A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process

Abstract

Water level control is a crucial step for steam generators (SG) which are widely used to control the temperature of nuclear power plants. The control process is therefore a challenging task to improve the performance of water level control system. The performance assessment is another consideration to underline. In this paper, in order to get better control of water level, the nonlinear process was first expressed in terms of a transfer function (TF), a proportional-integral-derivative (PID) controller was then attached to the model. The parameters of the PID controller was finally optimized using particle swarm optimization (PSO). Simulation results indicate that the proposed approach can make an effective tracking of a given level set or reference trajectory.

Intelligent Mechatronics in the Measurement, Identification, and Control of Water Level Systems: A Review and Experiment

“…A single-tank dynamical system (eventually, equipped with a reservoir-a basin of water), which is investigated in [39,60,61] (state-space approach, PI, PID), [22,37] (state-space representation in a discrete form), [2,7,19,26,62,63] (transfer function approach), [3,5,6,26,[62][63][64][65] (FL, PSO), [22,37] (predictive control), [18,20,27] (geometryvarying tank: conical and spherical) and others [34,46]; 2.…”

“…Linearization brings a new mathematical model that is valid only locally. This kind of modeling is concurrent with the popular transfer function approach [2,19,26,62,63].…”

“…The mathematical model (12) describes the system dynamics with a time delay. It has been adapted in [7,18,19,62], while among others, for the model ( 11) in [2,63], new constants of resistance and capacitance are introduced to find the time constant T = RC and gain k i = R.…”

Parameter effect analysis of particle swarm optimization algorithm in PID controller design

“…Because these algorithms have been agreed as an alternative method of solving deterministic optimization problems or stochastic programming whose solution is not feasible in most cases even though optimality is proven. Genetic algorithm (GA) [1,2], ant colony optimization (ACO) [3], cuckoo search algorithm (CSA) [4], flower pollination algorithm (FPA) [5], differential evolution [6], artificial bee colony algorithm (ABC) [8], and particle swarm optimization (PSO) [7,[9][10][11] are some of meta-heuristic optimization algorithms used in controller design process in control system area.…”

Particle swarm metaheuristic applied to the optimization of a PID controller

“…When the performance of a system controlled by PID is as expected, the tuning must migrate to other methodologies. In [3] the authors present a hybrid PSO method for PID controller for trajectory tracking application of a liquid level control process. In [4] the optimization of the PID parameters is performed by means of ant colony optimization.…”