Abstract. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can h...
In this paper we consider discrete-time nonlinear systems that are affected, possibly simultaneously, by parametric uncertainties and disturbance inputs. The min-max Model Predict...
Nonlinear model predictive control (MPC) of a simulated chaotic cutting process is presented. The nonlinear MPC combines a neural-network model and a genetic-algorithm-based optim...
This paper deals with hierarchical model predictive control (MPC) of distributed systems. A threelevel hierarchical approach is proposed, consisting of a high level MPC controller,...
Jan Dimon Bendtsen, Klaus Trangbaek, Jakob Stoustr...