OPTIMAL TUNING OF VIRTUAL FEEDBACK PID CONTROLLER
This project is design based on the paper "Optimal Tuning of Virtual Feedback PID Controller for a Continuous Stirred Tank Reactor (CSTR) using Particle Swarm Optimization (PSO) Algorithm". This work is based on the optimal tuning of virtual feedback PID control for a CSTR system using PSO and 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, a virtual feedback control is implemented to control the state variables using 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 like Particle Swarm Optimization (PSO) is used.
The sequence of steps to study the PSO for the CSTR system is given below:
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 particles 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 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 Continuous Stirred Tank Reactor (CSTR) using Particle Swarm Optimization (PSO) Algorithm
Author's Name: Geetha. M , Balajee. K. A, Jovitha Jerome
Source:IEEE-International Conference On Advances In Engineering, Science And Management
Year:2012
Simulation Video Demo
The sequence of steps to study the PSO for the CSTR system is given below:
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 particles 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 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 Continuous Stirred Tank Reactor (CSTR) using Particle Swarm Optimization (PSO) Algorithm
Author's Name: Geetha. M , Balajee. K. A, Jovitha Jerome
Source:IEEE-International Conference On Advances In Engineering, Science And Management
Year:2012
Simulation Video Demo
