Desempenho de métodos clássicos e meta-heurísticas aplicadas à sintonia de controladores PID sob o aspecto do fator de incontrolabilidade - Andrade Filho, Alfredo de Paulo (2023)
The PID (Proportional Integral Derivative) is one of the most used in industry, and has been employed around the world for decades in industrial control systems. Its popularity can be attributed in part, to its robust performance, in a wide range of operating conditions, and their functional simplicity, which allows engineers to operate them in a simple and direct way. However, tuning such controllers effectively, represents a challenge to control engineers, since it directly affects the efficiency of the system. In the context of modern industry, the setback is even greater, due to the complexity of the processes involved, in which the presence of the Dead Time and the Time Constant of the system, have a great impact on its response. Tuning of PID controllers aims to satisfy some specifications imposed on the transient and steadystate characteristics of the response of the system being controlled. Generally, these specifications fall on the values of Rise Time (Tr), Settlement Time (Ts), Maximum Value overshoot (Mp) and a maximum acceptable Stationary Error (Ess) value. Within the scope of control problems that offer great complexity, either because of the impossibility of previously knowing the transfer function of the system, or due to its multivariable characteristics, several researches have been carried out within the scope of optimizing the tuning of PID controllers using meta-heuristics. In many situations, the system PID cannot be accurately modeled, and still malfunctions in the presence of noise or external disturbances due to the adjustment problem of its parameters that are challenging in practice, as they depend on characteristics of the environment. In this work, we sought to evaluate the performance of the PID controller under the aspect of Uncontrollability Factor when tuned by Genetic Algorithm heuristics (GA) and Particle Swarm (PSO), which have been shown to be more effective in compared to classical tuning methods. Six case studies were evaluated, with varied Uncontrollability Factors.