Perceptive Model Predictive Control for Continuous Mobile Manipulation

A comparison of continuous and discrete tracking-error model-based predictive control for mobile robots

Model-based predictive control approaches can be successfully applied to the trajectory tracking of wheeled mobile-robot applications if the process nonlinearity is considered, if real-time performance is achieved and if assumptions made in the control-law design are met when applied to a particular process. In this paper, continuous tracking-error model-based predictive control is presented. T...

Improved Optimization Process for Nonlinear Model Predictive Control of PMSM

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

Control Model Learning for Whole-Body Mobile Manipulation

The ability to discover the effects of actions and apply this knowledge during goal-oriented action selection is a fundamental requirement of embodied intelligent agents. This requirement is most clearly demonstrated when the agent, or robot in our case, must continue to meet objectives in changing environmental contexts. For example, a humanoid robot that encounters a slippery surface might us...

Model Predictive Control of Nonholonomic Mobile Robots

Continuous Time Model Predictive Control for a Magnetic Bearing System

Abstract— The nature of active magnetic bearings has many advantages over the conventional bearing, as its operation is energy efficient and potentially leads to cleaner and noise-free environment. However, the successful operation of an active magnetic bearing system requires a complex real-time control system, because of its unstable characteristics, as well as its nature of being a multi-inp...