Optimal multivariable two-degree-of-freedom control of electric wheelchair using non-dominated sorting genetic algorithm-II

Electric wheelchairs (EW) experience various terrains surfaces and slopes as well as occupants with diverse weights. This, in turn, imparts a substantial amount of perturbation to the EW dynamics. In this paper we make use of a two-degree-of-freedom control architecture called disturbance observer (DOB) which reduces sensitivity to model uncertainties while enhancing rejection of disturbances which occur when entering slopes. The feedback loop which is designed via characteristic loci method (CLM) is then augmented with a DOB containing a parameterized low-pass filter. According to the disturbance rejection, sensitivity reduction, and noise rejection of the whole controller, three performance indices are defined which enable us to pick the filter's optimal parameters using a multi-objective optimization (MOO) approach called non-dominated sorting genetic algorithm-II (NSGA-II). Finally, experimental results show desirable improvement in stiffness and disturbance rejection of the proposed controller as well as its robust stability.