Optimal Gain Scheduling of PID Controller for the Speed Control of PMSM Drive Using Bio-Inspired Optimization Algorithms

Brushless Direct Current (BLDC) motors have gained popularity in recent years due to their high-power density. Many type of speed controller techniques have been developed and Proportional Integral Derivative (PID) controller has been the most widely used. However, PID’s performance deteriorates during nonlinear loads conditions. Over the past five years, controllers have been developed to overcome this limitations in BLDC speed control, however the solutions are focusing on forward motoring only. In this paper, a speed controller for BLDC with seamless speed reversal using Modified Fuzzy Gain Scheduling is proposed. The proposed controller regulates the speed using Fuzzy Gain Scheduling 49 base rules. The controller was tested for six test cases and compared to PID and Self-Tuning Fuzzy PID controller. It is found out the proposed controller yields lowest steady state error, ess of 0.025 % during step-changing speed test case. Overall, Modified Fuzzy Gain Scheduling BLDC speed controller outperforms the other two similar controllers in variable speed conditions. The controller has potential to be used as bidirectional drive in highly dynamic load conditions.

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Comparative Analysisof PID Controller for Speed Control of DC motor with Intelligent Optimization Algorithms

2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) ◽

10.1109/icacccn.2018.8748475 ◽

2018 ◽

Author(s):

Jeetendra Agarwal ◽

Girish Parmar ◽

Rajeev Gupta

Keyword(s):

Pid Controller ◽

Optimization Algorithms ◽

Speed Control ◽

Dc Motor ◽

Intelligent Optimization ◽

Intelligent Optimization Algorithms

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Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Energies ◽

10.3390/en14123385 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3385

Author(s):

Erickson Puchta ◽

Priscilla Bassetto ◽

Lucas Biuk ◽

Marco Itaborahy Filho ◽

Attilio Converti ◽

...

Keyword(s):

Pid Controller ◽

Optimization Algorithms ◽

Optimization Methods ◽

Buck Converter ◽

Algebraic Solution ◽

Nonlinear Load ◽

Bee Colony ◽

Arduous Task ◽

Whale Optimization

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

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