Tobias Raff

Nonlinear model predictive control of a turbocharged diesel engine

Control of turbocharged diesel engines is a challenging task due to system nonlinearities and constraints on the inputs and process variables. In this paper nonlinear model predictive control is applied to control a diesel engine with a variable geometry turbocharger and an exhaust gas recirculation valve. The overall control objective is to regulate the setpoints of the air-fuel ratio and the amount of recirculated exhaust gas in order to obtain low exhaust emission values and low fuel consumption without smoke generation. Simulation results are presented to study the advantages and disadvantages of nonlinear model predictive control. The achieved performance is compared in simulations with a linear state feedback controller and an input-output linearization based control method. As shown, nonlinear model predictive control achieves good overall control performance and constraint satisfaction.

Nonlinear model predictive control of a four tank system: An experimental stability study

There are well-known theoretical examples that show that stability constraints in nonlinear model predictive control (NMPC) are necessary in order to guarantee closed loop stability. In this paper it is shown that these stability constraints, derived from theory, are also essential in practice. In particular, an experimental study is carried out on a four tank system that illustrates the stability behavior of NMPC.

Observer with sample-and-hold updating for Lipschitz nonlinear systems with nonuniformly sampled measurements

This paper presents a new observer design for Lipschitz nonlinear continuous-time systems with nonuniformly sampled measurements. Based on recent results in sampled-data control of linear continuous-time systems, linear matrix inequality (LMI) conditions are established to guarantee global stability of the estimation error dynamics and to design the observer matrix. The applicability of the proposed observer is demonstrated via two examples, that are the flexible joint robotic arm and Chuas circuit.

An impulsive observer that estimates the exact state of a linear continuous-time system in predetermined finite time

This paper presents a new observer for the class of linear continuous-time systems. In contrast to many well-established observers, which normally estimate the system state in an asymptotic fashion, the proposed observer estimates the exact system state in predetermined finite time. The finite convergence time of the proposed observer is achieved by updating the observer state based on current observer data at a definite time instant. Simulation results are presented to illustrate the convergence behavior of the proposed observer.

Nonlinear Model Predictive Control: A Passivity-Based Approach

This paper presents a novel approach for nonlinear model predictive control based on the concept of passivity. The proposed nonlinear model predictive control scheme is inspired by the relationship between optimal control and passivity as well as by the relationship between optimal control and model predictive control. In particular, a passivity-based state constraint is used to obtain a nonlinear model predictive control scheme with guaranteed closed loop stability. Since passivity and stability are closely related, the proposed approach can be seen as an alternative to control Lyapunov function based approaches. To demonstrate its applicability, the passivity-based nonlinear model predictive control scheme is applied to control a quadruple tank system.

A Finite Time Unknown Input Observer For Linear Systems

This paper presents a finite time unknown input observer for linear systems, i.e. an observer which estimates the exact state of a linear system with unknown input disturbances in a predefined finite time. Based on recent results in observer design for linear systems, conditions are established to guarantee finite time convergence of the estimation error dynamics. The proposed observer is demonstrated on a DC motor for the estimation of the rotor current and the angular velocity despite an unknown input disturbance in the stator voltage

Observers with impulsive dynamical behavior for linear and nonlinear continuous-time systems

This paper presents new observers for Lipschitz nonlinear systems, for systems with nondecreasing nonlinearities, and for linear systems. The dynamics of these observers exhibits impulsive dynamical behavior due to the update of the observer state at discrete instants of time. Conditions are given to ensure global convergence of these observers and possible applications are discussed. Furthermore, several approaches are proposed to design the observer matrices via linear matrix inequality (LMI) techniques. Finally, the proposed impulsive observer for Lipschitz nonlinear systems is applied to estimate the system state of a flexible joint robotic arm.

An Observer that Converges in Finite Time Due to Measurement-based State Updates

Abstract This paper presents a new observer that estimates the exact state of a linear continuous-time system in predetermined finite time. The finite convergence time of the proposed observer is achieved by updating the observer state based on the difference between the measured output and the estimated output at discrete time instants. Simulation results are presented to illustrate the convergence behavior and the applicability of the proposed observer.

An EKF-based observer for nonlinear time-delay systems

This paper presents an observer for the class of nonlinear time-delay systems. The proposed observer is based on recent results in extended Kalman filtering for the class of deterministic nonlinear systems. However, the observer differs from the extended Kalman filter (EKF) by an additional term in the Riccati differential equation that takes the delayed states of the system into account. By using a Lyapunov-Krasovskii functional, sufficient conditions for local asymptotic stability of the estimation error dynamics are established. Furthermore, simulation results are presented to demonstrate the applicability of the proposed observer

Sampled-Data Nonlinear Model Predictive Control for Constrained Continuous Time Systems

Often one desires to control a nonlinear dynamical system in an optimal way taking constraints on the states and inputs directly into account. Classically this problem falls into the field of optimal control. Often, however, it is difficult, if not impossible, to find a closed solution of the corresponding Hamilton-Jacobi-Bellman equation. One possible control strategy that overcomes this problem is model predictive control. In model predictive control the solution of the Hamilton-Jacobi-Bellman equation is avoided by repeatedly solving an open-loop optimal control problem for the current state, which is a considerably simpler task, and applying the resulting control open-loop for a short time. The purpose of this paper is to provide an introduction and overview to the field of model predictive control for continuous time systems. Specifically we consider the so called sampled-data nonlinear model predictive control approach. After a short review of the main principles of model predictive control some of the theoretical, computational and implementation aspects of this control strategy are discussed and underlined considering two example systems.