Optimized fuzzy control for natural trajectory based FES-swinging motion

The use of electrical signals to restore the function of paralyzed muscles is called functional electrical stimulation (FES). FES is a promising ethod to restore mobility to individuals paralyzed due to spinal cord injury (SCI). A crucial issue of FES is the control of motor function by the artificial activation of paralyzed muscles due to the various characteristics of the underlying physiological/biomechanical system. Muscle response characteristics are nonlinear and time-varying. After developing a nonlinear model describing the dynamic behavior of the knee joint and muscles, a closed-loop approach of control strategy to track the reference trajectory is assessed in computer simulations. Then, the controller was validated through experimental work. In this approach only the quadriceps muscle is stimulated to perform the swinging motion by controlling the amount of stimulation pulsewidth. An approach of fuzzy trajectory tracking control of swinging motion optimized with genetic algorithm is presented. The results show the effectiveness of the approach in controlling FES-induced swinging motion in the simulation as well as in the practical environment

Identification of active properties of knee joint using GA optimization

Functional Electrical Stimulation requires an accurate model of electrically stimulated muscles to control the muscle contraction force. Characterization of electrically stimulated muscle is complex because of the non-linearity and time-varying nature of the system with interdependent variables. The muscle model consists of relatively well known time-invariant passive properties and uncertain time-variant active properties. In this research a new approach for estimating nonlinear active properties of the electrically stimulated quadriceps muscle group is investigated. The objective of this study is to develop a model that could be used to describe active joint properties including continuous-time nonlinear activation dynamics and nonlinear static contraction. As an example, the modelling of a freely swinging lower leg by electrical stimulation of the quadriceps is considered