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Journal ArticleDOI

PID controller tuning using metaheuristic optimization algorithms for benchmark problems

01 Nov 2017-Vol. 263, Iss: 5, pp 052021
TL;DR: The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm.
Abstract: This paper contributes to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned. Here, two different fitness functions such as Integral Time Absolute Error and Time domain Specifications were chosen and applied on PSO, GA and SA while tuning the controller. The proposed Algorithms are implemented on two benchmark problems of coupled tank system and DC motor. Finally, comparative study has been done with different algorithms based on best cost, number of iterations and different objective functions. The closed loop process response for each set of tuned parameters is plotted for each system with each fitness function.
Citations
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Journal ArticleDOI
01 May 2022-Heliyon
TL;DR: A thorough review of state-of-the-art and classical strategies for PID controller parameters tuning using metaheuristic algorithms can be found in this article , where the primary objectives of PID control parameters are to achieve minimal overshoot in steady state response and lesser settling time.

62 citations

Patent
20 Oct 2017
TL;DR: In this paper, a PID controller parameter setting algorithm based on an improved PSO (particle swarm optimization) algorithm is presented, and the algorithm comprises the following steps: 1, initializing the algorithm parameters; 2, switching to an iterative loop, and carrying out the updating of the position and speed of each particle; 3, randomly searching a new position in the neighborhood of a current position; 4, calculating the adaptability difference between two positions, and judging whether to accept the new position or not through a simulated annealing mechanism when the adaptation is inferior to the
Abstract: The invention discloses a PID controller parameter setting algorithm based on an improved PSO (particle swarm optimization) algorithm, and the algorithm comprises the following steps: 1, initializing the algorithm parameters; 2, switching to an iterative loop, and carrying out the updating of the position and speed of each particle; 3, randomly searching a new position in the neighborhood of a current position; 4, calculating the adaptability difference between two positions, and judging whether to accept the new position or not through a simulated annealing mechanism when the adaptability of the new position is inferior to the adaptability of an original position but is superior to the adaptability of a global optimal position; 5, updating the global optimal position of a population, carrying out the natural selection operation, carrying out the arrangement of all particles according to the adaptability values, and employing the information of a part of better particles to replace the information of the other half particles; 6, judging whether to stop the iteration or not; 7, outputting PID controller parameters or executing step 2 again. The method can achieve the automatic setting of control parameters, irons out a defect that a conventional PSO algorithm is very liable to be caught in local optimization, achieves the complementation of the simulated annealing operation and a natural selection strategy, improves the convergence precision of the algorithm under the condition that the number of convergence times of the algorithm is guaranteed, is higher in robustness and precision, and enables the PID controller to generate a more excellent control effect.

12 citations

Journal ArticleDOI
TL;DR: A surrogate‐based robust simulation‐optimization approach for robust tuning and analyzing the sensitivity of stochastic controllers and results confirmed a proper trade‐off between the model's performance with the measure of expected Integral Squared Error and robustness against uncertainty in the plant's physical parameters.
Abstract: This paper aims to make a trade‐off between performance and robustness in stochastic control systems with probabilistic uncertainties. For this purpose, we develop a surrogate‐based robust simulation‐optimization approach for robust tuning and analyzing the sensitivity of stochastic controllers. Kriging surrogate is combined with robust design optimization to construct a robust simulation‐optimization model in the class of dual response surfaces. Randomness in simulation experiments due to uncertainty is analyzed through bootstrapping technique by computing confidence regions for the estimation of Pareto frontier. Results confirmed a proper trade‐off between the model's performance with the measure of expected Integral Squared Error (ISE) and robustness against uncertainty in the plant's physical parameters. Finally, the proposed method is evaluated in terms of accuracy, computational cost, and simplicity particularly in comparison with some common existed techniques in the tuning of the Proportional‐Integral‐Derivative (PID) and Fractional‐Order PID (FOPID) controllers.

3 citations

Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper proposed a framework of operational stability and its control strategies to evaluate and reduce the robot's overturning probability, analyzes qualitatively the operational stability using zero-point moments and fuzzy theories, and implements a hybrid genetic algorithm-particle swarm optimization (GA-PSO) and proportional integral derivative (PID) controller.
Journal ArticleDOI
TL;DR: In this article , the grey wolf optimization (GWO) algorithm and the P-PI cascade controller are combined to enhance the motion performance of dual-ball-screw feed drive systems (DBSFDSs).
Abstract: Dual-ball-screw feed drive systems (DBSFDSs) are designed for most high-end manufacturing equipment. However, the mismatch between the dynamic characteristic parameters (e.g., stiffness and inertia) and the P-PI cascade control method reduces the accuracy of the DBSFDSs owing to the structural characteristic changes in the motion. Moreover, the parameters of the P-PI cascade controller of the DBSFDSs are always the same even though the two axes have different dynamic characteristics, and it is difficult to tune two-axis parameters simultaneously. A new application of the combination of the grey wolf optimization (GWO) algorithm and the P-PI cascade controller is presented to solve these problems and enhance the motion performance of DBSFDSs. The novelty is that the flexible coupling model and dynamic stiffness obtained from the motor current can better represent the two-axis coupling dynamic characteristics, and the GWO algorithm is used to adjust the P-PI controller parameters to address variations in the positions of the moving parts and reflect characteristic differences between the two axes. Comparison of simulation and experimental results validated the superiority of the proposed controller over existing ones in practical applications, showing a decrease in the tracking error of the tool center and non-synchronization error of over 34% and 39%, respectively.
References
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Journal ArticleDOI
TL;DR: In this paper, a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems is proposed, and the experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
Abstract: This paper proposes a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zone, and nonsmooth cost functions are considered using the proposed method in practical generator operation. The feasibility of the proposed method is demonstrated for three different systems, and it is compared with the GA method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.

1,635 citations

Book
17 Jan 2000
TL;DR: Nise applies control systems theory and concepts to current real-world problems, showing readers how to build control systems that can support today's advanced technology.
Abstract: From the Publisher: Designed to make the material easy to understand, this clear and thorough book emphasizes the practical application of systems engineering to the design and analysis of feedback systems. Nise applies control systems theory and concepts to current real-world problems, showing readers how to build control systems that can support today's advanced technology.

1,253 citations

Journal ArticleDOI
TL;DR: In this article, a successful adaptation of the particle swarm optimisation (PSO) algorithm to solve various types of economic dispatch (ED) problems in power systems such as, multi-area ED with tie line limits, ED with multiple fuel options, combined environmental economic dispatch, and the ED of generators with prohibited operating zones.

227 citations

Proceedings ArticleDOI
03 Mar 2016
TL;DR: In this paper, a Proportional-Integral-Derivative (PID) controller for the cruise control system has been proposed, which is a highly nonlinear, has been linearized around the equilibrium point.
Abstract: In this paper, the design of a Proportional-Integral-Derivative (PID) controller for the cruise control system has been proposed. The cruise control system, which is a highly nonlinear, has been linearized around the equilibrium point. The controller has been designed for the linearized model, by taking the dominant pole concept in the closed loop characteristic equation. The PID controller parameters, i.e. proportional, integral and derivative parameters have been tuned using Genetic Algorithm (GA). In this study, the performance of the controller has been compared with that of the conventional PID, state space and Fuzzy logic based controller. The simulation output reveals the superiority of the proposed controller in terms of maximum overshoot, peak time, rise time, settling time and steady state error. The sensitivity and complementary sensitivity analysis show the robust behaviour of the system with output disturbance and high-frequency noise rejection qualities. As a scope of further research, fractional order and 2-dof PID controller will be designed for this cruise control system and the performance will be compared with this design.

37 citations

Proceedings ArticleDOI
01 Aug 2016
TL;DR: It can be found that TSI not only helps the existing optimization algorithms to start at a better optima but also to converge at a lesser minimum value of the objective function, which is often desirable.
Abstract: In this paper, a different type of initialization technique called the two stage initialization (TSI) is used for initializing the population vectors of differential evolution (DE) and particle swarm optimization (PSO) These two stage initialized optimization algorithms are then used to tune the PID controller for a coupled tank liquid level control system In TSI, the population vector is randomly generated in two stages which would then go through the various phases involved in the algorithms The PID controller is the most preferred controller in almost all process control industries due to its robustness and dynamic behavior Hence it has been chosen for controlling the coupled tank liquid level system in this paper Comparison of the application of four optimization algorithms out of which two are already existing ones and the other two are their TSI versions to PID-coupled tank liquid level system is also done It can be found that TSI not only helps the existing optimization algorithms to start at a better optima but also to converge at a lesser minimum value of the objective function, which is often desirable

13 citations