Imperfect model reference adaptive control: deadbeat output error approach

It is well known that while the perfect model matching condition (i.e. unstable plant zeros must be zeros of the reference model) is not met, the model reference adaptive control cannot easily be implemented. In adaptive control systems, since the plant is assumed to be unknown previously, it is a difficult task to choose an adequate reference model such that the perfect model matching condition is guaranteed in every adaptive step. In this paper, a new design algorithm for model reference adaptive control systems is proposed to synthesize an adaptive controller such that the error between the reference model output and the plant output can vanish in a deadbeat manner. In this situation, the stringent matching condition can be relaxed in every adaptive step, so our design algorithm is also suitable for unstable or non-minimum phase systems. Several simulation results are presented to illustrate the good behaviour of our design algorithm.     

基于滑模模型参考自适应观测器的无速度传感器三相永磁同步电机模型预测转矩控制

针对三相永磁同步电机(PMSM)驱动系统,基于滑模变结构模型参考自适应(MRAS)技术,提出了一种新颖的无速度传感器模型预测转矩控制(MPTC)策略.采用滑模变结构模型参考自适应方法构造电机转速观测器,以改善速度估计精度并提高系统鲁棒性;利用模型预测转矩控制策略,以达到减小转矩和磁链纹波并提高系统控制性能的目的.仿真结果表明:就滑模MRAS观测器与MRAS观测器比较而言,基于前者的PMSM无速度传感器MPTC系统比基于后者的PMSM无速度传感器MPTC系统具有较强的鲁棒性和更好的动态性能;就MPTC与直接转矩控制(DTC)和磁场定向控制(FOC)比较而言,采用前者策略的无速度传感器电机驱动系统能够降低逆变器开关频率、减少相电流总谐波失真(THD),从而提高系统可靠性.     

Discrete-time model reference adaptive control for continuous-timesystems using generalized sampled-data hold functions

The generalized sampled-data hold functions approach of P.T. Kabamba (1987) is extended to the control of linear time-invariant systems with unknown parameters. The idea of Kabamba's approach is to periodically sample the plant output and define the control as the sampled output plus a discrete-time reference, each multiplied by an individual modulating function. Such a control makes it possible to assign an arbitrary discrete-time transfer function for the sampled closed-loop system and does not make assumptions on the plant other than controllability and observability. The authors propose an indirect adaptive controller which is based on this approach and estimates the modulating functions online. In particular, the control is modified so that persistent excitation of the continuous-time plant is ensured without making an assumption on the reference signal, and discrete-time asymptotic model-following is nevertheless obtained. The only assumptions on the plant are minimality, for the continuous and sampled plant, and known order     

Direct adaptive control using an adaptive reference model

Direct model reference adaptive control is considered when the plant-model matching conditions are violated due to large changes in the plant or incorrect knowledge of the plant's mathematical structure. Because of the mismatch, the plant can no longer track the original reference model, but may be able to track a modified reference model that still provides satisfactory performance. The proposed approach uses a time-varying ‘adaptive’ reference model that reflects the achievable performance of the changed plant. The approach consists of direct adaptation of state feedback gains for state tracking and simultaneous estimation of the plant-model mismatch. The reference model adapts to the changed plant, and is redesigned if the estimated plant-model mismatch exceeds a bound determined via robust stability and/or performance criteria. The resulting controller offers asymptotic state tracking in the presence of plant-model mismatch as well as matched parameter deviations.     

Neural network-based model reference adaptive control system

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.     

Discrete-time model for an adaptive optics system with input delay

The standard adaptive optics (AO) system can be viewed as a sampled-data feedback system with a continuous-time disturbance (the incident wavefront from the observed object) and discrete-time measurement noise. A common measure of performance of AO systems is the time average of the pupil variance of the residual wavefront. This performance can be related to that of a discrete-time system obtained by lifting the incident and residual wavefronts. This article derives the corresponding discrete-time model and the computation of the AO system residual variance based on that model. The predicted variance of a single mode of an AO system is shown to be the same as that obtained via simulation.     

Intelligent control of a stepping motor drive using an adaptive neuro–fuzzy inference system

Stepping motors are widely used in robotics and in the numerical control of machine tools where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and a closed-loop control becomes necessary. In this paper, fuzzy logic is applied to control the speed of a stepping motor drive with feedback. A neuro-fuzzy hybrid approach is used to design the fuzzy rule base of the intelligent system for control. In particular, we used the ANFIS methodology to build a Sugeno fuzzy model for controlling the stepping motor drive. An advanced test bed is used in order to evaluate the tracking properties and the robustness capacities of the fuzzy logic controller.     

Fuzzy model reference adaptive control

This paper investigates a fuzzy model reference adaptive controller (FMRAC) for continuous-time multiple-input-multiple-output (MIMO) nonlinear systems. The proposed adaptive scheme uses a Takagi-Seguno (TS) fuzzy adaptive system, which allows for the inclusion of a priori information in terms of qualitative knowledge about the plant operating points or analytical regulators (e.g., state feedback) for those operating points. A proportional-integral update law is used to obtain a fast parameters adaptation. Stability and robustness of this adaptive scheme are established using Lyapunov stability tools. The simulation results, for a two-link robot, confirm the performance of the proposed approach.     

Robust model reference adaptive control

We propose a new model reference adaptive control algorithm and show that it provides the robust stability of the resulting closed-loop adaptive control system with respect to unmodeled plant uncertainties. The robustness is achieved by using a relative error signal in combination with a dead zone and a projection in the adaptive law. The extra a priori information needed to design the adaptive law, are bounds on the plant parameters and an exponential bound on the impulse response of the inverse plant transfer function.     

Identifier-tracking model reference adaptive control

Model reference adaptive control is a major design method for controlling plants with uncertain parameters. The primary objective of this paper is to develop a new design approach for a differentiator-free model reference adaptive control of a single-input single-output linear time-invariant plant. The proposed method, called the “Identifier-tracking model reference adaptive control”, uses a stacked identifier structure that is new to the field of adaptive control. The goal is to make the output of the plant asymptotically track the output of the first identifier, and then driving the output of the first identifier to track that of the second identifier, and so forth, up to the qth identifier where q is the relative degree of the plant. Lastly, the output of the qth identifier is forced to converge to that of the reference model. Simulation results show the superiority of the proposed method over the traditional model reference adaptive control with augmented error in terms of the transient response. Since the resulting control systems are non-linear and time-varying, the stability analysis of the overall system plays a central role in developing the theory.     

基于系统辨识的模型参考自适应控制

介绍了荷兰Twente大学控制工程系研发的先进控制实验平台的基本组成、功能和基于机电一体化设计软件20-sim。以先进控制演示仪为研究对象,在20-sim环境下建立了系统的图标化数学模型,并从理论上对模型作了降阶分析。根据系统辨识的模型参考自适应控制（MRAC）原理,编制了模型参考自适应和Lyapunov求解模块。采用线性二次型调节器为控制模块,构成了完整的先进控制演示仪的MRAC控制系统。该系统仿真结果显示所建立的模型针对性强,可用于各种先进控制策略的仿真研究。     

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.     

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.     

An unstable dynamical system associated with model reference adaptive control

It is shown that a certain system of differential equations of importance to the proof of stability of the adaptive system proposed in [1], admit unbounded solutions. The implication of this result is that a much more elaborate argument than heretofore thought necessary is required to prove that the adaptive system of [1] is stable.     

最小拍无波纹直流电机控制系统仿真研究

在单位阶跃、单位速度和单位加速度3种典型输入下对直流电机的最小拍无波纹控制进行了研究.Matlab仿真结果表明,最小拍无波纹控制,输出能较快的跟随输入,且跟随后能基本消除静差.最小拍无波纹控制应用在直流电机控制系统中,能够使电机的转速在最少个采样周期内跟随期望转速,使系统达到无静差的稳态,表明了最小拍无波纹控制能实现较高的控制品质.     

Singularity-free multivariable model reference adaptive control

In this paper we propose a way to solve the problem of singularities in model reference adaptive control of linear multi-input-multi-output (MIMO) systems using a parameter modification procedure based on the least squares covariance matrix inverse. The scheme does not require any explicit prior knowledge about the leading coefficient matrix associated with the control input and secures a uniform lower bound for the determinant of the estimate of this matrix. A global convergence analysis is presented     

Continuous-time stochastic model reference adaptive control

The authors establish global convergence and asymptotic properties of a direct adaptive controller for continuous-time stochastic linear systems by presenting a direct adaptive control algorithm and an associated proof of convergence. This result is comprehensive and covers many other existing results as special cases. It has practical implications for the discrete-time case since it reveals how the existing discrete-time results must be modified so that they have meaningful limits as the sampling period decreases     

一类直接模型参考Backstepping自适应控制

提出一种新的直接模型参考Backstepping自适应控制系统结构:谈系统在直接模型参考卓适应控制(MRAC)结构的基础上,增加了Beckstepping控制信号发生器,通过Backstepping方法的灵活设计获得良好的过渡过程品质,得到直接MRAC在稳定性和鲁棒性设计等方面的优点.采用高阶调节器设计了未具规范化的直接模型参考Backstepping自适应律,克服了传统自适应律引入规范化信号后使系统过渡过程品质下降的缺点.     

Discrete model reference adaptive control with an augmented error signal

A method is developed for designing discrete model reference adaptive control systems when one has access to only the plant's input and output signals. Controllers for single-input, single-output, nonlinear, nonautonomous plants are developed via Lyapunov's second method. The augmented error signal method is employed to ensure that the normally used true error signal approaches zero asymptotically without requiring anticipative values of the plant output signal. Such anticipative signals are replaced by others easily obtained from low pass digital filters operating on the plant's input and output signals.     

间接型的混杂模型参考自适应控制

针对相对阶为1的理想系统,本文考虑了具有混杂自适应律的间接型模型参考自适应控制问题.通过建立系统和控制器的离散参数估计和它们的插值四者之间关系的性质,严格地分析了闭环系统的稳定性,证明了闭环系统中所有的信号都一致有界,并且跟踪误差渐进收敛于零.