Particle swarm parametric optimization of a constrained state feedback controller

The key to increasing the efficient functioning of automatic control systems for technical facilities and processes in various areas of industry is the optimal setting of the state feedback controller parameters, for instance, so as to minimize the duration of transient processes or achieve the minimum value of the integral control quality assessment. The optimization problem is solved for an integrand of a kind that ensures that the integral assessment better expresses the quality of control. At the same time, the control quality indicators are in a rather complex relation to the set parameters of the controller, which greatly complicates the analytical synthesis procedure. Numerical optimization algorithms differ in the way the adjustable parameters are changed, the most effective being those that achieve the result in a shorter computational time. Aim. The goal is to obtain the optimal parameter values for a state feedback controller with an integral component and limitations for continuous linear stationary systems based on the stochastic metaheuristic particle swarm method. Materials and methods. The modern theory of automatic control, swarm intelligence are used to solve the problem of optimizing the parameters of the state feedback controller and controlling technical systems. Results. The process of parametric optimization of a constrained state feedback controller using the particle swarm method for a one-dimensional controlled object is described. The results of simulation using the method of direct enumeration of the controller parameters and the particle swarm method are described and compared. Conclusion. The results of numerical studies allow us to conclude that the developed algorithm for particle swarm parametric optimization of a constrained state feedback controller is highly accurate and fast.