Abstract
This paper combines interval type-2 fuzzy logic with adaptive control theory for the control of a three degree-of-freedom (DOF) helicopter. This strategy yields robustness to various kinds of uncertainties and guaranteed stability of the closed-loop control system. Thus, precise trajectory tracking is maintained under various operational conditions with the presence of various types of uncertainties. Unlike other controllers, the proposed controller approximates the helicopter’s inverse dynamic model and assumes no a priori knowledge of the helicopter’s dynamics or parameters. The proposed controller is applied to a 3-DOF helicopter model and compared against three other controllers, i.e., PID control, adaptive control, and adaptive sliding-mode control. Numerical results show its high performance and robustness under the presence of uncertainties. To better assess the performance of the control system, two quantitative tracking performance metrics are introduced, i.e., the integral of the tracking errors and the integral of the control signals. Comparative numerical results reveal the superiority of the proposed method by achieving the highest tracking accuracy with the lowest control effort.
Highlights
Helicopters are able to levitate and navigate in tight and hazardous locations
This section is dedicated to the analysis of the performance of the 3-DOF helicopter whose physical parameters and control gains are defined in Tables 1 and 2, respectively
In order to assess the performance and robustness of the closed-loop control system, the 3-DOF helicopter model is implemented in MATLAB/Simulink R by MathWorks Inc
Summary
Helicopters are able to levitate and navigate in tight and hazardous locations. This requires a robust controller to deal with numerous uncertainties such as, changes in mass and inertia, along with other unpredictable factors like external disturbances. A mathematically ill-defined designed controller that is subjected to various disturbances and uncertainties can be approximated with computational intelligence tool, such as artificial neural networks and fuzzy logic systems, since these intelligent tools with high accuracy can uniformly approximate any real continuous function [28,29,30,31]. Such an advancement in neural network, can lead to modeling many complex models [11,12,32,33]. The remaining parts of the paper are organized as follow: Section 1 introduces the dynamic model of the helicopter and present the problem statement, Section 2 outlines the adaptive control methodology, Section 3 deals with numeric results and discussion, and Section 4 states the conclusion with comments and recommendations for future work
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