Optimal Control of Digital Hydraulic Drives Using Mixed-Integer Quadratic Programming

Abstract Control of dynamical systems gets considerably harder with an increasing number of control variables. Especially when the control variables are restricted to integer values, the solution is of combinatorial complexity. An example of such systems are Digital Hydraulic Drives, where several cylinders contribute to the output torque independently. In this work we present an optimal control approach for torque control of Digital Hydraulic Drives using Mixed-Integer Quadratic Programming in a Model Predictive Control framework. The nonlinear behavior and discrete valued inputs resulting from the use of on-off valves, are accommodated in the control model using a Mixed Logical Dynamical System representation. With the presented approach, optimal switching sequences for the electrical valves are computed that produce the desired torque trajectory with fast tracking and minimal ripple, while keeping switching events at a minimum and respecting physical system constraints.

[1]  Mingjun Wang,et al.  A chaotic secure communication scheme based on observer , 2009 .

[2]  X. Xia,et al.  Nonlinear observer design by observer error linearization , 1989 .

[3]  Peter J. Gawthrop,et al.  A nonlinear disturbance observer for robotic manipulators , 2000, IEEE Trans. Ind. Electron..

[4]  Changchun Hua,et al.  A new chaotic secure communication scheme , 2005 .

[5]  Mark W. Spong,et al.  Robot dynamics and control , 1989 .

[6]  J. Gauthier,et al.  Observability and observers for non-linear systems , 1986, 1986 25th IEEE Conference on Decision and Control.

[7]  Alberto Bemporad,et al.  Control of systems integrating logic, dynamics, and constraints , 1999, Autom..

[8]  A. Isidori Nonlinear Control Systems , 1985 .

[9]  Arthur J. Krener,et al.  Measures of unobservability , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[10]  Leonardo Acho,et al.  Impulsive Synchronization for a New Chaotic oscillator , 2007, Int. J. Bifurc. Chaos.

[11]  Arthur Akers,et al.  Hydraulic Power System Analysis , 2006 .

[12]  Liang Xu,et al.  A quantitative measure of observability and controllability , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[13]  Manfred Morari,et al.  Model Predictive Direct Torque Control—Part I: Concept, Algorithm, and Analysis , 2009, IEEE Transactions on Industrial Electronics.

[14]  Enrique Zuazua,et al.  Boundary obeservability for the space semi-discretization for the 1-d wave equation , 1999 .

[15]  J. Lofberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004, 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508).

[16]  Alexey A. Bobtsov,et al.  New approach to the problem of globally convergent frequency estimator , 2008 .

[17]  Carroll,et al.  Synchronization in chaotic systems. , 1990, Physical review letters.

[18]  Johan Efberg,et al.  YALMIP : A toolbox for modeling and optimization in MATLAB , 2004 .

[19]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[20]  S. H. Salter,et al.  Modeling of Digital-Displacement Pump-Motors and Their Application as Hydraulic Drives for Nonuniform Loads , 2000 .

[21]  A. Krener,et al.  Nonlinear controllability and observability , 1977 .

[22]  Leon O. Chua,et al.  Chaos Synchronization in Chua's Circuit , 1993, J. Circuits Syst. Comput..

[23]  Boris R. Andrievsky Adaptive synchronization methods for signal transmission on chaotic carriers , 2002, Math. Comput. Simul..

[24]  Zhong-Ping Jiang,et al.  A note on chaotic secure communication systems , 2002 .

[25]  Manfred Morari,et al.  Model predictive torque control of a Switched Reluctance Motor , 2009, 2009 IEEE International Conference on Industrial Technology.

[26]  Behzad Moshiri,et al.  Hybrid Modeling and Predictive Control of a Multi-Tank System: A Mixed Logical Dynamical Approach , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[27]  M. Ishida,et al.  Optimal control of DC-DC converter using mixed logical dynamical system theory , 2006, 2006 IEEE International Conference on Industrial Technology.

[28]  Dick Dee,et al.  Observability of discretized partial differential equations , 1988 .

[29]  M. Feki An adaptive chaos synchronization scheme applied to secure communication , 2003 .

[30]  R. Mohler,et al.  Nonlinear data observability and information , 1988 .

[31]  Qinghui Yuan,et al.  Multi-level control of hydraulic gerotor motors and pumps , 2006, 2006 American Control Conference.

[32]  Romeo Ortega,et al.  A globally convergent frequency estimator , 1999, IEEE Trans. Autom. Control..

[33]  Jiashu Zhang,et al.  Chaotic secure communication based on nonlinear autoregressive filter with changeable parameters , 2006 .

[34]  Ö. Morgül,et al.  A chaotic masking scheme by using synchronized chaotic systems , 1999 .

[35]  Driss Boutat,et al.  Single Output-Dependent Observability Normal Form , 2007, SIAM J. Control. Optim..

[36]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .