On Online Adaptive Direct Data Driven Control

Online data-driven composite adaptive backstepping control with exact differentiators

This paper presents an online data-driven composite adaptive backstepping control for a class of parametric strict-feedback nonlinear systems with mismatched uncertainties, where both tracking errors and prediction errors are utilized to update parametric estimates. Hybrid exact differentiators are applied to obtain the derivatives of virtual control inputs such that the complexity problem of i...

Data-driven Direct Adaptive Model Based Predictive Control

This paper is concerned with the design of Direct Adaptive Model Based Predictive Control (DAMBPC) using subspace identification technique to identify and implement the controller parameters. The direct identification of controller parameters reduces the design effort and computation load which is usually involved with classical adaptive control techniques. The proposed method requires a single...

Adaptive Voltage-based Control of Direct-drive Robots Driven by Permanent Magnet Synchronous Motors

Tracking control of the direct-drive robot manipulators in high-speed is a challenging problem. The Coriolis and centrifugal torques become dominant in the high-speed motion control. The dynamical model of the robotic system including the robot manipulator and actuators is highly nonlinear, heavily coupled, uncertain and computationally extensive in non-companion form. In order to overcome thes...

Direct data-driven control of constrained systems

In model-based control design one often has to describe the plant by a linear model. Deriving such a model poses issues of parameterization, estimation, and validation of the model before designing the controller. In this paper, a direct data-driven control method is proposed for designing controllers that can handle constraints without deriving a model of the plant and directly from data. A hi...

Data-Driven Optimal Control for Adaptive Optics