Model-based policy search for learning mulitvariate PID gain scheduling control

Abstract

Due to its simplicity and demonstrated performance, Proportional Integral and Derivative (PID) controller remains one of the most widely-used closed-loop control mechanisms in industrial applications. For the unknown model of a system, however, a PID design can become a significantly complex task especially for a Multiple Input Multiple Output (MIMO) case. For the efficient control of a nonlinear and non-stationary systems, a scheduled PID controller can be designed. The classical approach to gain scheduling is a system linearization and the design of controllers at different operating points with a subsequent application of interpolation. This thesis continues on the recent advances in application of Reinforcement Learning (RL) to a multivariate PID tuning. In this work we extend the multivariate PID tuning framework based on the Probabilistic Inference for Learning Control (PILCO) algorithm to tune a scheduled PID controllers. The developed method does not require the linear model of a system dynamics and is not restricted to the low-order or Single Input Single Output (SISO) systems. The algorithm is evaluated using the Noisy Cart-Pole and Non-stationary Mass-Damper systems. Additionally, the proposed method is applied to the tuning of a scheduled PID controllers of autonomous Remote Control (RC) car.