Abstract
One characteristic of neuro-fuzzy systems is the possibility of incorporating preliminary information in their structure as well as being able to establish an initial configuration to carry out the training. In this regard, the strategy to establish the configuration of the fuzzy system is a relevant aspect. This document displays the design and implementation of a neuro-fuzzy controller based on Boolean relations to regulate the angular position in an electromechanical plant, composed by a motor coupled to inertia with friction (a widely studied plant that serves to show the control system design process). The structure of fuzzy systems based on Boolean relations considers the operation of sensors and actuators present in the control system. In this way, the initial configuration of fuzzy controller can be determined. In order to perform the optimization of the neuro-fuzzy controller, the continuous plant model is converted to discrete time to be included in the closed-loop controller training equations. For the design process, first the optimization of a Proportional Integral (PI) linear controller is carried out. Thus, linear controller parameters are employed to establish the structure and initial configuration of the neuro-fuzzy controller. The optimization process also includes weighting factors for error and control action in such a way that allows having different system responses. Considering the structure of the control system, the optimization algorithm (training algorithm) employed is dynamic back propagation. The results via simulations show that optimization is achieved in the linear and neuro-fuzzy controllers using different weighting values for the error signal and control action. It is also observed that the proposed control strategy allows disturbance rejection.
Highlights
In control systems, fuzzy logic offers broad applicability given its flexibility to implement control strategies through a set of rules using membership functions [1,2,3]
Authors propose a hybridization through a combination of a finite element method, statistical technique, desirability function approach, fuzzy logic system, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Lightning Attachment Procedure Optimization (LAPO)
Linear controller design and optimization allow the design of the fuzzy controller as well as the definition of the initial optimization setup of this controller
Summary
Fuzzy logic offers broad applicability given its flexibility to implement control strategies through a set of rules using membership functions [1,2,3]. A remarkable application is observed for compliant mechanism design used in soft robotics, space, and bioengineering due to the advantages of free friction, monolithic structure, and minimal assembly Considering this area, a hybrid methodology is presented in [6] for solving the multi-objective optimization design. Authors propose a hybridization through a combination of a finite element method, statistical technique, desirability function approach, fuzzy logic system, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Lightning Attachment Procedure Optimization (LAPO). A hybrid procedure of the Taguchi method is described in [9], using fuzzy logic, response surface method, and Moth-flame optimization algorithm to solve the design optimization of a flexure hinge to enhance three quality characteristics. An integrated regression equation is determined via response surface method and flexure hinge is optimized using Moth-flame optimization algorithm
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