K. Latha

Otsu based optimal multilevel image thresholding using firefly algorithm

Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD) guided firefly algorithm (FA). A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsus between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Levy flight (LF) guided FA and random operator guided FA. The performance assessment comparison between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR), structural similarity (SSIM) index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.

Controller parameter optimization for nonlinear systems using enhanced bacteria foraging algorithm

An enhanced bacteria foraging optimization (EBFO) algorithm-based Proportional + integral + derivative (PID) controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.

I-PD controller tuning for unstable system using bacterial foraging algorithm: a study based on various error criterion

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 (Kp, Ki, Kd) 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.

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

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.

Tuning and Retuning of PID Controller for Unstable Systems Using Evolutionary Algorithm

Proportional + integral + derivative (PID) controllers are widely used in industrial applications to provide optimal and robust performance for stable, unstable, and nonlinear processes. In this paper, particle swarm optimization (PSO) algorithm is proposed to tune and retune the PID controller parameter for a class of time-delayed unstable systems. The proposal is to search the optimal controller parameters like 𝐾 𝑝 , 𝐾 𝑖 , and 𝐾 𝑑 by minimising the cost function. The integral of squared error (ISE) criterion is considered as the cost function, which guides the PSO algorithm to get the optimised controller parameters. The procedure for PID parameter tuning and retuning is presented in detail. A comparative study is done with the conventional PID tuning methods proposed in the literature. The simulation results show that the PSO-based PID controller tuning approach provides improved performance for the setpoint tracking, load disturbance rejection, error minimization, and measurement noise attenuation for a class of unstable systems.

Identification of Resonance in Hybrid Stepper Motor Through Measured Current Dynamics in Online for Accurate Position Estimation and Control

Stepper motors suffer with skipped steps due to the occurrence of resonance and instability, under certain load conditions. Unlike in other machines, variation of torque is not reflected in the current. Hence the current measurement based position estimation techniques fail. Experimental investigations reveal that high frequency ripples are present in the current dynamics during the resonating periods. This paper proposes an identification technique which works based on ripple content during the transient period of the current rise. It is shown that resonance can be identified and compensated well before the motor misses the step through the excitation voltage. With such compensation, current measurement based estimation techniques provide accurate speed & position estimation. The paper also matches experimental results with the simulation.

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

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.

Particle Swarm Approach for Identification of Unstable Processes

In this paper, a step response based closed loop system identification procedure for a class of time delayed unstable chemical process loops using Particle Swarm Optimization algorithm is proposed. A novel objective function is developed using the time domain specification data to guide the PSO algorithm. The step response based identification is a simple closed loop test with a Proportional (P) controller. The PSO algorithm finds the best possible values for the process model parameters such as process gain (K), process time constant (τ), and the closed loop delay (θ c). The method is tested on a class of unstable process models in the presence and absence of measurement noise. The performance of the proposed PSO based identification procedure is compared with the classical identification scheme existing in the literature. The results evident that, the proposed method helps to accomplish a better transfer function model with considerably reduced model mismatch.

A hybrid intelligent controller to reduce the energy of a wastewater treatment plant

This paper presents a Fuzzy Logic Control (FLC) scheme and Adaptive Network Fuzzy Inference System (ANFIS) for a biological waste water treatment plant, with the goal of reducing aeration energy. The famous Benchmark Simulation Model1 (BSM1) is used to test the control strategy. This hybrid controller works based on the control objectives framed in terms of rules and hence it does not require any set point to control the plant. The FLC is designed to meet the above mentioned goal. The proposed scheme has been tested under dry weather profile. To reduce the aeration energy consumption, ANFIS prediction of ammonia in tank5, is used by the FLC for the manipulation of oxygen transfer coefficient. Aeration energy of 200kWh/d has been recovered from the aeration system. Substantial improvement in the aeration energy reduction resulted when compared with the benchmark PI controller. Simulation results show the benefits of the proposed method.

Hybrid heuristic algorithm based controller tuning for a bioreactor

Bioreactor is one of the prime processing units widely employed to produce important chemical and biochemical compounds. In this paper, a hybrid heuristic algorithm has been attempted to tune PID controller for nonlinear Bioreactor model. The hybrid algorithm is a combination of Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm. Multiobjective performance indexes such as Integral Square Error, peak overshoot are considered to guide this algorithm for discovering best possible value of controller parameters. The controller tuning procedure is individually discussed for both stable and unstable steady state operating region of simulated bio-reactor model. The effectiveness of the proposed scheme has been validated through a comparative study with BFO, PSO based controller tuning methods proposed in the literature. The results show that, the hybrid method provides improved performance in reference tracking and load disturbance rejection with minimal ISE value.