Data compression for estimation of the physical parameters of stable and unstable linear systems

A two-stage method for the identification of physical system parameters from experimental data is presented. The first stage compresses the data as an empirical model which encapsulates the data content at frequencies of interest. The second stage then uses data extracted from the empirical model of the first stage within a nonlinear estimation scheme to estimate the unknown physical parameters. Furthermore, the paper proposes use of exponential data weighting in the identification of partially unknown, unstable systems so that they can be treated in the same framework as stable systems. Experimental data are used to demonstrate the efficacy of the proposed approach

Virtual actuators with virtual sensors

The virtual actuator approach to bond graph based control is extended to use virtual sensor inputs; this allows relative degree conditions on the controller to be relaxed. Furthermore, the effect of the transfer system can be eliminated from the closed loop system. Illustrative examples are given

Bond graph based control and substructuring

A bond graph framework giving a unified treatment of both physical-model based control and hybrid experimental–numerical simulation (also known as real-time dynamic substructuring) is presented. The framework consists of two subsystems, one physical and one numerical, connected by a transfer system representing non-ideal actuators and sensors. Within this context, a two-stage design procedure is proposed: firstly, design and/or analysis of the numerical and physical subsystem interconnection as if the transfer system were not present; and secondly removal of as much as possible of the transfer system dynamics while having regard for the stability margins established in the first stage. The approach allows the use of engineering insight backed up by well-established control theory; a number of possibilities for each stage are given. The approach is illustrated using two laboratory systems: an experimental mass-spring-damper substructured system and swing up and hold control of an inverted pendulum. Experimental results are provided in the latter case

Sensitivity bond graphs

A sensitivity bond graph, of the same structure as the system bond graph, is shown to provide a simple and effective method of generating sensitivity functions of use in optimisation. The approach is illustrated in the context of partially known system parameter and state estimation

Causality in real-time dynamic substructure testing

Causality, in the bond graph sense, is shown to provide a conceptual framework for the design of real-time dynamic substructure testing experiments. In particular, known stability problems with split-inertia substructured systems are reinterpreted as causality issues within the new conceptual framework. As an example, causality analysis is used to provide a practical solution to a split-inertia substructuring problem and the solution is experimentally verified

Estimation of the parameters of continuous-time systems using data compression

This chapter provides a unified introductory account of the estimation of the parameters of continuous-time systems using data compression based on a number of previous publication

Predictive pole-placement control with linear models

The predictive pole-placement control method introduced in this paper embeds the classical pole-placement state feedback design into a quadratic optimisation-based model-predictive formulation. This provides an alternative to model-predictive controllers which are based on linear–quadratic control. The theoretical properties of the controller in a linear continuous-time setting are presented and a number of illustrative examples are given. These results provide the foundation for novel linear and nonlinear constrained predictive control methods based on continuous-time models