OPTIMAL TUNING OF VIRTUAL FEEDBACK PID CONTROLLER FOR CONTINUOUS STIRRED TANK REACTOR (CSTR) USING PSO AND ACO ALGORITHM

     This design is based on the optimal tuning of virtual feedback PID control for a CSTR system using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithm for minimum Integral Square Error (ISE) condition.CSTR plays a vital role in almost all the chemical reactions and is a highly nonlinear system exhibiting stable as well as unstable steady states. The variables which characterize the quality of the final product in CSTR are often difficult to measure in real-time and cannot be directly measured using the feedback configuration. So, virtual feedback control is implemented to control the state variables using the Extended Kalman Filter (EKF) in the feedback path. Since it is hard to determine the optimal or near-optimal PID parameters using classical tuning techniques like Ziegler Nichols method, a highly-skilled optimization algorithm i.e. PSO and ACO are used and tested using Matlab Software.
    STEP 1: Specify the lower and upper bounds of Kp, Ki and Kd· Initialize randomly the particles of the swarm including swarm size, iteration,            acceleration constant, inertia weight factor, the position matrix x1and the velocity matrix Vi and so on.
    STEP 2: Calculate the evaluation value of each particle using the evaluation function given.
    STEP 3: Compare each particle's new fitness value with its personal best position's fitness value, and update the personal best position pbest.
    STEP 4: Search for the best position among all particle's personal best position, and denote the best position gbest.
    STEP 5: Update the velocity Vi of each particle according to equation,vid=wvid+c1r(pid-xid)+c2R(pgd-xid) update the particle position matrix        according to equation xid=xid+vid where c1 and c2 are positive constants, r and R are two random functions in the range [0,1].
    STEP 6: Update the control parameter.
    STEP 7: If the number of iterations reaches the maximum, then stop. The latest Obest is regarded as the optimal PID controller parameter.            Otherwise, go to step 2.

Reference Paper: Optimal Tuning of Virtual Feedback PID Controller for a CSTR using PSO Algorithm
Author’s Name: Optimal Tuning of Virtual Feedback PID Controller for a CSTR using PSO Algorithm
Source: lCAESM
Year: 2012

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