Model reference adaptive control for spectral systems

In this paper, we investigate a model reference adaptive control problem for the class of distributed parameter systems generated by spectral operators. First we present an equivalent static input-output representation for the spectral system using filtered values generated from the inputs and the outputs. Next, we design and analyse a model reference adaptive control system for systems with unknown input and output distribution functions.     

Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems

A novel model reference adaptive robust fuzzy control algorithm is presented for ship steering autopilot, which is an uncertain nonlinear system. In the proposed algorithm, fuzzy logic systems have been used to approximate lumped unknown function in the ship steering systems and the adaptive mechanism with minimal learning parameter, i.e. only one parameter, has been achieved by use of Lyapunov approach. The proposed methodology is verified using the simulation mode of the Dalian Maritime University's ocean-going training ship named Yulong. It is shown that the proposed algorithm guarantees that the ship steering autopilot system is asymptotically stable and its tracking error can approach to zero.     

Model reference adaptive control of drug infusion rate

The role of model reference adaptive control is under consideration for a computer based feedback system which will regulate the infusion rate of a drug (nitroprusside) in order to maintain desired blood pressure. Because the transfer function parameters are different for each patient and furthermore are variable with time, an adaptive algorithm is desirable for maintaining both steady-state and transient specifications. To this effect, computer simulation has shown that direct (or implicit) model reference procedures might be successfully applied to the control of blood pressure despite the uncertainty in the delays, time constant and gains. Additional efforts concerned with the actual demonstration of these concepts on dogs have further supported the role of adaptive control for blood pressure regulation.     

Application of a robust model reference adaptive control algorithm to a nonlinear automotive actuator

Model reference adaptive control is a viable control method to impose the demanded dynamics on plants whose parameters are affected by large uncertainty. In this paper, we show by means of experiments that robust adaptive methods can effectively face nonlinearities that are common to many automotive electromechanical devices. We consider here, as a representative case study, the control of a strongly nonlinear automotive actuator. The experimental results confirm the effectiveness of the method to cope with unmodeled nonlinear terms and unknown parameters. In addition, the engineering performance indexes computed on experimental data clearly show that the robust adaptive strategy provides better performance compared with those given by a classical model-based control solution with fixed gains.     

Model reference adaptive control system of a catalytic fluidized bed reactor

This paper presents a study of the control of a fluidized bed catalytic reactor using the model reference adaptive control algorithm with independent tracking and regulation objectives as presented by Landau and Lozano (1981). The purpose of the control is to maintain the temperature of the reactor as near as possible to a desired set value (490°C). Some of the heat produced by the exothermal reaction taking place between propylene, ammonia and oxygen is carried away by a ventilator. The control action is based on a low order mathematical model with time varying parameters. This method of control results in a significantly improved control for both servo and regulatory control.     

Model reference adaptive control of a solenoid valve controlled hydraulic system

The MRAC is applied to the control of an electrohydraulic system controlled by a solenoid valve. The adaptive algorithms proposed in this paper guarantee the asymptotic stability of the error between the plant output and that of the reference model. The robustness properties of the adaptive control system are examined based on the concept of a tuned system—an ideal converged (non-adaptive) closed-loop system when the gain of the valve is varying non-linearly. A modified adaptive law that guarantees the tracking error will be bounded in a residual region is proposed. The performance improvement is also studied by implementing the ternary mode solenoid valve. To illustrate the validity, the simulation results of a gun turret position control system are also included.     

Model reference adaptive control algorithms for industrial robots

In this paper some problems concerning the control of multifunctional manipulators (industrial robots) with high speed continuous movements are investigated. Although deterministic approaches to the control of robots, whose model are highly interconnected and non-linear, are known alternative approaches based on the Model Reference Adaptive System (MRAS) method of control are possible and useful. In the paper it is proved that a generalized MRAS control assures the convergence to a suitable reference model for a class of processes: the manipulator is shown to belong to such a class. The paper is completed by some applications evaluated by simulation.     

一种具有强鲁棒性的模型参考自适应控制系统

提出一种模型参考自适应控制系统．该系统只要求知道对象阶次的上界,不要求对象严格正实和最小相位,并且允许存在建模误差和作用在对象任何部位的大幅值高频干扰．系统的强鲁棒性来源于特殊的系统结构和对信号的良好滤波．Lyapunov稳定性理论证明了系统的渐近稳定性,Matlab仿真验证了系统的强鲁棒性．     

基于强化学习的模型参考自适应控制

提出了一种基于强化学习的模型参考自适应控制方法,控制器采用自适应启发评价算法,它由两部分组成:自适应评价单元及联想搜索单元.由参考模型给出系统的性能指标,利用系统反馈的强化信号在线更新控制器的参数.仿真结果表明:基于强化学习的模型参考自适应控制方法可以实现对一类复杂的非线性系统的稳定控制和鲁棒控制,该控制方法不仅响应速度快,而且具有较高的学习速率,实时性较强.     

Model reference adaptive control for nonminimum phase systems

In this paper we define a new model reference adaptive control problem which allows a large amount of plant uncertainty. Instead of the usual goal of asymptotic error regulation, here we require that the limit superior of the magnitude of the error be less than a predefined constant times the limit superior of the magnitude of the external reference input. This provides a natural way to incorporate uncertainty which includes nonminimum phase systems.     

Model reference adaptive control using the delta operator

Reference is made to a recent paper by G.C. Goodwin, R. Lozano Leal, D.Q. Mayne, and R.H. Middleton (Automatica, vol.22, p.199-207, 1986), where it is shown that a cancellation of nonminimum-phase zeros appearing in discrete-time model reference adaptive control (MRAC) at a fast sampling rate can be easily avoided if the model to be identified is expressed in terms of the delta operator. A new proof of stability of the MRAC is given using the delta operator which, in contrast to the proof given in the above paper, does not require an assumption that a system consisting of a model determined by estimated parameters and a control law based on certainty equivalence principle is exponentially stable. A simple parameterization is proposed for the discrete-time MRAC using the delta operator which allows application of the usual (i.e. without dead zone) estimation algorithms     

Model reference adaptive control of time varying and stochasticsystems

The adaptive control of general delay, linear time-varying systems is addressed. A simple model reference adaptive control law is devised. It does not require a priori knowledge of the sign of the high-frequency gain. This control law coupled with the parameter estimation equations allows a simple representation of the closed-loop system. This greatly eases the stability and performance analysis, and has potential for generalization to adaptive pole placement and other control laws suitable for nonminimum-phase systems. For the Kalman filter parameter estimator coupled with this control law, stability is obtained without persistence of excitation or knowledge of the sign of the high frequency gain. Performance results for the extended-least-squares algorithm are described     

Dynamical properties of an adaptive control system with a nonlinear reference model. I

We show the results of solving the problem of constructing an adaptive control system with nonlinear reference model for a nonstationary dynamical object with a nonlinear actuator. To analyze the dynamical properties of a nonlinear nonstationary control system we use the method of pointwise transformations. We show the results of analysis and mathematical modeling of the considered control system with the adaptation circuit switched off.     

Dynamical properties of an adaptive control system with a nonlinear reference model. II

We consider the problem of constructing an adaptive control system with nonlinear reference model for a low order nonstationary dynamical object with a nonlinear actuator. We show the structure, analysis, and mathematical modeling results for the adaptive control system.     

Model reference tracking control of an aircraft: a robust adaptive approach

This work presents the design and the corresponding analysis of a nonlinear robust adaptive controller for model reference tracking of an aircraft that has parametric uncertainties in its system matrices and additive state- and/or time-dependent nonlinear disturbance-like terms in its dynamics. Specifically, robust integral of the sign of the error feedback term and an adaptive term is fused with a proportional integral controller. Lyapunov-based stability analysis techniques are utilised to prove global asymptotic convergence of the output tracking error. Extensive numerical simulations are presented to illustrate the performance of the proposed robust adaptive controller.     

一类组合系统的模型参考自适应分散控制

针对一类组合系统的模型参考自适应分散控制问题,提出一种新的考虑问题的思路.在选择参考模型时,根据系统所期望的性能指标,考虑各子系统间的相互作用,保留互联项;基于Lyapunov稳定性理论,给出了模型参考自适应控制方案,并证明了其整体一致收敛性.     

Model reference adaptive control for plants with unknown relativedegree

A parameterization scheme for model reference control which guarantees stable model-plant transfer function matching is presented. This scheme has a linear dynamic feedforward block whose numerator and denominator should be stable polynomials for closed-loop stability. An adaptive law is designed to update the controller parameters in the feedback loop and the coefficients of the numerator of the feedforward block as well as some additional parameters. Since, for stability, the inverse of the polynomial differential operator defined by some of the estimated parameters has to be stable, the adaptive law is modified using a projection technique. The stability analysis of the model reference adaptive control (MRAC) scheme is given. The robustness of the MRAC scheme with respect to unmodeled dynamics is established     

神经网络非线性系统模型参考自适应控制器统一设计法

针对一类控制器无论是否具有可分离结构的非线性系统 ,利用内模控制的思想提出一种统一的神经网络模型参考自适应控制器设计方案 ,简化了基于神经网络的模型参考自适应控制系统的设计。给出了统一的设计步骤 ,它适用于任意非线性系统 ,更接近于工程实际。理论分析和仿真结果证明了该方案的合理性和有效性     

EMS型磁悬浮列车模块悬浮系统的模型参考自适应控制

模块是EMS型磁悬浮列车的基本功能单元,常用的悬浮控制方法是将其两端等效为两个完全独立的对象分别设计控制器。实际上模块两端的运动状态是互相耦合的,独立设计的控制器很难解决彼此之间的状态耦合。本文针对这种耦合情况,将模块视为一个刚体对象,采用反馈线性化方法实现模块运动的电气解耦,并针对模型的不确定性设计了模块悬浮系统的模型参考自适应控制器。仿真结果表明,该自适应控制器有效地解决耦合问题,提高悬浮系统的性能。