In this work, a Weiner-type nonlinear black box model was developed for capturing dynamics of open loop stable MIMO nonlinear systems with deterministic inputs. The linear dynamic component of the model was parameterized using orthogonal Laguerre filters while the nonlinear state output map was constructed either using quadratic polynomial functions or artificial neural networks. The properties...

We provide a stability and performance analysis for nonlinear model predictive control (NMPC) schemes subject to input constraints. Given an exponential stabilizability detectability condition w.r.t. the employed state cost, we sufficiently long prediction horizon ensure asymptotic desired bound infinite-horizon optimal controller. Compared existing results, provided is applicable positive semi...

Flexibility combined with the ability to consider external constraints comprises main advantages of nonlinear model predictive control (NMPC). Applied as a motion controller, NMPC enables applications in varying and disturbed environments, but requires time-consuming computations. Hence, given full multi-DOF robot model, delay-free execution providing short horizons at appropriate prediction fo...

This work addresses the application of control systems to the optimization of a monoclonal antibodies (MAb) production chain. The attention is focused on the maximization of hybridoma fedbatch culture productivity. The proposed model presents kinetics showing strong nonlinearities through min-max functions expressing overflow metabolism. A nonlinear model predictive control (NMPC) algorithm, ch...

One of the main drawbacks of NMPC schemes is the enormous computational effort these controllers require. On the other hand, linear MPC methods can be implemented solving just Quadratic Programming (QP) or Linear Programming (LP) problems. In this paper, an alternative implementation of NMPC suggested by De Keyser (1998) is implemented to reduce the computational effort. This methodology is bas...

This paper considers the stability, robustness and output feedback problem for sampled-data nonlinear model predictive control (NMPC). Sampleddata NMPC here refers to the repeated application of input trajectories that are obtained from the solution of an open-loop optimal control problem at discrete sampling instants. Specifically we show that, under the assumption that the value function is c...

Using organic Rankine cycles (ORC) for waste heat recovery in vehicles promises significant reductions fuel consumption. Controlling the cycle, however, is difficult due to highly transient exhaust gas conditions. To tackle this issue, nonlinear model predictive control (NMPC) has been proposed and approximate NMPC solutions have investigated reduce computational demand. Herein, we compare (i) ...

In this paper, we discuss model predictive control applied to blending processes. Blending processes are ubiquitous in the chemical process industries since reactants usually need be mixed before entering a reactor. Many times, is trivial as pure streams of mixed. We consider non-trivial problems which non-pure with The motivating example problem that occurs cement production. raw mix for kiln ...

Motion control in dynamic environments is one of the most important problems using mobile robots collaboration with humans and other robots. In this paper, motion a four-Mecanum-wheeled omnidirectional robot (OMR) studied. The robot’s differential equations are extracted Kane’s method converted to discrete state space form. A nonlinear model predictive (NMPC) strategy designed based on derived ...

This paper considers an application of model predictive control to automotive air conditioning (A/C) system in future connected and automated vehicles (CAVs) with battery electric or hybrid electric powertrains. A control-oriented prediction model for A/C system is proposed, identified, and validated against a higher fidelity simulation model (CoolSim). Based on the developed prediction model, ...