MRAC Based PID Controller Design with Genetic Algorithm for a Single Joint Robot Arm

2021 ◽  
Vol 9 (2) ◽  
pp. 86
Author(s):  
Yazen Hudhaifa Alnema ◽  
Abdullah I. Alsabawee ◽  
Jamal M. Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Controller Design ◽  
Robot Arm ◽  
Pid Controller Design

Optimal fuzzy PID controller design for an active magnetic bearing system based on adaptive genetic algorithms

2014 ◽  
Vol 24 (5) ◽  
Author(s):  
HUNG-CHENG CHEN
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Controller Design ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Adaptive Genetic Algorithm ◽  
Fuzzy Pid ◽  
Fuzzy Pid Controller ◽  
Pid Controller Design ◽  
Amb System

We propose an adaptive genetic algorithm (AGA) for the multi-objective optimisation design of a fuzzy PID controller and apply it to the control of an active magnetic bearing (AMB) system. Unlike PID controllers with fixed gains, a fuzzy PID controller is expressed in terms of fuzzy rules whose consequences employ analytical PID expressions. The PID gains are adaptive and the fuzzy PID controller has more flexibility and capability than conventional ones. Moreover, it can be easily used to develop a precise and fast control algorithm in an optimal design. An adaptive genetic algorithm is proposed to design the fuzzy PID controller. The centres of the triangular membership functions and the PID gains for all fuzzy control rules are selected as parameters to be determined. We also present a dynamic model of an AMB system for axial motion. The simulation results of this AMB system show that a fuzzy PID controller designed using the proposed AGA has good performance.

scholarly journals Optimal PID controller design using artificial bee colony algorithm for robot arm

2021 ◽  
Vol 21 (1) ◽  
pp. 84
Author(s):  
Ghassan A. Sultan ◽  
Muhammed K. Jarjes
Keyword(s):  
Pid Controller ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Controller Design ◽  
Dynamic Performance ◽  
Control Process ◽  
Robot Arm ◽  
Bee Colony ◽  
Pid Controller Design ◽  
Best Parameter

<p class="Default"><span>Proportional integral derivation (PID) controller is used in this paper for optimal design, and tuning by zeigler and nichol (ZN) with artificial bee colony algorithm. The best parameter were found using these algorithms for best performance of a robot arm. The advantage of using ABC were highlighted. The controller using the new algorithm was tested for valid control process. Different colony size has been performed for tuning process, settling time, from time domain performance, rise time, overshot, and steady state error with ABC tuning give better dynamic performance than controller using the (ZN).</span></p>

scholarly journals A fuzzy adaptive controller for cuckoo search algorithm in active suspension system

2018 ◽  
Vol 39 (3) ◽  
pp. 761-771
Author(s):  
Chun-Tang Chao ◽  
Ming-Tang Liu ◽  
Chi-Jo Wang ◽  
Juing-Shian Chiou
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Optimization Problem ◽  
Controller Design ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Local Optima ◽  
Pid Controller Design ◽  
Fuzzy Adaptive

This paper presents a fuzzy adaptive cuckoo search algorithm to improve the cuckoo search algorithm, which may easily fall into a local optimum when handling multiobjective optimization problems. The Fuzzy–Proportional-Integral-Derivative (PID) controller design for an active micro-suspension system has been incorporated into the proposed fuzzy adaptive cuckoo search algorithm to improve both driving comfort and road handling. In the past research, a genetic algorithm was often applied in Fuzzy–PID controller design. However, when the dimension is high and there are numerous local optima, the traditional genetic algorithm will not only have a premature convergence, but may also be trapped in the local optima. In the proposed fuzzy adaptive cuckoo search, all parameters of the PID controller and fuzzy rules are real coded to 75 bits in the optimization problem. Moreover, a fuzzy adaptive strategy is proposed for dynamically adjusting the learning parameters in the fuzzy adaptive cuckoo search, and this indeed enables global convergence. Experimental results verify that the proposed fuzzy adaptive cuckoo search algorithm can shorten the computing time in the evolution process and increase accuracy in the multiobjective optimization problem.

scholarly journals PID Controller Tuning Optimization with Genetic Algorithms for a Quadcopter

2018 ◽  
Vol 5 (1.) ◽  
Author(s):  
Komal Khuwaja ◽  
Noor-u-Zaman Lighari ◽  
Ioan Constantin Tarca ◽  
Radu Catalin Tarca
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Controller Design ◽  
Sliding Mode ◽  
Gain Control ◽  
Nonlinear Gain ◽  
Pid Controller Tuning ◽  
Pid Controller Design ◽  
The Stability ◽  
Stability And Accuracy

This paper is focused on the dynamic of mathematical modeling, stability, nonlinear gain control by using Genetic algorithm, utilizing MATLAB tool of a quadcopter. Previously many researchers have been work on several linear controllers such as LQ method; sliding mode and classical PID are used to stabilize the Linear Model. Quadcopter has a nonlinear dynamics and unstable system. In order to maintain their stability, we use nonlinear gain controllers; classical PID controller provides linear gain controller rather than nonlinear gain controller; here we are using modified PID control to improve stability and accuracy. The stability is the state of being resistant to any change. The task is to maintain the quadcopter stability by improving the performance of a PID controller in term of time domain specification. The goal of PID controller design is to determine a set of gains: Kp, Ki, and Kd, so as to improve the transient response and steady state response of a system as: by reducing the overshoot; by shortening the settling time; by decrease the rise time of the system. Modified PID is the combination of classical PID in addition to Genetic Algorithm. Genetic algorithm consists of three steps: selection, crossover, and mutation. By using Genetic algorithm we correct the behavior of quadcopter.

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