The application of continuous action reinforcement learning automata to adaptive PID tuning

Howell, M. N. and Gordon, Timothy and Best, M. C. (2000) The application of continuous action reinforcement learning automata to adaptive PID tuning. IEE Colloquium (Digest) (69). pp. 5-8. ISSN 0963-3308

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Item Type:Article
Item Status:Live Archive

Abstract

This paper investigates the application of the Continuous Action Reinforcement Learning Automata (CARLA) methodology to PID controller parameter tuning. The PID controller parameters are initially set using the standard Zeigler-Nichols methods. The CARLA then selects parameters stochastically based on a distribution that converges to a Gaussian around the optimal parameter values. The CARLA adaptively tunes the controller parameters on-line, to minimise a performance criterion such as the sum of time error square. The method has the benefit of producing a controller with improved performance over the Zeigler-Nichols settings that is robust to noise and to the system non-linearities. Minimal system modelling is requires since it can be applied on-line optimising the parameters for the actual system. The method is demonstrated on various different systems in simulation. It is also demonstrated as a practical example for parameter tuning of a PID controller of an engine idle speed control system for a Ford Zetec 1.8 engine during load change disturbances. Idle speed control is important to prevent engine stall and to help to reduce vehicle emissions.

Keywords:Adaptive control systems, Computer simulation, Error analysis, Learning systems, Parameter estimation, Three term control systems, Continuous action reinforcement learning automata (CARLA), Parameter tuning, PID controllers, Automata theory
Subjects:H Engineering > H990 Engineering not elsewhere classified
H Engineering > H331 Road Vehicle Engineering
H Engineering > H650 Systems Engineering
Divisions:College of Science > School of Engineering
ID Code:11676
Deposited On:04 Oct 2013 11:28

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