Offline tuning mechanism of joint angular controller for lower-limb exoskeleton with adaptive biogeographical-based optimization

Designing an accurate controller to overcome the nonlinearity of dynamic systems is a technical matter in control engineering, particularly for tuning the parameters of the controller precisely. In this paper, a tuning mechanism for a proportional-integral-derivative (PID) controller of lower limb exoskeleton (LLE) joints by adaptive biogeographical based-optimization (ABBO) is presented. The tuning of the controller is defined as an optimization problem and solved by ABBO, which is an iterative algorithm inspired by a blending crossover operator (BLX-alpha). The parameters of the migration change proportionally to the growth of iteration that conveys the error to rapid convergence by narrowing the searching space. The Lyapunov stability theory is proven for LLE nonlinear dynamic systems. ABBO algorithm is compared with other conventional optimization methods in step response, which guaranteed it was not trapped in local optima and demonstrated the lowest average error and the fastest convergence rate. The tuned controller is applied in a closed-loop system to verify its performance in the prototype. The experimental results of ABBO with PID controller ascertained that the proposed tuning mechanism is applicable in the LLE gait training.