PID Controller Tuning based on Metaheuristic Algorithms for Bioprocess Control

In order to provide the comfortable areas for human, the comfortable rooms are the basic needs area, controlled temperature, and relative humidity (RH). The aim of this study is to control and maintain the temperature and RH of the comfortable room using a proportional integral derivative (PID) controller tuned by metaheuristic optimization. In tuning gains of the PID controller, the modern metaheuristic optimizations, ant colony optimization (ACO), and symbiotic organism search (SOS) are applied and the performance of the proposed control system is compared to that of the traditional methods. In the experimental testing, the controlled room size is tested in the area of width 7.80 m, length 8.00 m, and height 3.80 m. The simulation results show that the performance of the proposed control system-tuned gains of PID controllers by using SOS algorithm has the least steady-state error with 15% rise time and also the overshoot can reach the setpoint. In the case of disturbance occurring in the system, the proposed control system is able to approach the setpoint. Therefore, the PID controller tuned by SOS algorithm can regulate the temperature and humidity of the comfortable room, proficiently.

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“…e variant of PID adjusts according to the nature of the system. Block diagram of PID controller is shown in Figure 6 [26].…”

Section: Pid Controller Pid Controller Is a Feedback Control System mentioning

confidence: 99%

Intelligent Tuning of PID Using Metaheuristic Optimization for Temperature and Relative Humidity Control of Comfortable Rooms

Janprom¹,

Permpoonsinsup²,

Wangnipparnto³

2020

Journal of Control Science and Engineering

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“…With the development of the intelligent control theory, intelligent optimization algorithms started to be applied in PID parameter tuning and optimizing and achieved incomparable results that the conventional parameter tuning methods cannot obtain. For example, the genetic algorithm (GA) [14][15][16], particle swarm optimization (PSO) [17][18][19], tabu search algorithm (TSA) [20][21][22], bacterial foraging algorithm (BFA) [23][24][25], ant colony algorithm (ACA) [26], artificial bee colony (ABC) algorithm [27], and BAT search algorithm [28] were adopted to optimize PID controller parameters and had achieved much better performances.…”

Section: Introductionmentioning

confidence: 99%

An MEA‐Tuning Method for Design of the PID Controller

Zhang

Dong

2019

Mathematical Problems in Engineering

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The optimization and tuning of parameters is very important for the performance of the PID controller. In this paper, a novel parameter tuning method based on the mind evolutionary algorithm (MEA) was presented. The MEA firstly transformed the problem solutions into the population individuals embodied by code and then divided the population into superior subpopulations and temporary subpopulations and used the similar taxis and dissimilation operations for searching the global optimal solution. In order to verify the control performance of the MEA, three classical functions and five typical industrial process control models were adopted for testing experiments. Experimental results indicated that the proposed approach was feasible and valid: the MEA with the superior design feature and parallel structure could memorize more evolutionary information, generate superior genes, and enhance the efficiency and effectiveness for searching global optimal parameters. In addition, the MEA-tuning method can be easily applied to real industrial practices and provides a novel and convenient solution for the optimization and tuning of the PID controller.

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“…Therefore, although PID controllers are sufficient to solve the control problem of many applications in industry, for bioprocesses they do not give good results with traditional tuning procedures. Few works in this field have claimed satisfactory results tuning the parameters based on alternative approaches such as: neural networks (Andrášik et al, 2004) and metaheuristic algorithms (Roeva and Slavov, 2012) to deal with the inherent complex behavior of bioprocess.…”

Section: Introductionmentioning

confidence: 99%

State Estimation and Trajectory Tracking Control for a Nonlinear and Multivariable Bioethanol Production System

Fernández¹,

Pantano²,

Rossomando³

et al. 2019

Braz. J. Chem. Eng.

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In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves finding feed rate profiles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefined concentration profiles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defined. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.

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PID Controller Tuning based on Metaheuristic Algorithms for Bioprocess Control

Cited by 26 publications

References 16 publications

Intelligent Tuning of PID Using Metaheuristic Optimization for Temperature and Relative Humidity Control of Comfortable Rooms

Intelligent Tuning of PID Using Metaheuristic Optimization for Temperature and Relative Humidity Control of Comfortable Rooms

An MEA‐Tuning Method for Design of the PID Controller

State Estimation and Trajectory Tracking Control for a Nonlinear and Multivariable Bioethanol Production System

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