On-line PID tuning for engine idle-speed control using continuous action reinforcement learning automata

PID systems are widely used to apply control without the need to obtain a dynamic model. However, the performance of controllers designed using standard on-line tuning methods, such as Ziegler-Nichols, can often be significantly improved. In this paper the tuning process is automated through the use of continuous action reinforcement learning automata (CARLA). These are used to simultaneously tune the parameters of a three term controller on-line to minimise a performance objective. Here the method is demonstrated in the context of engine idle speed control; the algorithm is first applied in simulation on a nominal engine model, and this is followed by a practical study using a Ford Zetec engine in a test cell. The CARLA provides marked performance benefits over a comparable Ziegler-Nichols tuned controller in this application.