Multiloop FOPID Controller Design for TITO Process Using Evolutionary Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 117-130 ◽  
Author(s):  
Lakshmanaprabu S.K. ◽  
Najumnissa Jamal D. ◽  
Sabura Banu U.
Keyword(s):  
Evolutionary Algorithm ◽  
Pid Controller ◽  
Controller Design ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Internal Model Control ◽  
Model Control ◽  
Fractional Order Pid

In this article, the tuning of multiloop Fractional Order PID (FOPID) controller is designed for Two Input Two Output (TITO) processes using an evolutionary algorithm such as the Genetic algorithm (GA), the Cuckoo Search algorithm (CS) and the Bat Algorithm (BA). The control parameters of FOPID are obtained using GA, CS, and BA for minimizing the integral error criteria. The main objective of this article is to compare the performance of the GA, CS, and BA for the multiloop FOPID controller problem. The integer order internal model control based PID (IMC-PID) controller is designed using the GA and the performance of the IMC-PID controller is compared with the FOPID controller scheme. The simulation results confirm that BA offers optimal controller parameter with a minimum value of IAE, ISE, ITAE with faster settling time.

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 A rubust fractional order PID controller for MIMO power systems

2019 ◽  
Vol 87 ◽  
pp. 01024
Author(s):  
Vasampalli Shashidhar ◽  
Dola Gobinda Padhan
Keyword(s):  
Power Systems ◽  
Fractional Order ◽  
Pid Controller ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Distribution Networks ◽  
Cuckoo Search Algorithm ◽  
Search Optimization ◽  
Control Scheme ◽  
Fractional Order Pid

This paper proposes a novel fractional order PID control scheme for Multi Input and Multi Output (MIMO) power systems. This control scheme utilizes Cuckoo Search algorithm to tune the fractional PID controller to guarantee better closed loop performance in the transmission or distribution networks. Cuckoo Search optimization algorithm is proposed to optimize gain values of the fractional PID controller. The effectiveness of the proposed control strategy has been assessed by simulations in MATLAB/Simulink platform. The robustness of the proposed controller has been validated by varying the plant parameters.

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

2021 ◽  
pp. 014233122110196
Author(s):  
Davut Izci
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Absolute Error ◽  
Dc Motor ◽  
Cuckoo Search Algorithm ◽  
Lévy Flight ◽  
Motor Speed ◽  
Levy Flight ◽  
Speed Regulation

This paper deals with the design of an optimally performed proportional–integral–derivative (PID) controller utilized for speed control of a direct current (DC) motor. To do so, a novel hybrid algorithm was proposed which employs a recent metaheuristic approach, named Lévy flight distribution (LFD) algorithm, and a simplex search method known as Nelder–Mead (NM) algorithm. The proposed algorithm (LFDNM) combines both LFD and NM algorithms in such a way that the good explorative behaviour of LFD and excellent local search capability of NM help to form a novel hybridized version that is well balanced in terms of exploration and exploitation. The promise of the proposed structure was observed through employment of a DC motor with PID controller. Optimum values for PID gains were obtained with the aid of an integral of time multiplied absolute error objective function. To verify the effectiveness of the proposed algorithm, comparative simulations were carried out using cuckoo search algorithm, genetic algorithm and original LFD algorithm. The system behaviour was assessed through analysing the results for statistical and non-parametric tests, transient and frequency responses, robustness, load disturbance, energy and maximum control signals. The respective evaluations showed better performance of the proposed approach. In addition, the better performance of the proposed approach was also demonstrated through experimental verification. Further evaluation to demonstrate better capability was performed by comparing the LFDNM-based PID controller with other state-of-the-art algorithms-based PID controllers with the same system parameters, which have also confirmed the superiority of the proposed approach.

scholarly journals Position Tracking of ML System using CSA Based PID Controller

2019 ◽  
Vol 8 (11) ◽  
pp. 1956-1959
Keyword(s):  
Pid Controller ◽  
Magnetic Levitation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Pid Controllers ◽  
Position Tracking ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Simulation Results

The classical proportional integral derivative (PID) controllers are still use in various applications in industry. Magnetic levitation (ML) systems are rigidly nonlinear and sometimes unstable systems. Due to inbuilt nonlinearities of ML systems, tracking of position of ML Systems is still difficult. For the tracking purpose of position, PID controller parameters are found by choosing Cuckoo Search Algorithm (CSA) of optimization. The ranges of parameters are customized by z-n method of parameters. Simulation results show the tracking of position of ML systems using conventional and optimized parameters obtained with the CSA based controller.

A Survey of Computational Intelligence Algorithms and Their Applications

2020 ◽  
pp. 1886-1929
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Computational Intelligence ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Evolutionary Strategy ◽  
General Process ◽  
Fireworks Algorithm ◽  
Starting Point ◽  
New Algorithms

This chapter subscribes in the framework of an analytical study about the computational intelligence algorithms. These algorithms are numerous and can be classified in two great families: evolutionary algorithms (genetic algorithms, genetic programming, evolutionary strategy, differential evolutionary, paddy field algorithm) and swarm optimization algorithms (particle swarm optimisation PSO, ant colony optimization (ACO), bacteria foraging optimisation, wolf colony algorithm, fireworks algorithm, bat algorithm, cockroaches colony algorithm, social spiders algorithm, cuckoo search algorithm, wasp swarm optimisation, mosquito optimisation algorithm). We have detailed each algorithm following a structured organization (the origin of the algorithm, the inspiration source, the summary, and the general process). This paper is the fruit of many years of research in the form of synthesis which groups the contributions proposed by various researchers in this field. It can be the starting point for the designing and modelling new algorithms or improving existing algorithms.

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