Multi-objective meta-heuristic optimization in intelligent control: A survey on the controller tuning problem

Rodríguez-Molina, Alejandro; Mezura-Montes, Efrén; Villarreal-Cervantes, Miguel Gabriel; Aldape-Pérez, Mario

doi:10.1016/j.asoc.2020.106342

Cited by 92 publications

(42 citation statements)

References 119 publications

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“…On the other hand, there are many optimizers in the specialized literature in control system tuning tasks, and three of them have been shown to have superior performance. These alternatives are the Differential Evolution (DE), the Genetic Algorithm (GA), and the Particle Swarm Optimization (PSO) [20,27,61]. Then, these three additional optimizers (BI techniques) are selected to work along with the AACT proposal for comparative purposes.…”

Section: A Comparative Analysis Among Bio-inspired Techniques In Aactmentioning

confidence: 99%

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Novel Asynchronous Activation of the Bio-Inspired Adaptive Tuning in the Speed Controller: Study Case in DC Motors

2021

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The fusion of bio-inspired algorithms into online controller tuning (adaptive controller tuning) is one of the main topics in Intelligent Control. One crucial issue is to reduce the times that the tuning process performs over time. In this work, a novel Asynchronous Adaptive Controller Tuning (AACT) approach is proposed to reduce the number of tuning process activations, and hence, this promotes resource savings in the overall computational cost for the tuning process. In this approach, an event function is designed to determine the control parameter update where the use of identification and predictive stages set the current control parameters. Furthermore, an elitist initialization in the differential evolution algorithm is also incorporated for solving the optimization problems at each stage. The speed regulation of the DC motor under disturbances is the study case in the ACCT approach. Comparative results with state-of-theart bio-inspired algorithms in control tuning reveal in the AACT approach that the elitist initialization in differential evolution notably benefits the controller performance. Moreover, the comparative results with the Synchronous Adaptive Controller Tuning (SACT) approach show that the proposal reduces 61% the tuning process computation frequency with a similar speed regulation performance when disturbances appear.INDEX TERMS Optimal controller tuning, DC motor, Event based tuning, Bio-inspired algorithm.

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Section: A Comparative Analysis Among Bio-inspired Techniques In Aactmentioning

confidence: 99%

“…Some of these methods are based on linear system theory [6][7][8][9], or optimization approaches for nonlinear systems [10][11][12][13][14][15][16][17][18][19]. Other works handle diverse control performance trade-offs to obtain different control gain alternatives to the designer [20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Novel Asynchronous Activation of the Bio-Inspired Adaptive Tuning in the Speed Controller: Study Case in DC Motors

2021

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“…Recently, promising meta-heuristic optimization methods are being studied to solve many complex problems such as turning PID controllers using three-stage optimization memetic exploration (3SOME), 21 tuning the membership functions of a fuzzy logic system using bee colony optimization (BCO), 22 optimum design of fuzzy controller using hybrid ant lion optimizer and Jaya algorithm, 23 a survey on the intelligent controller tuning problem, 24 and so on. Among them, the topic of neural networks-based heuristic optimization plays a trending technology.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator

2021

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This manuscript introduces a new adaptive inverse neural (AIN) control method applied to precisely track the piezoelectric (PZT) actuator displacement.First, a 3-layer neural network optimized by the enhanced differential evolution technique which modifies a mutation scheme and provides suggestions for selecting mutant coefficient F, crossover coefficient CR, and population size NP, is used to identify the inverse nonlinearity hysteresis structure of the PZT actuator. Second, a feed-forward control based on the identified model is proposed to compensate for the PZT hysteresis effect. Third, the Lyapunov stability principle is used to design and implement an adaptive law-based neural sliding mode model plus the feed-forward compensator to ensure that the whole PZT plant is operated in asymptotical stability. The experiment results demonstrate the proposed AIN controller proves superiority in comparison with other advanced control methods. K E Y W O R D S adaptive inverse neural controller, back-propagation, enhanced differential evolution, hybrid adaptive inverse neural control, Lyapunov stability concept

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“…These issues are effectively tackled in literature with nature-inspired algorithms. For instance, genetic algorithm (GA) [27]- [32] and particle swarm optimization (PSO) [33]- [37] have been successfully applied for parameter optimization in PFC and active power filter (APF) systems. The aforementioned literature addresses parameter optimization for the PFC system and mainly focuses on the PI control parameter optimization.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Three-Phase Boost Power Factor Correction Rectifier Through Combined Parameters Optimization of Proportional-Integral and Repetitive Controller

et al. 2021

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This paper performs parameter optimization of proportional-integral (PI) and repetitive controller (RC) with a new objective function by adding two degrees of freedom for a three-phase boost power factor correction (PFC) rectifier. The main objectives are to optimize the multiple control loop parameters for total harmonics distortion (THD) reduction and dynamic performance indices improvement, including overshoot, rise time, and zero steady-state error. The control parameters of the three-phase boost PFC rectifier are optimized through a standard genetic algorithm. After obtaining the optimal PI and RC parameters values, fast Fourier transform and dynamic response analysis were performed using MATLAB. Moreover, separate evaluation functions are used to validate the optimal results in terms of THD reduction and dynamic performance indices improvement. Furthermore, the results are compared with the existing objective functions to show the proposed objective function superiority. Simulation results demonstrated that our proposed objective function outperforms existing objective functions to achieve optimal PI and RC parameters value. Finally, simulation results are validated through experimental results. The experimental setup includes a 5kW three-phase PFC rectifier with DSP TMS320F28335 prototype hardware to verify controller parameter performance.

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Multi-objective meta-heuristic optimization in intelligent control: A survey on the controller tuning problem

Cited by 92 publications

References 119 publications

Novel Asynchronous Activation of the Bio-Inspired Adaptive Tuning in the Speed Controller: Study Case in DC Motors

Novel Asynchronous Activation of the Bio-Inspired Adaptive Tuning in the Speed Controller: Study Case in DC Motors

Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator

Performance Improvement of Three-Phase Boost Power Factor Correction Rectifier Through Combined Parameters Optimization of Proportional-Integral and Repetitive Controller

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