Evaluation of identifier based and & non-identifier based adaptive supervisory & control using a benchmark example (original) (raw)

Multi-model unfalsified adaptive switching supervisory control

Automatica, 2010

The paper studies how on-line inferring stability of a potential control-loop consisting of an uncertain plant interconnected in feedback with a candidate controller using plant I/O pairs recorded while the plant is possibly driven by a different controller. In such a context, a convenient tool to work with is to resort to the conceptual entity of a virtual reference (VR). The adopted approach consists of embedding, in the so-called unfalsified adaptive switching control schemes based on VR, a family of nominal models pairwise associated with the given candidate controllers. The result is that the supervised switching mechanism can moderate the chance that destabilizing controllers be switched-on and, hence, reduce both the magnitude and time durations of “learning” transients after start-up, while, in contrast with pre-existing multi-model based methods, stability in-the-large is guaranteed under the minimal conceivable assumption that a stabilizing candidate controller exist.

Observer-based adaptive control using multiple-models switching and tuning

IET Control Theory & Applications, 2014

The purpose of this paper is to marry the two concepts of multiple model adaptive control and safe adaptive control. In its simplest form, multiple model adaptive control involves a supervisory switching among one of a finite number of controllers as more is learnt about the plant, until one of the controllers is finally selected and remains unchanged. Safe adapative control is concerned with ensuring that when the controller is changed the closed-loop is never unstable. This paper introduces a receding horizon multiple model, switching and tuning control scheme based on an on-line redesign of the controller.. This control scheme has a natural two-stage adaptive control algorithm: identification of the closest model and design of the control law. The computational complexity aspects of this approach to adaptive control are discussed briefly. A nonlinear system is used to illustrate the ideas.

Robust adaptive control using multiple models, switching and tuning

Control Theory & Applications, IET, 2011

The supervisory control problem is analysed as an online robust design problem using switching to select the relevant models for designing the control law. The proposed supervisory control algorithm is based on the integration of concepts used in supervisory adaptive control, robust control and receding horizon control.

Multiple-model adaptive switching control for uncertain multivariable systems

2011

This paper addresses the problem of controlling an uncertain multi-input multi-output (MIMO) system by means of adaptive switching control schemes. In particular, the paper aims at extending the approach of multiple-model unfalsified adaptive switched control, so far restricted to single-input single-output systems, to a general multivariable setting. The proposed scheme relies on a data-driven "high-level" unit, called the supervisor, which at any time can switch on in feedback with the uncertain plant one controller from a finite family of candidate controllers. The supervisor performs routing and scheduling tasks by monitoring suitable test functionals which, based on the measured data, provide a measure of mismatch between the potential loop made up by the uncertain plant in feedback with the candidate controller and the nominal "reference loop" related to the same candidate controller.

Enhancement of Robust Tracking Performance via Switching Supervisory Adaptive Control

When the process is highly uncertain, even linear minimum phase systems must sacrifice desirable feedback control benefits to avoid an excessive 'cost of feedback', while preserving the robust stability. In this paper, the control structure of supervisory based switching Quantitative Feedback Theory (QFT) control is proposed to control highly uncertain plants. According to this strategy, the uncertainty region is suitably divided into smaller regions. It is assumed that a QFT controller-prefilter exits for robust stability and robust performance of the individual uncertain sets. The proposed control architecture is made up by these local controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the candidate local model behavior with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching for stability reasons. Besides, each controller is designed to be stable in the whole uncertainty domain, and as accurate in command tracking as desired in its uncertainty subset to preserve the robust stability from any failure in the switching.

Unfalsified adaptive switching supervisory control of time varying systems

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009

In recent years, unfalsified adaptive switching supervisory control (UASSC) has emerged as an effective technique for tackling the problem of controlling uncertain plants only on the basis of the plant I/O data. The aim of this paper is to construct a novel switching logic, which, when combined with appropriate test functions, makes it possible to extend UASSC, so far restricted to time-invariant systems, to the case of systems whose dynamics are subject to infrequent but possibly large variations.

Multiple model adaptive control. Part 2: switching

International Journal of Robust and Nonlinear Control, 2001

This paper addresses the problem of controlling a continuous-time linear system with large modelling errors. We employ an adaptive control algorithm consisting of a family of linear candidate controllers supervised by a high-level switching logic. Methods for constructing such controller families have been discussed in the recent paper by the authors. The present paper concentrates on the switching task in a multiple model context. We describe and compare two di!erent switching logics, and in each case study the behaviour of the resulting closed-loop hybrid system.

Multi–Controller Adaptive Control (MCAC) for a Tracking Problem using an Unfalsification approach

Proceedings of the 44th IEEE Conference on Decision and Control, 2005

In this paper, we apply a multiple controller based adaptive method to solve a tracking problem, where the plant output is required to track a reference input. The proposed methodology is based on an unfalsification approach. The method relies on a finite set of candidate-controllers; depending on the evolving plant data, it learns and selects an optimal controller from the candidate controller set. Although prior plant knowledge is helpful in selecting the candidate controller set, the method makes no use of, nor tries to identify, the plant structure or its parameters while deciding the optimal switching sequence. Probable performance of candidate controllers is evaluated directly from the plant data.

Algorithms for Optimal Model Distributions in Adaptive Switching Control Schemes

Machines, 2016

Several multiple model adaptive control architectures have been proposed in the literature. Despite many advances in theory, the crucial question of how to synthesize the pairs model/controller in a structurally optimal way is to a large extent not addressed. In particular, it is not clear how to place the pairs model/controller is such a way that the properties of the switching algorithm (e.g., number of switches, learning transient, final performance) are optimal with respect to some criteria. In this work, we focus on the so-called multi-model unfalsified adaptive supervisory switching control (MUASSC) scheme; we define a suitable structural optimality criterion and develop algorithms for synthesizing the pairs model/controller in such a way that they are optimal with respect to the structural optimality criterion we defined. The peculiarity of the proposed optimality criterion and algorithms is that the optimization is carried out so as to optimize the entire behavior of the adaptive algorithm, i.e., both the learning transient and the steady-state response. A comparison is made with respect to the model distribution of the robust multiple model adaptive control (RMMAC), where the optimization considers only the steady-state ideal response and neglects any learning transient.

A new approach to switching robust adaptive control

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), 2004

Comparison of advantages and disadvantages of control methods used in nonlinear systems is presented. To take advantage of both adaptive and robust control methods, a novel method which switches between the two is proposed. Robust control is used during transients and situations where parameters are uncertain. During steady state operations, adaptive control is used. Switching between the two methods is carried out based on the standard deviation of the estimated parameter vector. The method has been implemented on a 2 DOF articulated robot. Experimental results are presented to prove the robustness and the accuracy of the new control method.