All publications by Gawthrop in 1998

The paper discusses the generation of symbolic models of complex systems using hierarchical bond graphs. The uses to which such models can be put include simulation code generation, linearisation, system inversion for actuator sizing and controller design. This methodology is illustrated with reference to three modelling projects: aircraft systems, plastic-onto-wire extrusion and a gravity wave detector

The continuous-time generalised predictive controller is shown to include the exact-linearisation controller as a special case. An alternative (prediction-free) version of GPC is shown to provide one possible extension of exact linearisation to cope with nonlinear systems with unstable dynamics.

The multivariable continuous-time generalised predictive controller (CGPC) is recast in a state-space form and shown to include generalised minimum variance (GMV) and a new algorithm, predictive GMV (PGMV) as special cases. Comparisons are drawn with the exact linearisation methods of nonlinear control, and it is noted that, unlike the transfer function approach, the state-space approach extends readily to the nonlinear case. The resulting state space design algorithms are conceptually and algorithmically simpler than the corresponding transfer function based versions and have been realised as a freely available Matlab tool-box

Keywords: CGPC;Matlab tool-box;PGMV;exact linearisation methods;generalised minimum variance;linear systems;multivariable continuous-time generalised predictive control ;nonlinear control;nonlinear systems;predictive GMV;state space design algorithms;state-space approach;transfer function based versions;multivariable control systems;nonlinear control systems;predictive control;state-space methods;

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 difficult 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.