A Novel Immune Genetic Algorithm-Based PID Controller Design and Its Application to CIP-I Intelligent Leg

Tan, Guozhen; Jiang, Bin; Yang, Liming

doi:10.1109/icnc.2007.113

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…The most important tasks in control design are the system model identification, the law or the control algorithm, the tuning and the control implementation. Asides from the proper operation of all the elements within a control-loop, the performance of the whole control system mainly depends on the controller parameters which are obtained by its tuning [9, 10]. For many years much research has been done with the aim of adjusting the controller parameters.…”

Section: Introductionmentioning

confidence: 99%

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PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems

Jaen-Cuéllar

Romero-Troncoso

Morales-Velazquez

et al. 2013

International Journal of Advanced Robotic Systems

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Performance improvement is the main goal of the study of PID control and much research has been conducted for this purpose. The PID filter is implemented in almost all industrial processes because of its wellknown beneficial features. In general, the whole system's performance strongly depends on the controller's efficiency and hence the tuning process plays a key role in the system's behaviour. In this work, the servo systems will be analysed, specifically the positioning control systems. Among the existent tuning methods, the GainPhase Margin method based on Frequency Response analysis is the most adequate for controller tuning in positioning control systems. Nevertheless, this method can be improved by integrating an optimization technique. The novelty of this work is the development of a new methodology for PID control tuning by coupling the Gain-Phase Margin method with the Genetic Algorithms in which the micro-population concept and adaptive mutation probability are applied. Simulations using a positioning system model in MATLAB and experimental tests in two CNC machines and an industrial robot are carried out in order to show the effectiveness of the proposal. The obtained results are compared with both the classical Gain-Phase Margin tuning and with a recent PID controller optimization using Genetic Algorithms based on real codification. The three methodologies are implemented using software.

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Section: Introductionmentioning

confidence: 99%

“…The performance of the system is validated for the model simulations. A methodology for a PID controller design was presented in [10], where the antibodies concept was used to improve a simple GA providing population diversity and searching speed. The validation of this approach was performed using simulations for an artificial leg.…”

Section: Introductionmentioning

confidence: 99%

PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems

Jaen-Cuéllar

Romero-Troncoso

Morales-Velazquez

et al. 2013

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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Robotic excavator trajectory control using an improved GA based PID controller

Feng

Yin

Weng

et al. 2018

Mechanical Systems and Signal Processing

124

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Multi-objective Genetic Algorithm for System Identification and Controller Optimization of Automated Guided Vehicle

Wu¹,

Lou²,

Tang³

2011

JNW

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This paper presents a multi-objective genetic algorithm (MOGA) with Pareto optimality and elitist tactics for the control system design of automated guided vehicle (AGV). The MOGA is used to identify AGV driving system model and optimize its servo control system sequentially. In system identification, the model identified by least square method is adopted as an evolution tutor who selects the individuals having balanced performances in all objectives as elitists. In controller optimization, the velocity regulating capability required by AGV path tracking is employed as decision-making preferences which select Pareto optimal solutions as elitists. According to different objectives and elitist tactics, several sub-populations are constructed and they evolve concurrently by using independent reproduction, neighborhood mutation and heuristic crossover. The lossless finite precision method and the multi-objective normalized increment distance are proposed to keep the population diversity with a low computational complexity. Experiment results show that the cascaded MOGA have the capability to make the system model consistent with AGV driving system both in amplitude and phase, and to make its servo control system satisfy the requirements on dynamic performance and steady-state accuracy in AGV path tracking

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A Novel Immune Genetic Algorithm-Based PID Controller Design and Its Application to CIP-I Intelligent Leg

Cited by 3 publications

References 10 publications

PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems

PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems

Robotic excavator trajectory control using an improved GA based PID controller

Multi-objective Genetic Algorithm for System Identification and Controller Optimization of Automated Guided Vehicle

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