Affiliation:
1. Department of Electrical and Computer Engineering, COMSATS University, Islamabad 45550, Pakistan
2. Department of Electrical and Computer Engineering, International Islamic University, Islamabad 44000, Pakistan
3. School of Computer Science, Faculty of Science and Engineering, University of Hull, Kingston upon Hull HU6 7RX, UK
Abstract
This research proposes a robust nonlinear hybrid control approach to the speed control of a multi-input-and-multi-output separately excited DC motor (SEDCM). The motor that was under consideration experienced parametric uncertainties and load disturbances in the weak field region. The proposed technique aims to merge the benefits of adaptive backstepping (AB) and integral sliding mode control (ISMC) to enhance the overall system’s robustness. The unknown parameters with load disturbances are estimated using an adaptation law. These estimated parameters are incorporated into the controller design, to achieve a highly robust controller. The theoretical stability of the system is proved using the Lyapunov stability criteria. The effectiveness of the proposed AB–ISMC was demonstrated by simulation, to track the reference speed under parametric uncertainties and load disturbances. The control performance of the proposed technique was compared to that of feedback linearization (FBL), conventional sliding mode control (SMC), and AB control laws without and with the adaptation law. Regression parameters, such as integral square error, integral absolute error, and integral time absolute error, were calculated to quantitatively analyze the tracking performance and robustness of the implemented nonlinear control techniques. The simulation results demonstrated that the proposed controller could accurately track the reference speed and exhibited robustness, with steady-state error accuracy. Moreover, AB–ISMC overperformed, compared to the FBL, SMC, AB controller without adaptation law and AB controller with adaptation law, in reducing the settling time by factors of 27%, 67%, 23%, and 21%, respectively, thus highlighting the superior performance of the proposed controller.
Subject
Artificial Intelligence,Control and Optimization,Mechanical Engineering
Cited by
22 articles.
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