PID Tuning: Robust and Intelligent Multi-Objective Approaches

The proportional-integral-derivative (PID) control structures have been widely used in industrial applications due to their design/structure simplicity and inexpensive cost. The success of the PID controllers depends on the appropriate choice of their parameters. In practice, tuning the PID parameters/gains is usually realized by classical, trial-and-error approaches, and experienced human experts, which they may not capable to achieve a desirable performance for complex real-world systems with high-order, time-delays, nonlinearities, uncertainties, and without precise mathematical models. On the other hand, the most of real-world control problems refer to multi-objective control designs that several objectives such as stability, disturbance attenuation and reference tracking with considering practical constraints must be simultaneously followed by a controller. In such cases, using a single norm based performance criteria to evaluate the robustness of resulted PID-based control systems is difficult and multi-objective tuning solutions are needed. This chapter introduces three effective robust and intelligent multi-objective methodologies for tuning of PID controllers to improve the performance of the closed-loop systems in comparison of conventional PID tuning approaches. The introduced tuning strategies are based on mixed H2/H∞, multi-objective genetic algorithm (GA), fuzzy logic, and particle swarm optimization (PSO) techniques. Indeed, these robust and intelligent techniques are employed as optimization engines to produce the PID parameters in the control loops with performance indices near to the optimal ones. Numerical examples on automatic generation control (AGC) design in multi-area power systems are given to illustrate the mentioned methodologies. It has been found that the controlled systems with proposed PID controllers have better capabilities of handling the large scale and complex dynamical systems.