Optimal design of adaptive and proportional integral derivative controllers using a novel hybrid particle swarm optimization algorithm

被引:8
|
作者
Bejarbaneh, Elham Yazdani [1 ]
Ahangarnejad, Arash Hosseinian [2 ]
Bagheri, Ahmad [3 ]
Bejarbaneh, Behnam Yazdani [4 ]
Binh Thai Pham [5 ]
Buyamin, Salinda [1 ]
Shirinzadeh, Fatemeh [6 ]
机构
[1] Univ Teknol Malaysia, Fac Elect Engn, Skudai 81310, Johor, Malaysia
[2] Dept Mech Engn, Blacksburg, VA USA
[3] Univ Guilan, Fac Mech Engn, Dept Dynam Control & Vibrat, Rasht, Iran
[4] Univ Teknol Malaysia, Fac Civil Engn, Dept Geotech & Transportat, Skudai, Johor, Malaysia
[5] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
[6] Ahrar Inst Technol & Higher Educ, Dept Elect Engn, Rasht, Iran
关键词
Rotational inverted pendulum; model reference adaptive PID control; particle swarm optimization; sine cosine algorithm; levy flight; whale optimization algorithm; INVERTED PENDULUM SYSTEM; LEVY FLIGHT; SLIDING CONTROL;
D O I
10.1177/0142331219891571
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Controlling of a rotational inverted pendulum is considered as a challenging problem, mainly due to the system's inherent nonlinear and unstable dynamics. In fact, the goal of this control is to maintain the pendulum vertically upward regardless of external disturbances. This paper aims to optimally design a model reference adaptive proportional integral derivative (PID) control for rotary inverted pendulum system based on a novel hybrid particle swarm optimization algorithm, combining sine cosine algorithm and levy flight distribution. Evaluation of the performance quality of the proposed adaptive controller is accomplished based on the stabilization and tracking control of rotary inverted pendulum system. In addition, two other PID controllers are designed to get a better understanding of the performance and robustness of the proposed controller. To make a complete comparison, the performance of the hybrid particle swarm optimization algorithm is examined against two other optimization techniques known as simple particle swarm optimization and whale optimization algorithm. Finally, the obtained simulation results demonstrate that the proposed optimal adaptive controller is superior to the other controllers, especially in terms of the transient response characteristics and the magnitude of control output signal.
引用
收藏
页码:1492 / 1510
页数:19
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