An Application of Novel Nature-Inspired Optimization Algorithms to Auto-Tuning State Feedback Speed Controller for PMSM

2018 ◽  
Vol 54 (3) ◽  
pp. 2913-2925 ◽  
Author(s):  
Tomasz Tarczewski ◽  
Lech M. Grzesiak
Keyword(s):  
State Feedback ◽  
Optimization Algorithms ◽  
Speed Controller ◽  
Auto Tuning

scholarly journals Parallel computing applied to auto-tuning of state feedback speed controller for PMSM drive

2019 ◽  
Vol 28 ◽  
pp. 01031
Author(s):  
Rafal Szczepanski ◽  
Tomasz Tarczewski ◽  
Lech M. Grzesiak
Keyword(s):  
Parallel Computing ◽  
State Feedback ◽  
Computation Time ◽  
The Other ◽  
Speed Controller ◽  
Speed Up ◽  
Tuning Process ◽  
Auto Tuning ◽  
Reduce Computation Time ◽  
Simulation Time

Nowadays the simulation is inseparable part of researcher's work. Its computation time may significantly exceed the experiment time. On the other hand, multi-core processors can be used to reduce computation time by using parallel computing. The parallel computing can be employed to decrease the overall simulation time. In this paper the parallel computing is used to speed-up the auto-tuning process of state feedback speed controller for PMSM drive.

scholarly journals Auto-Tuning Process of State Feedback Speed Controller Applied for Two-Mass System

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3067
Author(s):  
Rafal Szczepanski ◽  
Marcin Kaminski ◽  
Tomasz Tarczewski
Keyword(s):  
State Feedback ◽  
Mass System ◽  
Tuning Method ◽  
Optimal Coefficients ◽  
Speed Controller ◽  
Bee Colony ◽  
Tuning Process ◽  
Drive Systems ◽  
The Stability ◽  
Variable Moment

The state feedback controller is increasingly applied in electrical drive systems due to robustness and good disturbance compensation, however its main drawback is related to complex and time consuming tuning process. It is particularly troublesome for designer, if the plant is compound, nonlinear elements are taken into account, measurement noise is considered, etc. In this paper the application of nature-inspired optimization algorithm to automatic tuning of state feedback speed controller (SFC) for two-mass system (TMS) is proposed. In order to obtain optimal coefficients of SFC, the Artificial Bee Colony algorithm (ABC) is used. The objective function is described and discussed in details. Comparison with analytical tuning method of SFC is also included. Additionally, the stability analysis for the control system, optimized using the ABC algorithm, is presented. Synthesis procedure of the controller is utilized in Matlab/Simulink from MathWorks. Next, obtained coefficients of the controller are examined on the laboratory stand, also with variable moment of inertia values, to indicate robustness of the controller with optimal coefficients.

scholarly journals Speed Proportional Integrative Derivative Controller: Optimization Functions in Metaheuristic Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Luis Fernando de Mingo López ◽  
Francisco Serradilla García ◽  
José Eugenio Naranjo Hernández ◽  
Nuria Gómez Blas
Keyword(s):  
Pid Controller ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Metaheuristic Algorithms ◽  
Mathematical Background ◽  
Speed Controller ◽  
Search Optimization ◽  
Metaheuristic Search ◽  
Self Driving Cars ◽  
The Right

Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema.

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