Data-Driven Adaptive Steady-State-Integral-Derivative Controller Using Recursive Least Squares With Performance Conditions

This paper presents a data-driven adaptive steady state-integral-derivative (SS-ID) control algorithm that uses gradient descent and recursive least squares (RLS) with forgetting factor. A simplified first-order differential equation of the system was designed its parameters were estimated in real-time using RLS algorithm. The steady-state input for target-state tracking derived based on performance conditions. integrated error to gain least-squares method, saved past data finite sliding window determine input. integral adapted gradient, error, adaptation rate. Simplified dynamics designed, their parameter derivative can be real time from constant-based proposed controller MATLAB/Simulink environment. evaluation conducted under various scenarios DC motor simulation model an actual test platform equipped optical encoder.