Designing a nonlinear disturbance observer and LQR based fractional order backstepping controller for a wearable rehabilitation robot

Recently, a vast variety of wearable robots with various applications, including rehabilitation, have been produced, but a very challenging part of exoskeleton designing which is its motion control system still requires further investigation to be completed. Due to the nonlinearity in the dynamics of human-exoskeleton, uncertainty in parameters, unmodeled or simplified structures, and external disturbances (such as interaction of exerted human forces and movements), the use of robust control strategies is inevitable. Thus, in this research, a nonlinear disturbance rejection observer was used to estimate all of those as total disturbances. Then, a fractional order backstepping sliding mode (FOBSC) was utilized for enhanced tracking plus a Linear Quadratic Regulator (LQR) method to optimize the convergence to the equilibrium points. The advantage of using LQR is the optimum selection of the control input, and the FOBSC guarantees the robustness of the controller against uncertainties and disturbances. The combination of fractional order theory and control methods causes less chattering in the human-exoskeleton interactions. Moreover, particle swarm algorithm was used in order to select the coefficients of the cost function of LQR. In order to calculate the effect of the exoskeleton on human muscles and bones, the human parameters and knee motions, OpenSim was used. Matlab was used to implement the control strategy through OpenSim. The proposed method was then compared with the normal backstepping, fractional order system and LQR methods. The results show the superiority of this method compared to the classical methods.