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.
GawthropP. J.Bond graph based control using virtual actuators, Proc. Instn Mech. Engrs, Part I: J. Systems and Control Engineering, 2004, 218 (14), 251–268. DOI: 10.1243/0959651041165864.
2.
GawthropP. J.BallanceD. J.VinkD.Bond graph based control with virtual actuators. In Proceedings of the 13th European Simulation Symposium onSimulation in Industry Eds N. Giambiasi and C. Frydman), Marseille, France, October 2001, pp. 813–817, SCS; ISBN 90–77039-02–3.
3.
GawthropP. J.BallanceD. J.Virtual actuator control of mechanical systems. In Proceedings of the International Conference onBond Graph Modeling and Simulation (ICBGM'05), Simulation Series, New Orleans, Louisiana, January 2005, pp. 233–238 (Society for Computer Simulation, San Diego, USA).
4.
GawthropP. J.WallaceM. I.WaggD. J.Bond-graph based substructuring of dynamical systems, Earthquake Engng Struct. Dynamics, 2005, 34 (6), pp. 687–703.
5.
WaggD. J.StotenD. P.Substructuring of dynamical systems via the adaptive minimal control approach, Earthquake Engng Struct. Dynamics, June 2001, 30 (6), 865–877.
6.
DarbyA. P.BlakeboroughA.WilliamsM. S.Improved control algorithm for real-time substructure testing, Earthquake Engng Struct. Dynamics, March 2001, 30 (3), 431–448.
7.
BallanceD. J.BevanG. P.GawthropP. J.DistonD. J.Model transformation tools (MTT): The open source bond graph project. In Proceedings of the International Conference onBond Graph Modeling and Simulation (ICBGM'05), Simulation Series, New Orleans, Louisiana, January 2005, pp. 123–128 (Society for Computer Simulation, San Diego, USA).
8.
NgwompoR. F.ScavardaS.ThomassetD.Physical model-based inversion in control systems design using bond graph representation. Part 1: Theory, Proc. Instn Mech. Engrs, Part I: J. Systems and Control Engineering, 2001, 215 (12), 95–103.
9.
GawthropP. J.Physical interpretation of inverse dynamics using bicausal bond graphs, J. Franklin Inst., 2000, 337 (6), 743–769. DOI: 10.1016/S0016–0032(00)00051-X.
10.
NgwompoR. F.GawthropP. J.Bond graph based simulation of nonlinear inverse systems using physical performance specifications, J. Franklin Inst., November 1999, 336 (8), 1225–1247. DOI: 10.1016/S0016–0032(99)00032–0.
11.
GawthropP. J.Control system configuration: Inversion and bicausal bond graphs. In Proceedings of the 1997 International Conference onBond Graph Modeling and Simulation (ICBGM'97) Eds J. J. Granda and G. Dauphin-Tanguy), Vol. 29, Simulation Series, Phoenix, Arizona, January 1997, pp. 97–102 (Society for Computer Simulation, San Diego, USA).
12.
GawthropP. J.Bicausal bond graphs. In Proceedings of the International Conference onBond Graph Modeling and Simulation (ICBGM'95) Eds F. E. Cellier and J. J. Granda), Vol. 27, Simulation Series, Las Vegas, Nevada, January 1995, pp. 83–88 (Society for Computer Simulation, San Diego, USA); ISBN 1–56555-037–4.
13.
KwakernaakH.SivanR.Linear Optimal Control Systems, 1972 (John Wiley, New York).
14.
KarnoppD. C.Bond graphs in control: Physical state variables and observers, J. Franklin Inst., 1979, 308 (3), 221–234.
15.
GawthropP. J.Physical model-based control: A bond graph approach, J. Franklin Inst., 1995, 332B (3), 285–305.
16.
KrsticM.KanellakopoulosI.KokotovicP. V.Nonlinear and Adaptive Control Design, 1995 (John Wiley, New York).
