this paper employs nonlinear disturbance observer (ndo) for robust trajectory-free nonlinear model predictive control (nmpc) of biped robots. the ndo is used to reject the additive disturbances caused by parameter uncertainties, unmodeled dynamics, joints friction, and external slow-varying forces acting on the biped robots. in contrary to the slow-varying disturbances, handling sudden pushing ...

A concurrent learning adaptive-optimal control architecture for aerospace systems with fast dynamics is presented. Exponential convergence properties of concurrent learning adaptive controllers are leveraged to guarantee a verifiable learning rate while guaranteeing stability in presence of significant modeling uncertainty. The architecture switches to online-learned model based Model Predictiv...

Adaptive model predictive control is applied to the control of consistency. The process model used for the control is updated on the basis of the operation conditions. The process gain is calculated by the mass balance equations or by using the preceding consistency measurement. The process dynamics is determined on the basis of the scheduling variable, i.e. the flow rate. The performance of th...

Neural network model predictive controllers have demonstrated high potential in the non-conventional branch of nonlinear control. However, the major issue in process control of nonlinear systems is the sensitivity to parameters variations and uncertainties. Indeed, when the process is controlled by neural network model predictive control (NNMPC) and subject to parameters variations or uncertain...

In dealing with model predictive controllers (MPC), controller tuning is a key design step. Various tuning methods are proposed in the literature which can be categorized as heuristic, numerical and analytical methods. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a proposed analytical MPC tuning approach for plants can be appr...

This paper employs nonlinear disturbance observer (NDO) for robust trajectory-free Nonlinear Model Predictive Control (NMPC) of biped robots. The NDO is used to reject the additive disturbances caused by parameter uncertainties, unmodeled dynamics, joints friction, and external slow-varying forces acting on the biped robots. In contrary to the slow-varying disturbances, handling sudden pushing ...