Natural Trajectory based FES-induced Swinging Motion Control B.S. K. K. Ibrahim Dept of Mechatronics and Robotics, Faculty of Electrical & Electronic Engineering University Tun Hussein Onn Batu Pahat, Johor, Malaysia babul@uthm.edu.my M.O. Tokhi, M.S. Huq and S.C. Gharooni Department of Automatic Control and System Engineering, University of Sheffield, Sheffield, United Kingdom. Abstract— The use of electrical signals to restore the function of paralyzed muscles is called functional electrical stimulation (FES). FES is a promising method to restore mobility to individuals paralyzed due to spinal cord injury. FES induced movement control is a significantly challenging area, mainly emanating from various characteristics of the underlying physiological/biomechanical system. 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 this approach only the quadriceps muscle is stimulated to perform the swinging motion by controlling the amount of stimulation pulsewidth Keywords- Functional electrical stimulation, fuzzy logic, genetic algorithm, swinging motion, quadriceps, paraplegic. I. INTRODUCTION FES induced movement control is a significantly challenging area for researchers. The challenge mainly arises due to many obstacles in stimulating the paralyzed neuromuscular system, such as fatigue, time-varying and nonlinear properties of paralyzed muscles (Levy et al., 1990). Primarily due to the complexity of the system (nonlinearities, time-variation) practical FES systems are predominantly open- loop where the controller receives no information about the actual state of the system (Crago et al., 1996). In its basic form, these systems require continuous user input. Practical success of this open-loop control strategy is still, however, seriously limited due to the fixed nature of the associated parameters. Accurate control of FES-induced movement can be ensured with a suitable closed-loop adaptive control mechanism. Such approach has several advantages over open-loop schemes, including better tracking performance and smaller sensitivity to modelling errors, parameter variations, and external disturbances (Huq,2009). Although conventional proportional, integral, derivative (PID) control is still the most widely adopted method in industry for various control applications due to its simple structure, ease of design, and low implementation cost, it might not perform satisfactorily if the system to be controlled is of highly nonlinear and/or uncertain nature. Classical closed-loop control algorithms have failed to provide satisfactory performance and are not able to guarantee stability, a desired property of the controlled system (Jezernik et al, 2004). Many control strategies have been developed to provide enhanced reproducibility of muscle response, including model reference adaptive control (Bernotas et al., 1987), fixed- parameter feedback control (Abbas, 1991), and sliding mode control (Jezernik et al., 2004). Model-reference adaptive control does not need a precise model of the musculoskeletal system, but the control performance is satisfactory only when the closed-loop bandwidth is restricted by appropriate choice of reference model parameters (Hatwell et al., 1991). Fixed parameter feedback control involves the construction of a precise mathematical model that describes the dynamic behaviour of the controlled musculoskeletal system. As muscle is very complex, fixed-parameter feedback control techniques have only enjoyed limited success (Chang et al., 1997). Jezernik et al. (2004) used sliding mode FES control to regulate knee joint angle and tested this on six neurologically intact subjects and two untrained paraplegic subjects. Good tracking of a desired knee joint trajectory was achieved, but this could only be applied to mathematical model based plant. The overall model of the plant being considered is a multi- input-multi-output (MIMO) nonlinear model consisting of nonlinear lumped parameters comprising passive joint viscoelasticity and active muscle properties and segmental dynamics (Ibrahim et al., 2010). On the other hand, fuzzy logic control (FLC) has long been known for its ability to handle complex nonlinear systems without the need for a mathematical model. FLC is the fastest growing soft computing tool in medicine and biomedical engineering (Teodorescu et al., 1999). This paper presents the development of strategies for swinging motion control by controlling the amount of stimulation pulsewidth to the quadriceps muscle of the knee joints. The knee joint model developed in Matlab/Simulink, as described in (Ibrahim et al., 2010), is used to develop the FLC based on reference trajectory derived from passive oscillation to control the knee joint movement. The FLC output is the controlled FES stimulation pulsewidth signal which stimulates the knee extensors providing torque to the knee joint. The swinging movement is performed by only controlling stimulation pulsewidth to the 2011 4th International Conference on Mechatronics (ICOM), 17-19 May 2011, Kuala Lumpur, Malaysia 978-1-61284-437-4/11/$26.00 ©2011 IEEE