A novel adaptive PID controller with new seesaw algorithms using alternative derivatives

Abstract

The [Formula: see text] controller is present in at least 90% of industrial applications due to its simplicity and robustness to control linear and non-linear systems. Its main drawbacks include difficulty in achieving proper gains tuning for complex systems and lack of adaptability attributed to its fixed gains when system dynamics change. Despite the emergence of promising controllers like Neural Networks, Fuzzy Logic, Sliding Modes, or Genetic Algorithms, diverse research efforts focus on using these to improve the [Formula: see text] rather than replacing it, aiming for either ideal fixed gains or adaptive gains. Nevertheless, while some require system models, training data, rules establishment, extensive iterations, and demanding computational resources, the [Formula: see text] and the novel [Formula: see text] controller do not. The new seesaw algorithms propose alternative behaviours based on the current error and its derivative, which are dynamic parameters that change over time. These behaviours can be inserted into the [Formula: see text] to imbue it with adaptive characteristics, achieving adaptive [Formula: see text] ([Formula: see text]) controllers with faster signal rise and stabilization times, reduction of the maximum peak, and less error accumulation compared to conventional [Formula: see text].

Keywords

• Seesaw algorithms
• alternative derivatives
• adaptive PID control
• control algorithms
• adaptive control
• PID control