Local control gradients criterion for selection of neuroemulators for model reference adaptive neurocontrol

We discuss neural identification and control of nonlinear dynamic plant. We analyze a problem of selection of a proper neuroemulator for training neurocontrollers, and we propose a new effective criterion based on the analysis of local gradients of neuroemulator??s input neurons. We present results of numerical simulations of neurocontroller training by a gradient descent method and by an Extended Kalman Filter method.     

Simple method for model reference adaptive control

A simple method is presented for combined signal synthesis and parameter adaptation within the framework of model reference adaptive control theory. The results are obtained using a simple derivation based on an improved Lyapunov function.     

Tracking and parameter identification for model reference adaptive control

We provide barrier Lyapunov functions for model reference adaptive control algorithms, allowing us to prove robustness in the input‐to‐state stability framework and to compute rates of exponential convergence of the tracking and parameter identification errors to zero. Our results ensure identification of all entries of the unknown weight and control effectiveness matrices. We provide easily checked sufficient conditions for our relaxed persistency of excitation conditions to hold. Our illustrative numerical example demonstrates the performance of the control methods.     

Vector control for induction motor drives based on adaptive variable structure control algorithm

This paper presents a new adaptive robust control for induction motor drives. The proposed control scheme is based on the so‐called field oriented control theory that allows to control the induction motor like a separately excited direct current motor drive, where the field flux (produced by the field current) and the armature flux (produced by the armature current) are decoupled. The robust control law is based on the sliding mode control theory, but unlike the traditional sliding mode control schemes, the proposed design incorporates an adaptive switching gain that avoids the need of calculating an upper limit of the system uncertainties. Moreover the proposed control law is smoothed out in order to avoid the high control activity inherent to the switching control laws. The resulting closed loop system is proven to be stable using the Lyapunov stability theory. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

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.     

一种实用的模型自适应MPC策略

在丙烯精馏塔的控制中,由于被控对象的非线性特性,采用线性模型的模型预测控制器难以保持良好的控制性能。本文提出基于系统稳态模型的模型自适应MPC策略,利用稳态模型在不同操作点上被控变量对操纵变量及扰动变量的相对变化率的变化,来刷新RMPCT控制器中各通道的模型增益。在模型输出对输入的相对变化率的计算中,使用主操作区间内的变化率以替代实际操作点的变化率,并采用设定模型变化域和控制模型变化频度的方法,以解决模型变化过大,与模型变换周期和RMPCT控制周期不协调而引起的系统不稳定等问题。实际投运效果表明:采用该控制策略,塔顶、塔底温度控制偏差与传统RMPCT比下降了一个数量级,有利于稳定与提高产品质量。     

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.     

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.     

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.     

A simplified algorithm for model reference adaptive control

Generation of one of the auxiliary inputs in model reference adaptive control systems requires a positive definite quadratic function of2nsystem variables for annth order system. To generate such a function can require as many as2nsignal multiplications. In this paper, it is shown how this input can be replaced by one that is much simpler to generate and reduces the number of signal multiplications required to one. The property of global asymptotic stability of the closed-loop adaptive system is preserved with the simpler auxiliary input.     

Robust sliding mode control using adaptive switching gain for induction motors

A robust sliding mode approach combined with a field oriented control （FOC） for induction motor （IM） speed control is presented. The proposed sliding mode control （SMC） design uses an adaptive switching gain and an integrator. This approach guarantees the same robustness and dynamic performance of traditional SMC algorithms. And at the same time, it attenuates the chattering phenomenon, which is the main drawback in actual implementation of this technique. This approach is insensitive to uncertainties and permits to decrease the requirement for the bound of these uncertainties. The stability and robustness of the closed- loop system are proven analytically using the Lyapunov synthesis approach. The proposed method attenuates the effect of both uncertainties and external disturbances. Experimental results are presented to validate the effectiveness and the good performance of the developed method.     

A blackboard control architecture for model selection and sequencing

Model management systems are computerised systems that facilitate the management of large numbers of decision models used in organizations. Model selection and sequencing in a model management system is the problem of processing a given model base in order to arrive at a sequence of models that can be executed to produce a set of required outputs (goal). Prior solution approaches do not attempt to solve this problem such that the goal is achieved while best meeting the objectives of the user. Instead, research to date has typically provided the first sequence of models which satisfy the goal, without attempting to optimise the objectives of the user. This restricts the applicability of many existing approaches to problems with unique solutions or to situations where users exhibit no preference among the candidate model sequences (i.e. solutions). In many real-world problems, however, multiple solutions may exist and users may prefer a certain solution over the others, based on a variety of criteria such as solution cost, accuracy and so on. In this paper, we present an architecture based on the concept of blackboard control that solves the model selection and sequencing problem while attempting to optimise the objectives of the user. We also discuss the applicability of the proposed approach for solving other problems encountered in the area of model management.     

Stochastic adaptive prediction and model reference control

Guo and Chen (1991) have recently shown how to establish the self-optimality and mean square stability of a self-tuning regulator. The idea allows us to proceed with the development of a more comprehensive theory of stochastic adaptive filtering, control and identification. In adaptive filtering, we examine both indirect and noninterlaced direct schemes for prediction, using both least-squares and gradient parameter estimation algorithms. In addition to analyzing similar direct adaptive control algorithms, we propose new generalized certainty equivalence adaptive model reference control laws with simultaneous disturbance rejection. We also establish that the parameters converge to the null space of a certain matrix. From this one may deduce the convergence of several adaptive controllers     

Direct and indirect model reference adaptive control

The paper considers the control of an unknown linear time-invariant plant using Direct and Indirect Model Reference Adaptive Control. Employing a specific controller structure and the concept of positive realness, adaptive laws are derived using Indirect Control which are identical to those obtained in the case of Direct Control. The stability questions that arise are also shown to be the same. Simulation results using the new scheme are presented for the control of both stable and unstable plants.     

基于滑模与自适应观测器的感应电机非线性控制新策略

提出一种结合滑模变结构和自适应观测技术的感应电机非线性控制新方法. 以定子电流与定子磁链为状态变量建立感应电机模型, 采用非线性分析方法建立转矩与磁链误差方程, 使用自适应滑模技术设计转矩与磁链控制器, 推导出定子电压控制量. 基于模型参考技术设计自适应观测器, 向控制器提供准确的转速辨识与磁链观测值,并给出了控制系统的稳定性证明. 该方法具有转矩脉动小、定子磁链畸变不明显的优点, 低速时也具有良好的控制性能, 且对参数与负载变化有较强的鲁棒性. 仿真与实验结果证明了该控制策略的正确性与有效性.     

Robust model reference adaptive control with hybrid adaptive law

Robust model reference adaptive control (RMRAC) with hybrid adaptive law is considered. The advantages of the hybrid RMRAC scheme over other continuous counterparts include smaller computational effort during implementation and better robustness properties in the presence of modelling errors and disturbances. For the hybrid adaptive control scheme, stability and the tracking performance are analysed rigorously.     

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.     

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     

A model reference adaptive control scheme for pure-feedbacknonlinear systems

A model reference adaptive control (MRAC) scheme is presented for nonlinear systems in a pure-feedback canonical form with unknown parameters. The present of parameter uncertainty in the system causes imperfect linearization, i.e. it introduces nonlinear additive terms in the transformed coordinates. Under some mild technical assumptions, global convergence of the output error is established for all initial estimates of the parameter vector lying in an open neighborhood of the true parameters in the parameter space