OFF-EQUILIBRIUM LINEARISATION AND DESIGNOF GAIN SCHEDULED CONTROL WITHAPPLICATION TO VEHICLE SPEED

In conventional gain scheduled control design, linearisation of a time-invariant nonlinear system and local control design for the resulting set of linear time-invariant systems is performed at a set of equilibrium points. Due to its validity only near equilibrium, such a design may result in poor transient performance. To resolve this problem one can base the control design on a dynamic linearisation about some nominal trajectory. However, a drawback with this approach is that control design for the resulting linear time-varying system is in general a diicult problem. In this paper it is suggested that linearisation and local controller design should be carried out not only at equilibrium states, but also in transient operating regimes. It is shown that this results in a set of time-invariant linearisations which, when they are interpolated, form a close approximation to the time-varying system resulting from dynamic linearisation. Consequently, the transient performance can be improved by increasing the number of linear time-invariant controllers. The feasibility of this approach, and possible improvements in transient performance, are illustrated with results from an experimental vehicle speed control application.

[1]  O. Gehring,et al.  Practical results of a longitudinal control concept for truck platooning with vehicle to vehicle communication , 1997, Proceedings of Conference on Intelligent Transportation Systems.

[2]  Kenneth J. Hunt,et al.  Local controller network for autonomous vehicle steering , 1996 .

[3]  Dimiter Driankov,et al.  A Takagi-Sugeno fuzzy gain-scheduler , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[4]  William Leithead,et al.  Appropriate realization of gain-scheduled controllers with application to wind turbine regulation , 1996 .

[5]  Peter J Gawthrop,et al.  CONTINUOUS-TIME LOCAL MODEL NETWORKS , 1996 .

[6]  Tor Arne Johansen,et al.  Constructive empirical modelling of longitudinal vehicle dynamics using local model networks , 1996 .

[7]  Kazuo Tanaka,et al.  An approach to fuzzy control of nonlinear systems: stability and design issues , 1996, IEEE Trans. Fuzzy Syst..

[8]  P. Gawthrop Continuous-time local state local model networks , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[9]  P. Gahinet,et al.  A convex characterization of gain-scheduled H∞ controllers , 1995, IEEE Trans. Autom. Control..

[10]  Kazuo Tanaka,et al.  A robust stabilization problem of fuzzy control systems and its application to backing up control of a truck-trailer , 1994, IEEE Trans. Fuzzy Syst..

[11]  A. Packard Gain scheduling via linear fractional transformations , 1994 .

[12]  Douglas A. Lawrence,et al.  Gain scheduling dynamic linear controllers for a nonlinear plant , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[13]  Carlos Silvestre,et al.  A velocity algorithm for the implementation of gain scheduled controllers with applications to rigid body motion control , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[14]  M. Athans,et al.  Gain scheduling: potential hazards and possible remedies , 1991, IEEE Control Systems.

[15]  Michael Athans,et al.  Analysis of gain scheduled control for nonlinear plants , 1990 .

[16]  Wilson J. Rugh,et al.  Analytical Framework for Gain Scheduling , 1990, 1990 American Control Conference.

[17]  Karl Johan Åström,et al.  Computer-Controlled Systems: Theory and Design , 1984 .