptive fuzzy decentralized backstepping output-feedback control approach is proposed for a class of nonlinear large-scale systems with completely unknown functions,the interconnections mismatched in control inputs,and without the measurements of the states.Fuzzy logic systems are employed to approximate the unknown nonlinear functions,and an adaptive high-gain observer is developed to estimate the unmeasured states.Using the designed high-gain observer,and combining the fuzzy adaptive control theory with backstepping approach,an adaptive fuzzy decentralized backstepping output-feedback control scheme is developed.It is proved that the proposed control approach can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded(SUUB),and that the observer errors and the tracking errors converge to a small neighborhood of the origin by appropriate choice of the design parameters.Finally,a simulation example is provided to show the eectiveness of the proposed approach.     

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．     

Fuzzy hypercubes: Linguistic learning/reasoning systems for intelligent control and identification

This paper introduces a new tool for intelligent control and identification. A robust and reliable learning and reasoning mechanism is addressed based upon fuzzy set theory and fuzzy associative memories. The mechanism storesa priori an initial knowledge base via approximate learning and utilizes this information for identification and control via fuzzy inferencing. This architecture parallels a well-known scheme in which memory implicative rules are stored disjunctively. We call this process afuzzy hypercube. Fuzzy hypercubes can be applied to a class of complex and highly nonlinear systems which suffer from vagueness uncertainty and incomplete information such as fuzziness and ignorance. Evidential aspects of a fuzzy hypercube are treated to assess the degree of certainty or reliability. The implementation issue using fuzzy hypercubes is raised, and finally, a fuzzy hypercube is applied to fuzzy linguistic control.     

The simplest fuzzy controllers using different inference methods are different nonlinear proportional-integral controllers with variable gains

The author analytically proves that the simplest fuzzy controllers using different inference methods are different nonlinear proportional-integral (PI) controllers with proportional-gains and integral-gains changing with inputs of the controllers. The inference methods involved are Mamdani's minimum inference method, Larsen's product inference method, the drastic product inference method and the bounded product inference method. Configuration of the fuzzy controllers is minimal, which includes two input fuzzy sets, three output fuzzy sets, four control rules, Zadeh fuzzy logic AND, Lukasiewicz fuzzy logic OR and a center of gravity defuzzification algorithm. After analytically investigating properties of the nonlinear PI controllers, the author reveals that the bounded product inference method is inappropriate for the control purpose while the other three inference methods are appropriate. Dynamic and static control behaviors of the fuzzy controllers with the appropriate inference methods are analytically compared with each other, and are also compared with those of the linear PI controller. Finally, it is analytically proven that the fuzzy control systems have the same local stability at the equilibrium point as the corresponding linear PI control system does.     

Adaptive fuzzy output-feedback fault-tolerant tracking control of a class of uncertain nonlinear switched systems

ABSTRACT

This paper considers the output-feedback fault-tolerant tracking control problem for a class of uncertain nonlinear switched systems with nonlinear faults and strict-feedback form, where the faults which are nonaffine occur on the actuator. As a kind of specialised function approximating tool, fuzzy logic systems (FLSs), are employed to approximate the unknown smooth nonlinear functions. A switched fuzzy observer is designed to address the problem of unmeasurable states, filtered signals are used to address algebraic loop problem and the average dwell time (ADT) method is further utilised to prove the stability of the resulting closed-loop systems under a type of slowly switching signals. Based on the backstepping recursive design technique and Lyapunov function method, an adaptive fuzzy output-feedback control scheme is developed. The developed control method can ensure all the signals are semi-globally uniformly ultimately bounded (SGUUB) and the system output tracks the reference signal tightly even if unknown fault occurs. A simulation carried on an example demonstrates the validity of the obtained control scheme.     

基于改进量子粒子群算法的NCS模糊控制器参数优化

针对网络化控制系统中模糊控制器的量化因子和比例因子采用传统经验方法难以整定的问题, 提出了一种改进量子粒子群（IQPSO）算法对模糊控制器量化因子和比例因子进行优化。该方法将ABC算法中的搜索算子作为变异算子引入到QPSO算法中, 使得IQPSO算法较好地克服了QPSO算法保持种群多样性差容易早熟收敛的缺陷, 并以ITAE指标作为IQPSO算法的适应度函数对模糊控制器进行优化。典型工业过程仿真结果表明, IQPSO优化的模糊控制器具有比PID控制器和标准QPSO优化的模糊控制器更好的控制性能和适用性。     

Direct adaptive fuzzy control of a class of MIMO non-affine nonlinear systems

An adaptive fuzzy control approach is proposed for a class of multiple-input–multiple-output (MIMO) nonlinear systems with completely unknown non-affine functions. The global implicit function theorem is first used to prove the existence of an unknown ideal implicit controller that can achieve the control objectives. Within this scheme, fuzzy systems are employed the approximate the unknown ideal implicit controller, and robustifying control terms are used to compensate the approximation errors and external disturbances. The adjustable parameters of the used fuzzy systems are deduced from the stability analysis of the closed-loop system in the sense of Lyapunov. To show the efficiency of the proposed controllers, two simulation examples are presented.     

一类非线性系统的自适应滑模模糊控制

针对一类具有多个子系统的欠驱动非线性系统提出了一种自适应滑模模糊控制方法. 首先通过分析模糊控制与边界层滑模控制的相似性,提出了滑模模糊控制方法;然后根据滑模 面斜率和各子系统控制对于系统动态性能的影响,分别采用模糊推理根据系统状态自动地实时 调节滑模面斜率和各子系统在系统控制中的作用;最后通过简单的滑模模糊控制器实现对具有 多个子系统的欠驱动非线性系统的控制.将该方法应用于吊车的运输控制中,仿真结果证明了 其有效性和鲁棒性.