Nonlinear model predictive control with Timed-Elastic-Bands

• Applicant: Professor Dr.-Ing. Torsten Bertram
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: from 2017 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 318063616

As technical processes become progressively more complex, the demands on their control and automation increase as well. Hence control concepts that explicity consider process constraints such as limitations of control and state variables gain importance in research and applications. In this context predictive controllers provide a means to repeatedly solve a receding horizon optimal control problem for nonlinear dynamic processes. Within the past decade the interest in numerical efficient realizations of predictive control has grown considerably in order to enable their widespread utilization even for mechatronic systems with fast dynamics. Until nowadays the majority of approaches in predictive control are restricted to the minimization of quadratic cost functionals, in particular control error and effort. However, these conventional formulations are not suited for time-optimal control tasks. With these objectives in mind, the research project devotes itself to the development of innovative methods for nonlinear predictive control with explicit consideration of temporal information in state representation and cost function. This project focusses on two central objectives: Firstly, extension of model predictive control to time-optimal control tasks and their efficient computation and secondly, approximation of the optimal control problem for general control tasks with dedicated efficient numeric solvers.The research programme involves the conception, development, realization and valuation of a new approach for efficient model predictive control that is based on so called Timed-Elastic-Bands. A comparative investigation among the novel approach and established methods is carried out in simulations and experiments including the analysis of computational effort, control performance as well as robustness of the prediction and closed-loop control under disturbances.

• DFG Programme: Research Grants