All publications by Gawthrop in 2005

The rapid growth of GNU/Linux in recent years has focused attention on free and open source software. MTT (Model Transformation Tools) is, as far as the authors are aware, the only open source project related to bond graphs. This paper surveys MTT in its present form and invites collaboration from the wider bond graph community.

Bond graph modelling has been applied widely and many ideas have been collected from the user community for improvement, particularly associated with federated systems. A project is underway to address these via a prototype toolset with the working title of Next-Generation Transformation Tools (NTT). This paper will summarise the motivations behind this development and describe notational changes that have been found necessary. This should provide useful insights into the problem of large-scale bond graphs.

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.

The virtual actuator approach is explained and illustrated using three experimental systems. Conclusions are drawn about the importance of transfer system design.

The previously developed Predictive Pole Placement (PPP) controller is modified to give enhanced numerical and stability properties by embedding the method in a linear-quadratic formulation to give a linear-quadratic PPP (LQPPP) controller. Input, output and state constraints are considered using an natural quadratic programming (QP) formulation of LQPPP. Illustrative examples are given.

A bond graph approach to hybrid simulation of dynamical systems using numerical-experimental real-time substructuring is presented. The bond graph concepts of a virtual junction and a virtual actuator, hitherto used in the context of physical-model based control, are used to perform the substructuring in an intuitively appealing way. The approach is illustrated by the reworking of a previously-published example.

The approach is verified experimentally using a bench-top multiple mass-spring system for the physical substructure and automatically generated real-time code is used to implement the numerical substructure.

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 non-linear 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 of a stable system. Experimental data are used to demonstrate the efficacy of the proposed approach.

Although structural constraints such as model order and time delay have been incorporated in the continuous time system identification since its origin, the constraints on the estimated model parameters were rarely enforced. This paper proposes a continuous time system identification approach with constraints. It shows that by incorporating physical parameter information known a priori as hard constraints, the traditional parameter estimation schemes are modified to minimize a quadratic cost function with linear inequality constraints. Using the structure of Frequency Sampling Filters as the vehicle, the paper shows that the constraints can be readily imposed on continuous time frequency response estimation and step response estimation. In particular, a priori knowledge in both time-domain and frequency domain is utilized simultaneously as the constraints for the optimal parameter solution. A Monte-Carlo simulation study with 100 noise realization is used to demonstrate the improvement of the estimation results in terms of continuous time frequency response and continuous time step response.