Towards human skill adaptive manual control
by K. Tervo, H. Koivo
International Journal of Advanced Mechatronic Systems (IJAMECHS), Vol. 2, No. 1/2, 2010

Abstract: In processes with direct human control, the operator's machine controlling skills have a tremendous impact on the overall performance. This paper proposes a framework for human skill adaptive control (SAC) systems aiming at increasing the efficiency of human operated task execution in human-machine systems. In the framework the human operator's machine controlling skill level is evaluated continuously. Based on the skill level, the control interface is tuned to be optimal for the operator's current skill level. As the operator is learning, the control interface changes along. In particular, an algorithm for realising the SAC system is proposed. The first step in the algorithm is modelling the operator's dynamics by using the modified optimal control model (MOCM). A method using particle swarm optimisation (PSO) for obtaining the MOCM parameters from trial data is proposed. Based on the identified operator model, the optimal values for the control interface parameters are searched with optimisation. The current values of the parameters are then changed according to a simple learning rule. The usability of the method is tested in a preliminary experiment by using a simulator of a manually controlled trolley crane system.

Online publication date: Sun, 10-Jan-2010

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