Investigating the effect of GA based PID controller performance indices with application to stirred-tank heater (jacket model)

2016 ◽  
Author(s):  
Najeh Ibrahim Allafi ◽  
Ali S. Zayed ◽  
Mohamed Shtewi Daw ◽  
Wesam M. Ahmed
Keyword(s):  
Pid Controller ◽  
Stirred Tank ◽  
Performance Indices ◽  
Controller Performance

scholarly journals Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
A. Jayachitra ◽  
R. Vinodha
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Absolute Error ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Operating Range ◽  
Continuous Stirred Tank ◽  
Dynamic Nonlinearity

Genetic algorithm (GA) based PID (proportional integral derivative) controller has been proposed for tuning optimized PID parameters in a continuous stirred tank reactor (CSTR) process using a weighted combination of objective functions, namely, integral square error (ISE), integral absolute error (IAE), and integrated time absolute error (ITAE). Optimization of PID controller parameters is the key goal in chemical and biochemical industries. PID controllers have narrowed down the operating range of processes with dynamic nonlinearity. In our proposed work, globally optimized PID parameters tend to operate the CSTR process in its entire operating range to overcome the limitations of the linear PID controller. The simulation study reveals that the GA based PID controller tuned with fixed PID parameters provides satisfactory performance in terms of set point tracking and disturbance rejection.

Fuzzy Control Tuning Method of Digital PID Parameters

2012 ◽  
Vol 499 ◽  
pp. 469-473
Author(s):  
Yan Zhong Huo ◽  
Guo Ling Niu ◽  
Shi Jun Ma ◽  
Xu Du
Keyword(s):  
Fuzzy Control ◽  
Pid Controller ◽  
Control Method ◽  
Parameter Tuning ◽  
Control Technology ◽  
Tuning Method ◽  
Parameters Tuning ◽  
Digital Pid ◽  
Controller Performance ◽  
Pros And Cons

As a control method, PID control is the most widely used in industrial processes. However, PID controller parameter tuning of the pros and cons of PID controller performance has been an important factor. Fuzzy control technology is an advanced intelligent control technology, because of its advanced features and easy implementation, it can develop rapidly. This paper describes the theory and method of fuzzy control to realize the dynamic PID controller parameters tuning approach to the PID controller to achieve the best control performance.

scholarly journals Automatic Load Frequency Control of a Multi-Area Dynamic Interconnected Power System Using a Hybrid PSO-GSA-Tuned PID Controller

2019 ◽  
Vol 11 (24) ◽  
pp. 6908 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Izzri Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Arangarajan Vinayagam ◽  
Mohammad Lutfi Othman ◽  
...  
Keyword(s):  
Steady State ◽  
Power System ◽  
Pid Controller ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Load Frequency Control ◽  
Power Coefficient ◽  
Performance Indices ◽  
Control Effort ◽  
Interconnected Power System

This paper proposes a new population-based hybrid particle swarm optimized-gravitational search algorithm (PSO-GSA) for tuning the parameters of the proportional-integral-derivative (PID) controller of a two-area interconnected dynamic power system with the presence of nonlinearities such as generator rate constraints (GRC) and governor dead-band (GDB). The tuning of controller parameters such as Kp, Ki, and Kd are obtained by minimizing the objective function formulated using the steady-state performance indices like Integral absolute error (IAE) of tie-line power and frequency deviation of interconnected system. To test the robustness of the propounded controller, the system is studied with system uncertainties, such as change in load demand, synchronizing power coefficient and inertia constant. The two-area interconnected power system (TAIPS) is modeled and simulated using Matlab/Simulink. The results exhibit that the steady-state and transient performance indices such as IAE, settling time, and control effort are impressively enhanced by an amount of 87.65%, 15.39%, and 91.17% in area-1 and 86.46%, 41.35%, and 91.04% in area-2, respectively, by the proposed method compared to other techniques presented. The minimum control effort of PSO-GSA-tuned PID controller depicts the robust performance of the controller compared to other non-meta-heuristic and meta-heuristic methods presented. The proffered method is also validated using the hardware-in-the-loop (HIL) real-time digital simulation to study the effectiveness of the controller.

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