Adaptive Control Design Procedure Research Articles

Model reference adaptive controller with interpretable fuzzy rules for linear MIMO systems

This paper presents a method for fuzzy model reference adaptive control system design for a partially known multi-input multi-output (MIMO) linear dynamic plant. Differential or convolutional equations can describe the plant to be controlled, and the fuzzy controller is assumed to be from the MIMO sector-bounded nonstationary nonlinearities class. The task is to obtain robust adaptive stabilization. Some absolute stability theorems and integral evaluations of the state and control vectors are applied in the MIMO fuzzy adaptive controller design procedure for the first time. A method for automatically obtaining a near-optimal nonlinear dynamic reference model is described. A practical step-by-step procedure of the MIMO adaptive fuzzy controller design is proposed, resulting in highly interpretable MIMO fuzzy control rules based on triangular fuzzy sets and strong triangular fuzzy partition. An example of the fuzzy controller design according to the proposed procedure is given.

Adaptive output feedback tracking control for non‐linear switched stochastic systems with unknown control directions

The problem of adaptive fuzzy output feedback tracking control for a class of switched stochastic non-linear systems with time-varying delay and unknown control directions is addressed in this study. During the adaptive controller design procedure, a state observer is introduced to estimate the unmeasurable system states, an appropriate stochastic Lyapunov-Krasovskii functional is utilised to compensate the time-varying delay terms, and fuzzy logic systems are employed to approximate a suitable unknown function which is composed of all the unknown non-linearities of the switched systems. In addition, the Nussbaum-type function is used to detect the unknown control directions. With the help of the designed controller independent of switching signals, the semi-globally uniformly ultimately boundedness of all the closed-loop signals is guaranteed and the tracking error converges to a small neighbourhood of the origin. Finally, a simulation example is given to illustrate the effectiveness of the proposed approach.

Asymptotic Fuzzy Tracking Control for a Class of Stochastic Strict-Feedback Systems

This paper presents an asymptotic tracking control design method for stochastic strict-feedback systems via fuzzy logic systems. In the existing results, stochastic controls are usually limited to be bounded in probability. However, how to realize the asymptotic tracking control for stochastic strict-feedback systems remains a control dilemma. This paper achieves the asymptotic tracking control by proposing a novel gain suppressing inequality approach. Specifically, the three-part construction is performed to achieve such control objective for stochastic systems. First, the novel gain suppressing inequality technique is developed to lay the foundation for carrying out the Lyapunov stability analysis. Second, the developed inequality technique is integrated with each step of the backstepping-based adaptive control design procedure. Third, analyses are provided to realize the asymptotic tracking control of stochastic strict-feedback systems.

Adaptive multiple input delays compensation under input constraints applied to perimeter traffic control

Abstract: Pragmatic design method of direct model reference adaptive control (MRAC) under input constraints for a class of systems with multiple input delays based only on the lumped-delays is developed. To overcome the difficulty to directly predict the plant state, a control design is proposed relies on a decomposition of the adaptive control design procedure where a “generalized error”, and auxiliary linear Smith-like dynamic units with adjustable gains are introduced. The effect of such a decomposition is to pull the input delays out at the first step of the design procedure. Simulation results of a coupled perimeter control of two interacting aggregate urban traffic regions are presented.

Adaptive Fuzzy Output-Feedback Tracking Control for a Class of Switched Stochastic Nonlinear Time-Delay Systems

In this paper, a design scheme for an adaptive fuzzy tracking controller is proposed for a class of switched stochastic nonlinear time-delay systems via dynamic output-feedback. First, a reduced-order observer is introduced to estimate the unmeasurable states of the switched system. During the adaptive controller design procedure, an appropriate stochastic Lyapunov---Krasovskii functional deals with the time-delay terms, and fuzzy logic systems are employed to approximate the unknown nonlinearities. Based on the designed controller, the semi-globally uniform ultimate boundedness of all the closed-loop signals is guaranteed and the tracking error converges to a small neighborhood of the origin. Finally, a simulation example is given to illustrate the validity of the proposed approach.

Active identification for discrete-time nonlinear control. II. Strict-feedback systems

For Part I, see ibid., vol. 47, no. 2, pp. 210-24 (2002). Presents a novel adaptive control design procedure for discrete-time nonlinear systems that can be transformed into the parametric-strict feedback form. This procedure utilizes the tools that were introduced in Part I, namely temporal separation of the parameter estimation task from the control task, the implementation of an active identification procedure through a recursive orthogonalized projection estimator and an input selection algorithm that guarantees complete identification in finite time. However, compared to the output-feedback case presented in Part I, the active identification task is now further complicated by the fact that the nonlinearities are allowed to depend on all states, not just the output. This requires a significant modification of the input selection procedure, which utilizes all the measured states to guarantee that the identification task will be completed in less time than in the output-feedback case.

A robust adaptive nonlinear control design

An adaptive control design procedure for a class of nonlinear systems with both parametric uncertainty and unknown nonlinearities is presented. The unknown nonlinearities lie within some ‘bounding functions’, which are assumed to be partially known. The key assumption is that the uncertain terms satisfy a ‘triangularity condition’. As illustrated by examples, the proposed design procedure expands the class of nonlinear systems for which global adaptive stabilization methods can be applied. The overall adaptive scheme is shown to guarantee global uniform ultimate boundedness.

An Adaptively Controlled Electrohydraulic Servo-Mechanism: Part 2: Implementation

An adaptive controller design procedure has been applied to an electrohydraulic servo-system. Detailed accounts are provided on applying the design method and on initializing the controller free design parameters. Experimental results demonstrate the ability of the adaptive controller to maintain consistently good model following behaviour under changing operating conditions.