Nonlinear observer-based optimal control of an active transfemoral prosthesis

This paper designs a joint controller/observer framework using a state dependent Riccati equation(SDRE)approach for an active transfemoral prosthesis system.An integral state control technique is utilized to design a tracking controller for a robot/prosthesis system.This framework promises a systematic flexible design using which multiple design specifications such as robustness,state estimation,and control optimality are achieved without the need for model linearization.Performance of the proposed approach is demonstrated through simulation studies,which show improvements versus a robust adaptive impedance controller and an extended Kalman filter-based state estimation method.Numerical results confirm the benefits of our method over the above-mentioned approaches with regard to control optimality and state estimation.

A neuro-observer-based optimal control for nonaffine nonlinear systems with control input saturations

In this study,an adaptive neuro-observer-based optimal control(ANOPC)policy is introduced for unknown nonaffine nonlinear systems with control input constraints.Hamilton–Jacobi–Bellman(HJB)framework is employed to minimize a non-quadratic cost function corresponding to the constrained control input.ANOPC consists of both analytical and algebraic parts.In the analytical part,first,an observer-based neural network(NN)approximates uncertain system dynamics,and then another NN structure solves the HJB equation.In the algebraic part,the optimal control input that does not exceed the saturation bounds is generated.The weights of two NNs associated with observer and controller are simultaneously updated in an online manner.The ultimately uniformly boundedness(UUB)of all signals of the whole closed-loop system is ensured through Lyapunov’s direct method.Finally,two numerical examples are provided to confirm the effectiveness of the proposed control strategy.