Fuzzy Gain Scheduled Continuous Stirred Tank Reactor with Particle Swarm Optimization based PID control Minimizing Integral Square Error

ISRN Artificial Intelligence

Surekha

2013

Nonlinear processes are very common in process industries, and designing a stabilizing controller is always preferred to maximize the production rate. In this paper, tuning of PID controller for a class of time delayed stable and unstable process models using Particle Swarm Optimization (PSO) algorithm is discussed. The dimension of the search space is only three (, , and ); hence, a fixed weight is assigned for the inertia parameter. A comparative study is presented between various inertia weights such as 0.5, 0.75, and 1. From the result, it is evident that the proposed method helps to attain better controller settings with reduced iteration number. The efficacy of the proposed scheme has been validated through a comparative study with classical controller tuning methods and heuristic methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Finally, a real-time implementation of the proposed method is carried on a nonlinear spherical tank system. From the simulation and real-time results, it is evident that the PSO algorithm performs well on the stable and unstable process models considered in this work. The PSO tuned controller offers enhanced process characteristics such as better time domain specifications, smooth reference tracking, supply disturbance rejection, and error minimization.

Section: Pso Algorithm Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…Step 5 (update the swarm). The particle position is updated using (6). The values of the objective function are calculated for updated positions of particles.…”

Section: Pso Algorithm Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PSO-Based PID Controller Design for a Class of Stable and Unstable Systems

ISRN Artificial Intelligence

Surekha

2013

“…In recent years, evolutionary approach-based controller autotuning methods has attracted the control engineers and the researchers due to it is nonmodel-based approach, simplicity, high computational efficiency, easy implementation, and stable convergence [9][10][11][12][13]. In this paper, the I-PD controller tuning is proposed for unstable system using the Bacteria Foraging Optimization (BFO) algorithm introduced by Passino [14].…”

Section: Introductionmentioning

confidence: 99%

I-PD Controller Tuning for Unstable System Using Bacterial Foraging Algorithm: A Study Based on Various Error Criterion

Applied Computational Intelligence and Soft Computing

2012

This paper proposes a novel method to tune the I-PD controller structure for the time-delayed unstable process (TDUP) using Bacterial Foraging Optimization (BFO) algorithm. The tuning process is focussed to search the optimal controller parameters (, , ) by minimising the multiple objective performance criterion. A comparative study on various cost functions like Integral of Squared Error (ISE), Integral of Absolute Error (IAE), Integral of Time-weighted Squared Error (ITSE), and Integral of Time weighted Absolute Error (ITAE) have been attempted for a class of TDUP. A simulation study for BFO-based I-PD tuning has been done to validate the performance of the proposed method. The results show that the tuning approach is a model independent approach and provides enhanced performance for the setpoint tracking with improved time domain specifications.

“…They have proposed a multiobjective PSO algorithm to tune the H ∞ PID controller for a nonlinear system [26]. Banu and Uma have discussed a hybrid algorithm-based PID controller implementation for a nonlinear CSTR [27]. Kanth and Latha have attempted a PSO-based PID and I-PD controller-tuning for a class of unstable systems [28,29].…”

Section: Introductionmentioning

confidence: 99%

Modeling, Analysis, and Intelligent Controller Tuning for a Bioreactor: A Simulation Study

ISRN Chemical Engineering

2012

In this paper, a novel modeling technique has been attempted to develop the mathematical model for a bioreactor functioning at multiple operating regions. The first principle mathematical equations of the reactor are used with the POLYMATH software to generate essential data for the model development. A relative analysis is also carried out with the existing models in the literature. An optimal PID controller is then designed using a multiobjective particle swarm optimization algorithm. The controller tuning procedure is individually discussed for both the stable and unstable steady state regions. The controller tuned for each region is scheduled using a set-point scheduler to achieve a complete control over the bioreactor. The effectiveness of the proposed scheme has been confirmed through a comparative study with the controller tuning methods proposed in the literature. The results show that, the proposed method provides enhanced performance in effective reference tracking and load disturbance rejection with minimal ISE and IAE. Finally the proposed method is validated on the nonlinear bioreactor model in the presence of a measurement noise. The results testify that the PSO tuned PID performs well in tracking the change in biomass concentration at the entire operating region.