State-space approach to non-linear model reference adaptive control

A new method is developed for tracking in non-linear delay-differential systems when the exact knowledge of the system parameters are not known. The method is based on securing the stability in the state spaces of plant and reference models. The results obtained are quite general in nature and can be applied to many physical systems.     

A new approach to stochastic adaptive control

The principal techniques used up to now for the analysis of stochastic adaptive control systems have been 1) super-martingale (often called stochastic Lyapunov) methods and 2) methods relying upon the strong consistency of some parameter estimation scheme. Optimal stochastic control and filtering methods have also been employed. Although there have been some successes, the extension of these techniques to a broader class of adaptive control problems, including the case of time-varying parameters, has been difficult. In this paper a new approach is adopted: if an underlying Markovian state-space system for the controlled process is available, and if this process possesses stationary transition probabilities, then the powerful ergodic theory of Markov processes may be applied. Subject to technical conditions, such as stability, one may deduce 1) the existence of an invariant measure for the process and 2) the convergence almost surely of the sample averages of a function of the state process (and of its expectation) to its conditional expectation. The technique is illustrated by an application to a previously unsolved problem involving a linear system with unbounded random time-varying parameters.     

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.     

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.     

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.     

New approach to robust model reference adaptive control for a class of plants

Motivated by recent advances in designing robust adaptive controllers and in dealing with uncertain dynamical systems, a new model reference adaptive control which is robust to a class of unmodelled dynamics and bounded output disturbances in the case of relative degree one is presented. The implementation of the controllers includes a switching mechanism which plays a crucially important role in functions of stabilizing as well as tracking. It is shown that global stability of the overall system is achieved under no assumption of persistency of excitation, and tracking errors will converge to a residual set whose size can be directly related to the size of unmodelled dynamics and of output disturbances explicitly. In the ideal case, the residual set degenerates to a single null point and convergence can be achieved in finite time without any requirement of persistency of excitation.     

A new approach to adaptive control: no nonlinearities

In adaptive control the goal is to design a controller to control an uncertain system whose parameters may be changing with time. Typically the controller consists of an identifier (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that good asymptotic behaviour is achieved, such as model matching (for minimum phase systems) or stability. However, a typical adaptive controller does not track time-varying parameters very well, and it is often highly nonlinear, which can result in undesirable behaviour, such as large transients or a large control signal. Furthermore, most adaptive controllers provide only asymptotic tracking, with no ability to design for a pre-specified settling time.Here we propose an alternative approach, which yields a linear periodic controller. Rather than estimating the plant or compensator parameters, instead we estimate what the control signal would be if the plant parameters were known. In this paper we argue the utility of this approach and then examine the first order case in detail, including a simulation. We also explore the benefits and limitations of the approach.     

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.     

基于虚拟参考反馈整定的改进无模型自适应控制

针对一类离散时间非线性系统, 提出一种基于虚拟参考反馈整定的改进无模型自适应控制方案. 首先, 利用动态线性化方法给出非线性系统的紧格式动态线性化模型; 然后, 基于优化技术设计控制算法和伪偏导数估计算法; 最后, 设计基于虚拟参考反馈整定的伪偏导数初值和重置值的估计算法. 该控制方案设计仅依赖于被控系统的输入和输出数据, 且能保证闭环系统的稳定性和收敛性. 仿真比较结果验证了所提出方法的有效性.

     

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.     

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     

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.     

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     

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.     

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     

A hyperstability criterion for model reference adaptive control systems

This paper considers the stability problem of the model reference adaptive control systems by means of the properties of hyperstable systems. A theorem concerning the hyperstability of model reference adaptive control systems is presented. This theorem directly gives a structure of the adaption mechanism. The results presented here include all the results obtained by Butchart, Shackcloth, Parks, Winsor, Roy, and Dressler. The hyperstability approach presented in this paper also allows for other solutions to the adaption mechanism and represents a general method for studying this type of adaptive systems. The results are directly applicable to the design of model reference adaptive control systems and they were verified for some particular cases by analogical simulation.     

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.     

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     

Bifurcation in model reference adaptive control systems

We study the effect of unmodeled dynamics on the performance of a simple model reference adaptive control mechanism. Exact stability boundaries are computed and different routes to chaotic behavior and global instability are described. Typically, we observed a region of period doubling and Hopf bifurcations similar to those observed in the Ziegler-Nichols approach to PID control design. The analysis applies to higher order and certain nonlinear systems as well.