Discontinuous pole assignment control of uncertain systems using only input/output measurements

This paper deals with the combined use of continuous and discontinuous adaptation mechanisms in order to control uncertain linear time-invariant systems described by I/O relationships. the complexity of the combined schemes proposed in the literature up to now relies on both the complexity of the underlying linear structure, i.e. the structure of the controller designed under the assumption of perfect knowledge of the plant parameters in order to attain the prescribed control objective, and the complexity of the additional scheme which needs to be introduced in the uncertain case so as to guarantee the stability of the relevant error equation as well as the convergence of the adjusted parameters. In the present paper it is shown how, by choosing a pole assignment control objective, it is possible to conceive a new combined variable structure-adaptive control scheme which on the whole results in being simpler than those previously proposed in the literature.     

Adaptive guaranteed cost control of a class of uncertain nonlinear systems

This paper proposes an adaptive guaranteed cost control (GCC) method for a class of nonlinear systems with uncertainties, which guarantees the system cost to be bounded by some specified values while adaptively compensating for the effects of uncertainties and nonlinearities in the system dynamics. By Lyapunov's theory and linear matrix inequality techniques, a parameter adaptation law and a sufficient condition for GCC design are derived for a class of uncertain nonlinear systems. Simulation results on a mechanical system with hard nonlinearity show the effectiveness of the proposed method.     

Passive adaptive control of non-linear systems

We develop a new recursive procedure for the design of adaptive controllers for non-linear systems with unknown parameters and unmeasured states. The use of novel nonlinear damping terms endows the closed-loop systems with the remarkable property of boundedness for arbitrary parametric uncertainty even when the adaptation is switched off. The combination of these nonlinear terms with interlacing functions produces ‘damped interlacing’, which results in new passivity and transient performance properties. Damped interlacing allows us to construct a full Lyapunov function that encompasses all the variables of the adaptive system.     

Decentralized adaptive backstepping control of electric power systems

In this paper, a decentralized adaptive backstepping excitation controller, tuned using a Particle Swarm Optimization technique (PSO), is designed for stability enhancement of multi-machine power systems. To achieve decentralization, each machine is modeled as an independent uncertain dynamic subsystem, where the uncertainty is a disturbance that represents the effects of the rest of the system on that particular machine. This disturbance is expressed as a polynomial function of electric power deviation, and its parameters are adapted using PSO. The proposed technique is illustrated with a two-area benchmark power system. This system exhibits inter-area oscillations which are effectively damped with the proposed decentralized controllers under severe contingencies, for which traditional power system stabilizers fail.     

Evolution of intelligent control in adaptive systems

The evolution of adaptive control systems is considered from the viewpoint of the use of informatics and artificial intelligence (AI) tools. Some general principles of intellectronics (AI tools) are discussed taking into consideration adaptive control systems with non-linear dynamics operating under conditions of uncertainty. The principles are illustrated by an analytical review of adaptive-intelligent robot control and logical-probabilistic decision rules as applied to expert systems of adaptive control. Example of the use of intellectronics in adaptive systems are given.     

Design of adaptive algorithms for the tracking of time-varying systems

The design of adaptive algorithms for the tracking of slowly time-varying systems is investigated. A criterion for measuring the tracking capability of an algorithm in this situation was introduced in an earlier work; the domain of validity of this criterion is shown to be much wider than expected. On the other hand, multistep algorithms, introduced in the Soviet literature, are generalized and systematically studied; they are shown to provide significant improvements over the classical (one-step) methods for tracking. Finally, a complete design methodology for adaptive algorithms, used on time-varying systems is given.     

A design of model reference adaptive control systems with unknown disturbances

Recently, a design of model matching and adaptive control systems under the presence of measurement noise employing a nonminimal order state observer was reported. But this method cannot be used for the case where the plant has deterministic disturbances. In this paper, we will show that the idea is also applicable to the case where the plant has both measurement noise and deterministic disturbances. For this purpose, we will employ a direct method of adaptive control where the controller parameters are estimated. © 2000 Scripta Technica, Electr Eng Jpn, 131(3): 51–57, 2000     

Asymptotic stability of a class of adaptive systems

In this short paper we give conditions for uniform asymptotic stability of the equilibrium of a class of non-linear non-homogeneous differential equations. This result is of interest in adaptive systems theory, where this strong stability allows us to establish some robustness properties.     

基于神经网络的一类非线性系统自适应滑模控制

针对可以分解为标称系统和不确定系统两部分的SISO非线性系统,提出了一种基于神经网络的自适应滑模控制方案。控制器由标称控制律和补偿控制律组成,标称控制律用来控制标称系统,补偿控制律是基于Lyapunov稳定性理论设计的自适应神经滑模控制律,用来控制不确定系统,而神经网络用来逼近系统的不确定性。理论分析和计算机仿真都证明,本文提出的控制策略不但能解决这类系统的轨迹跟踪控制问题,而且可以保证闭环系统的渐近稳定性。     

离散时间不确定系统的鲁棒稳定控制

对于线性离散时间不确定系统,基于Ｌｙａｐｕｎｏｖ稳定性理论,给出了一种鲁棒线性状态反馈控制器,并给出了算例。     

Decentralized stochastic adaptive control of interconnected systems

This paper considers the stochastic adaptive control problem for a class of large-scale systems formed by arbitrary interconnection of subsystems with unknown parameters and non-linearities. For the estimation of the unknown parameters of the local controllers, stochastic approximation algorithms are used. Conditions sufficient for global stability of the overall system are established. It is shown that the overall tracking error is bounded by a quantity depending on the size of interconnections.     

Model reference adaptive control applied to electrical machines

This contribution reports about a discrete-time robust adaptive control strategy for single-input/single-output systems with arbitrary zeros. The adaptive algorithm is mainly based on a direct model reference method. The basic idea presented here is to divide the feedback law into a direct adaptive and a fixed part. By a certain choice of the non-adaptive controller parameters the robustness of the adaptive control loop with respect to unmodelled dynamics of the plant may be increased. The non-adaptive part of the controller can be reformulated as a bypass to the plant and will be denoted as the ‘correction network’ owing to its action on the open-loop zeros of the augmented plant. The new adaptive control strategy removes the major drawbacks of model reference control and is investigated for speed control of a DC motor and voltage control of a synchronous generator.     

Speed gradient adaptive control algorithms for mechanical systems

A number of recent and some new adaptive control algorithms for robot manipulators are considered from the viewpoint of the speed gradient method. Various types of algorithms are considered in a unified framework that allows useful comparisons to be made.     

一类不确定系统的脉冲控制

关于线性不确定系统的鲁棒性能分析与控制,已有结果是基于连续系统,采用状态反馈的方法实现的,本文采用脉冲控制来镇定一类线性不确定系统,获得了脉冲控制下新的鲁棒稳定性判据,并通过实例进行了仿真,仿真结果表明,采用脉冲控制具有容易实现,能耗小的优点。     

Adaptive decoupling control of mimo systems

This paper describes a new adaptive controller for MIMO systems. The resulting closed-loop system will be stable and decoupled. The system to be controlled can be linear, non-minimum phase and/or unstable. The interactor matrix need not be known beforehand. Instead of employing the certainty equivalence principle to design the adaptive controller, a special controller design procedure is used. By using this special scheme, the computation time required for controller design will be drastically reduced. Moreover, the boundedness of the controller parameters is automatically ensured. This is a desired property which is useful in stability analysis. The steady state performance of the controller designed by the proposed method will be the same as that of controllers designed using the certainty equivalence principle. Approximate decoupling can be achieved by underestimating the degree of the decoupling matrix without affecting the stability of the system. To achieve decoupling, we need to specify the denominator of the closed-loop transfer function matrix of the system which is assumed to be in the form T(q^?1) = t(q^?1)I where the polynomial t(q^?1) is assumed to be monic and stable. Global stability of the adaptive system is obtained for deterministic systems. A simulation example is presented to demonstrate this control scheme.     

Stability of the equilibria of adaptive systems with leakage estimator

We study the stability of the equilibria of the differential equations that describe an adaptive controller in closed loop with a linear time-invariant (LTI) undermodelled plant when the parameter update law is a leaky gradient, i.e. a s?-modified estimator. Hsu and Costa studied the full-order case and showed that under certain limiting conditions the resulting dynamic system has three, possibly unstable, equilibrium points. Here we provide a modest extension to that work by further characterizing the class of undermodelled LTI plants for which the equilibria exist and are (un)stable Interestingly enough, it is shown that the equilibria are stable iff a given compensator stabilizes the plant. This compensator is, up to the plant ‘steady state gain’, known to the designer.     

A self-coordinating adaptive control scheme for HVDC transmission systems

The transients in a HVDC system can temporarily change line currents, voltages, and power substantially from the reference steady state values and the behavior of the system depends greatly upon the parameters of the converter controller. The DC system performances are affected by the AC system characteristics such as the short circuit ratio, and the AC system inertia. A controller which has good performance for a particular operating condition may not have sufficiently good performance under another operating condition or vice versa. Performance of the controller also depends on the impedance at the rectifier and the inverter bus, which may vary according to the operating conditions of the AC network. A controller with self-coordinating adaptive capability can perform better in various situations. The proposed controller is tested at a very low SCR, the most critical value for system performance evaluation, and at the low inertia of the AC system. Simulation results are compared between proposed controller, fuzzy tuned controller, gain scheduling controller, and fixed-parameter controller. All simulations are conducted using MATLAB/SIMULINK software package.     

不确定欠驱动非线性系统的模糊滑模控制

针对一类不确定欠驱动非线性系统,提出了一种模糊滑模控制新方案．该方案在T-S型模糊控制的基础上引入滑模控制,有效避免了T-S型模糊控制可能出现的不稳定情况,同时改善了系统的动态性能。采用吊车系统进行系统仿真,结果表明了该控制方案的有效性。     

An indirect model reference adaptive controller based on the multirate sampling of the plant output

The use of sampled-data multirate-output controllers for model reference adaptive control of possibly non-stably invertible linear systems with unknown parameters is investigated. Multirate-output controllers contain a multirate sampling mechanism with different sampling period at each system output. Such a control allows us to assign an arbitrary discrete-time transfer function matrix for the sampled closed-loop system and does not make assumptions on the plant other than controllability, observability and the knowledge of two sets of structural indices, namely the controllability and the observability indices. An indirect adaptive control scheme based on these sampled-data controllers is proposed which estimates the unknown plant parameters (and consequently the controller parameters) on-line from sequential data of the inputs and the outputs of the plant, which are recursively updated within the time limit imposed by a fundamental sampling period T₀. Using the proposed adaptive algorithm, the model reference adaptive control problem is reduced to the determination of a fictitious static state feedback controller owing to the merits of multirate-output controllers. Known indirect model reference adaptive control techniques usually resort to the direct computation of dynamic controllers. The controller determination reduces to the simple problem of solving a linear algebraic system of equations, whereas in known indirect model reference adaptive control techniques, matrix polynomial Diophantine equations usually need to be solved. Moreover, persistent excitation of the continuous-time plant is provided without making any special richness assumption on the reference signals.