Improving operator performance through the use of a multivariable human-machine control strategy

Abstract

Most of the mechatronics equipments and gadgets that we all nowadays rely on incorporate some kind of multidimensional human-machine systems. There is an increasing concern for improving the usability, performance, ergonomics and safety of such devices, and ultimately this will lead to the mass-production of next-generation intelligent machines, which will be capable to assist the human operator and to reduce the global effort by estimating and measuring its skills. This ongoing work introduces a novel human-machine multi-dynamic modeling methodology which can be applied on the development of these Human Adaptive Mechatronic (HAM) machines, able to adapt to the skill/dexterity levels of its users, and to enhance Human's proficiency. As a new strategy for model development, a number of two-dimensional independent pursuit manual tracking experiments are evaluated. A human-machine state-space linear model is obtained and successfully applied to design an improved closed-loop multivariable control structure. © 2012 IFIP International Federation for Information Processing.