A fuzzy logic controller equipped with a training (adaptive) algorithm is proposed in this work to achieve H/sup /spl infin// tracking performance for a class of uncertain (model free) nonlinear single-input single-output (SISO) systems with external disturbances. An attempt is also made to create a bridge between two important control design techniques, i.e., H/sup /spl infin// control design and fuzzy control design, so as to supply H/sup /spl infin// control design with more intelligence and fuzzy control design with better performance. The perfect matching of parameters in an adaptive fuzzy logic system is generally deemed impossible. Therefore, a desired tracking performance cannot be guaranteed in the conventional adaptive fuzzy control systems. In this study, the influence of both fuzzy logic approximation error and external disturbance on the tracking error is attenuated to a prescribed level. Both indirect and direct adaptive fuzzy controllers are employed to treat this H/sup /spl infin// tracking problem. The authors' results indicate that arbitrarily small attenuation level can be achieved via the proposed adaptive fuzzy control algorithm if a weighting factor of control variable is adequately chosen. The proposed design method is also useful for the robust tracking control design of the nonlinear systems with external disturbances and a large uncertainty or unknown variation in plant parameters and structures. Furthermore, only smooth control signals are needed via the proposed control designs. Two simulation examples are given finally to illustrate the performance of the proposed methods. Computer simulation results confirm that the effect of both the fuzzy approximation error and external disturbance on the tracking error can be attenuated efficiently by the proposed method.

H/sup /spl infin// tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach

A review of the fundamentals of fuzzy sets, fuzzy rules and fuzzy inference systems is provided in this chapter. Beginning with crisp or classical sets and their operations, we derived fuzzy sets and their operations. Classical set membership functions and fuzzy membership functions are discussed in detail following set theory. Fuzzy rules are described using an air conditioner control example. The different defuzzification techniques and their processes are discussed with the same example step by step. Finally, some other fuzzy techniques are discussed such as off-line and on-line fuzzy control systems as well as a fuzzy closed-loop control system including multiple lookup tables.

https://www.springer.com/cda/content/document/cda_downloaddocument/9781846284687-c1.pdf

Fundamentals of Fuzzy Logic Control — Fuzzy Sets, Fuzzy Rules and Defuzzifications

In this paper, an observer-based direct adaptive fuzzy-neural control scheme is presented for nonaffine nonlinear systems in the presence of unknown structure of nonlinearities. A direct adaptive fuzzy-neural controller and a class of generalized nonlinear systems, which are called nonaffine nonlinear systems, are instead of the indirect one and affine nonlinear systems given by Leu et al. By using implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the fuzzy-neural controller are derived for the nonaffine nonlinear systems. Based on strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. Moreover, the overall adaptive scheme guarantees that all signals involved are bounded and the output of the closed-loop system will asymptotically track the desired output trajectory. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.

https://ir.nctu.edu.tw:443/bitstream/11536/13544/1/000230505600007.pdf

Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems

A deadzone compensator is designed for industrial positioning: systems using a fuzzy logic (FL) controller. The FL approach is shown to subsume other approaches that use switching or indicator functions. The classification property of FL systems makes them a natural candidate for the rejection of errors induced by the deadzone, which has regions in which it behaves differently. A tuning algorithm is given for the FL parameters, so that the deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The adaptive FL deadzone compensator is implemented on an actual industrial CNC machine tool to show its efficacy.

Deadzone compensation in motion control systems using adaptive fuzzy logic control

The application of fuzzy logic to the unit commitment problem is demonstrated. This method allows a qualitative description of the behavior of a power system, the system's characteristics, and response without the need for exact mathematical formulations. It is demonstrated, through a numerical example, that a fuzzy-logic-based approach achieves a logical and feasible economical cost of operation of the power system, which is the major objective of unit commitment.

A fuzzy logic approach to unit commitment

Fuzzy Logic and Control

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Fuzzy Logic and Control