Das Parameterraumverfahren für Robuste Regelung - Methode, Werkzeuge und Anwendung auf Fahrdynamikregelung

Das Parameterraumverfahren ist geeignet als Methode zur Synthese und Analyse robuster parametrischer Regelungssysteme geringer Komplexität. Die algebraischen und numerischen Algorithmen des Parameterraumverfahrens erlauben die Abbildung unterschiedlicher Anforderungen an die Regelgüte in eine Ebene aus Betriebs- und/oder Reglerparametern. Dabei wird von einer physikalischen Modellierung der Regelstrecke ausgegangen. Für die Robustheitsanalyse werden als Modellunsicherheiten Variationen physikalischer Parameter innerhalb eines Betriebsbereichs betrachtet. Für die Reglersynthese wird hingegen in der Regel eine endliche Anzahl diskreter Betriebspunkte berücksichtigt. Die dem Verfahren zugänglichen Spezifikationen beziehen sich einerseits auf lineare Merkmale wie Eigenwertlage, Amplitudengänge und Ortskurvenverläufe. Andererseits lassen sich auch einige nichtlineare Ortskurvenkriterien einbeziehen. Als Spezifikationen werden in jedem Fall a priori erlaubte Gebiete für die Lage der Eigenwerte bzw. den Verlauf von Amplitudengängen oder Ortskurven zugewiesen. Als Ergebnis des Parameterraumverfahrens erhält man diejenigen Parametergebiete, für die eine oder mehrere Spezifikationen erfüllt sind

Robust two degree of freedom vehicle steering controller design

Robust steering control based on a specific two degree of freedom control structure is used here for improving the yaw dynamics of a passenger car. The usage of an auxiliary steering actuation system for imparting the corrective action of the steering controller is assumed. The design study is based on six operating conditions for vehicle speed and the coefficient of friction between the tires and the road representing the boundary of the operating domain of the vehicle. The design is carried out by finding the region in controller parameter plane where Hurwitz stability and a mixed sensitivity frequency domain constraint are simultaneously satisfied. A velocity based gain scheduling type implementation is used. Moreover, the steering controller has a fading effect that leaves the low frequency driving task to the driver, intervening only when necessary. The effectiveness of the final design is demonstrated using linear and nonlinear simulations

How to Make Steer-By-Wire Feel Like Power Steering

In this paper for the design of a Steer-by-Wire (SbW) system a generic controller structure is proposed with bidirectional position feedback. The design goal for SbW here is to match the dynamics of an (electric or hydraulic power) steering system which may notionally be subdivided into a manual and an assistance steering part. For matching the manual steering part a generic linear controller structure and for matching the assistance steering part a nonlinear unilateral controller structure are suggested. The controller design problem is formulated as a system dynamics equivalence problem, either based on a physical or an identified model, and is solved exactly. This result is then adapted according to practical considerations. For robustness and stability analysis of the linear part of the steer-by-wire system passivity theory is applied and performance is evaluated by Bode magnitude plots and a H?-performance criterion. Nonlinear simulations at various operating conditions (vehicle speed, road-tire contact) with a high fidelity vehicle dynamics model demonstrate the robustness of the whole system

Actuator rate limits in robust car steering control

OP 980216

Design of gain scheduling controllers in parameter space.

Up to now the parameter space approach was either utilized for robustness analysis or for design of fixed gain controllers. This paper presents an extension of this method which allows the design of gain scheduling controllers which simultaneously stabilize a finite number of representatives of an uncertain plant. The approach is applied to an automotive control example. 1 Introduction The parameter space approach can be used for design of controllers and robustness analysis of linear uncertain plants. In general, the approach requires a physically motivated modelling of the plant, i.e. the uncertain parameters have a physical relation, for example masses, lengths, etc. For convenience the uncertain parameters are gathered in q = [q 1 : : : q ` ] T , an uncertainty vector, where ` denotes the number of uncertain parameters. Each of the q i lies in an interval q i 2 [q \Gamma i ; q + i ]. In the case of independent uncertain parameters the uncertainty domain Q = fq i j q i 2 [q ..