Optimal Design of PID Process Controllers based on Genetic Algorithms

Wang, P.; Kwok, D.P.

doi:10.1016/s1474-6670(17)48452-7

Cited by 53 publications

(30 citation statements)

References 7 publications

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“…The model equations given above are derived from first principles [17]. the objectives is that the output temperature of the cold fluid ‗T 0 ' to track the step input change in hot fluid inlet flow rate ‗  h '.…”

Section: Dynamical Equations Of Heat Exchanger Systemmentioning

confidence: 99%

“…Most implementations of the PSO usually have w equal to one or each particle always trusts itself. Variable dir directs the velocity of a swarm being updated by attraction -addition (+1)‖ or repulsion -subtraction (-1).‖ The current direction and the diversity value of the swarm greater or lower than a threshold value [17] determine if the swarm will be updated in an attraction or repulsion manner. Variable S symbolizes the swarm at discrete epochs of time.…”

Section: Overview Of Particle Swarm Optimization and Its Variants 31mentioning

confidence: 99%

“…For example, disadvantage of the IAE and ISE criteria is that its minimization can result in a response with relatively small overshoot but a long settling time because the ISE performance criterion weights all errors equally independent of time. Although the ITSE performance criterion can overcome the disadvantage of the ISE criterion, the derivation processes of the analytical formula are complex and time-consuming [17]. …”

Section: Fitness Functionmentioning

confidence: 99%

See 2 more Smart Citations

Swarm Optimization based Controller for Temperature Control of a Heat Exchanger

Rajasekaran¹,

Kannadasan²

2012

IJCA

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In This paper uses an Attractive-Repulsive Particle Swarm Optimization (ARPSO) method for determining the optimal parameters of proportional-integral-derivative (PID) controller for temperature control of a shell and tube heat exchanger. Most of the heat exchange process is characteristic of nonlinear, large time delay and time varying. For such process it is very difficult to tune its controller parameters based on traditional PID tuning. The proposed method has excellent features, including high computational efficiency, quick stable convergence and easy implementation than standard PSO. In the proposed system, the ARPSO is implemented by MATLAB and compared with standard PSO and Genetic Algorithm (GA). The result shows that the proposed system has a more efficient in improving the step response characteristics such as reducing the steady state error, rise time; settling time and maximum peak overshoot in temperature control of a shell and tube heat exchanger.

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Section: Dynamical Equations Of Heat Exchanger Systemmentioning

confidence: 99%

Section: Overview Of Particle Swarm Optimization and Its Variants 31mentioning

confidence: 99%

Section: Fitness Functionmentioning

confidence: 99%

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Swarm Optimization based Controller for Temperature Control of a Heat Exchanger

Rajasekaran¹,

Kannadasan²

2012

IJCA

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“…Therefore many optimization methods are developed to tune the PID controllers such as fuzzy logic [2,3], neural network [4], neural-fuzzy logic [5], immune algorithm [6], simulated annealing [7], and pattern recognition [8]. In addition, we have many other optimum tuning PID methods based on many random search methods such as genetic algorithm (GA) [9,10], particle swarm optimization [11], and ant colony optimization [12].…”

Section: Introductionmentioning

confidence: 99%

Tuning PID Controller Using Multiobjective Ant Colony Optimization

Chiha¹,

Liouane

Borne

2012

Applied Computational Intelligence and Soft Computing

112

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This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (K p , K i , and K d ) by minimizing the multiobjective function. The potential of using multiobjective ant algorithms is to identify the Pareto optimal solution. The other methods are applied to make comparisons between a classic approach based on the "Ziegler-Nichols" method and a metaheuristic approach based on the genetic algorithms. Simulation results demonstrate that the new tuning method using multiobjective ant colony optimization has a better control system performance compared with the classic approach and the genetic algorithms.

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“…Considering the limitations of the Ziegler-Nichols method and some empirical techniques in raising the performance of PID controller, recently artificial intelligence techniques such as fuzzy logic [18,21], fuzzy neural network [2,10], and some stochastic search and optimization algorithms such as simulated annealing [20], genetic algorithm [11,19], particle swarm optimization approach [4], immune algorithm [14], and ant colony optimization [6] have been applied to improve the performances of PID controllers. In these studies, it has been shown that these approaches provide good solutions in tuning the parameters of PID controllers.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Particle Swarm Optimization Applied to Optimum Controller Design for AVR Power Systems

Aghababa¹,

Shotorbani²,

Shotorbani³

2010

IJCA

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This paper describes an improved version of particle swarm optimization (PSO) method, called adaptive particle swarm optimization (APSO), for solving engineering optimization problems especially in power system fields. This algorithm uses a novel PSO algorithm to increase convergence rate and avoid being trapped in local optimum. The APSO algorithm efficiency is verified using some benchmark functions. Numerical simulation results demonstrate that the APSO is fast and has much less computational cost. Then, the proposed APSO method is used for determining the parameters of the optimal proportional-integralderivative (PID) controller for an AVR power system. The proposed approach has superior features including easy implementation, stable and fast convergence characteristics and good computational efficiency. Also, the proposed method is indeed more efficient and robust in improving the step response of the AVR system.

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Optimal Design of PID Process Controllers based on Genetic Algorithms

Cited by 53 publications

References 7 publications

Swarm Optimization based Controller for Temperature Control of a Heat Exchanger

Swarm Optimization based Controller for Temperature Control of a Heat Exchanger

Tuning PID Controller Using Multiobjective Ant Colony Optimization

An Adaptive Particle Swarm Optimization Applied to Optimum Controller Design for AVR Power Systems

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