Design of an H ∞ PID controller using particle swarm optimization

Zamani, Majid; Sadati, N.; Karimi-Ghartemani, Masoud

doi:10.1007/s12555-009-0213-9

Cited by 60 publications

(40 citation statements)

References 24 publications

(39 reference statements)

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“…Gaing proposed a PSO based PID controller for a higher order stable system with a particle size of 50 [36]. Majid et al discussed about the PID and H ∞ PID controller tuning for a class of stable process models with N = 30 [16,17]. Nelendran and Poobalan proposed a SWPID controller tuning fora class of unstable process models using the basic PSO algorithm with N = 20 [20].…”

Section: Algorithm Parameters For Optimization Search Pso Parametersmentioning

confidence: 99%

Setpoint weighted PID controller tuning for unstable system using heuristic algorithm

Rajinikanth¹,

Latha²

2012

Archives of Control Sciences

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Most of the real time chemical process loops are unstable in nature and designing a suitable controller for such systems are difficult than open loop stable processes. In this work, an attempt is made with a two degree of freedom setpoint weighted PID controller tuning procedure for a class of unstable systems using the recent heuristic algorithms such as Particle Swarm Optimization and Bacterial Foraging Optimization. The problem considered in this study is to aptly tune the controller in order to enhance the overall closed loop performance. A novel objective function proposed in this study is used to monitor the heuristic algorithms in order to get the optimal controller parameters like K p , K i , K d , and α with minimized iteration number. The proposed method is validated with a simulation study and this helps to accomplish enhanced system performance such as smooth reference tracking, satisfactory disturbance rejection, and error minimization for a class of unstable systems.

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Section: Algorithm Parameters For Optimization Search Pso Parametersmentioning

confidence: 99%

Setpoint weighted PID controller tuning for unstable system using heuristic algorithm

Rajinikanth¹,

Latha²

2012

Archives of Control Sciences

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“…6 Similarly, variations of PSO were used for tuning purposes in different applications such as high performance drilling, 7 control of automatic gantry crane, 8 steam temperature control in boilers, 9 and control of autonomous underwater vehicles. 10 Furthermore, PSO has been used for control gain tuning of more advanced PID-type controllers such as fuzzy PID, 11 H ∞ PID, 12 and robust PID. 13 In our previous research, [14][15][16][17] the concept of position domain control (PDC) was introduced in the form of PD and PID control.…”

Section: Introductionmentioning

confidence: 99%

Gain tuning of position domain PID control using particle swarm optimization

Pano

Ouyang

2014

Robotica

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Particle swarm optimization (PSO) is a heuristic optimization algorithm and is commonly used for the tuning of PD/PID-type controllers. In this paper, PSO is applied for control gain tuning of a position domain PID controller in order to improve contour tracking performances of linear and nonlinear contours for a serial multi-DOF robotic manipulator. A new fitness function is proposed for gain tuning based on the statistics of the contour error, and pre-existed fitness functions are also applied for the optimization process, followed by some comparison studies. The PSO tuning technique demonstrated the same effectiveness in position domain controllers as in time domain controllers with the results being quite satisfying with low contour errors for both linear and nonlinear contours, and the proposed fitness function is proved to be on par with the pre-existed fitness functions.

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“…Many researchers have employed mixed H 2 /H 1 optimal design strategy for optimal robust PID controller [6,7,16,17,22,24,44]. In [7], authors propose the design of simple genetic algorithm (GA) based mixed H 2 /H 1 controller for SISO system by minimizing ISE (H 2 norm) subjected to robust stability and disturbance attenuation constraints.…”

Section: Introductionmentioning

confidence: 99%