The composition of simple local models for approximating complex nonlinear mappings is a common practice in recent modeling and control literature. This paper presents a comparative analysis of two different local approaches: the neuro-fuzzy inference system and the lazy learning approach. Neuro-fuzzy is a hybrid representation which combines the linguistic description typical of fuzzy inference systems, with learning procedures inspired by neural networks. Lazy learning is a memory-based technique that uses a query-based approach to select the best local model configuration by assessing and comparing different alternatives in cross-validation. In this paper, the two approaches are compared both as learning algorithms, and as identification modules of an adaptive control system. We show that lazy learning is able to provide better modeling accuracy and higher control performance at the cost of a reduced readability of the resulting approximator. Illustrative examples of identi cation and control of a nonlinear system starting from simulated data are given.

This paper presents local methods for modeling and control of discrete-time unknown nonlinear dynamical systems, when only a limited amount of input-output data is available. We propose the adoption of lazy learning, a memory-based technique for local modeling. The modeling procedure uses a query-based approach to select the best model configuration by assessing and comparing different alternatives. A new recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. Also, three methods to design controllers based on the local linearization provided by the lazy learning algorithm are described. In the first method the lazy technique returns the forward and inverse models of the system which are used to compute the control action to take. The second is an indirect method inspired to self-tuning regulators where recursive least squares estimation is replaced by a local approximator. The third method combin...

In the past year I have been inundated with articles on fuzzy logic as well as encouraged to use it for control systems. After reading some articles on fuzzy logic control, listening to a seminar by Zadeh, and attending a one day course on Intelligent Control, I started forming an opinion about how fuzzy logic control works. I believe that there are some fundamental pieces of information not provided in most fuzzy logic control papers. When one realizes what those pieces of information are, one gets a different opinion about how and when fuzzy logic control works and when it is more practical than conventional control. I will first state some opinions on fuzzy logic and try to justify them. Once this is done, I will return to some of the articles written by proponents of fuzzy logic and use the previous understanding to shed some light on what is really responsible for the improved system performance. 1. Introduction Before the 1992 CDC (IEEE Conference on Decision and Control) , I atte...

Abstract. The composition of simple local models for approximating complex nonlin-ear mappings is a common practice in recent modeling and control literature. This paper presents a comparative analysis of two different local approaches: the neuro-fuzzy infer-ence system and the lazy learning approach. A neuro-fuzzy system is an hybrid representation which combines the linguistic descrip-tion of fuzzy inference systems with learning procedures inspired by neural networks. Lazy learning is a memory-based technique that uses a query-based approach to select the best local model configuration by assessing and comparing different alternatives in cross-validation. The two approches are compared both as learning algorithms and as identification modules of an adaptive control system. The paper will show how the lazy learning is able to provide better modeling accuracy and control performance at the cost of a reduced readibility of the resulting approximator. Illustrative examples of identifica-tion and control of a nonlinear system starting from simulated data are given. 1

Abstract — In this paper, we consider a class of infinitehorizon discrete-time linear quadratic N-person games, in which one of the players lack the complete information of the game. With the assumptions on perfect state information pattern and steady state feedback strategies, we convert the original game problem into a multivariable adaptive control problem by taking use of the concept of fictitious play and the scheme of adaptive control. For the proposed adjustment procedure, we prove that each element of the estimates converges to its corresponding true value under the condition of persistent excitation. I.