Fast Model Predictive Control Method for Large-Scale Structural Dynamic Systems: Computational Aspects

A fast and accurate model predictive control method is presented for dynamic systems representing large-scale structures. The fast model predictive control formulation is based on highly efficient computations of the state transition matrix, that is, the matrix exponential, using an improved precise integration method. The enhanced efficiency for model predictive control is achieved by exploiting the sparse structure of the matrix exponential at each discrete time step. Accuracy is maintained using the precise integration method. Compared with the general model predictive control method, the reduced central processing unit (CPU) time required by the fast model predictive control scheme can result in a shorter control update interval and a lower online computational burden. Therefore, the proposed method is more efficient for large-scale structural dynamic systems.

[1]  N. Higham The Scaling and Squaring Method for the Matrix Exponential Revisited , 2005, SIAM J. Matrix Anal. Appl..

[2]  Jan Swevers,et al.  A model predictive control approach for time optimal point-to-point motion control , 2011 .

[3]  José Manoel Balthazar,et al.  On control and synchronization in chaotic and hyperchaotic systems via linear feedback control , 2008 .

[4]  Jianjun Shi,et al.  Feedback Linearization Based Generalized Predictive Control of Jupiter Icy Moons Orbiter , 2009 .

[5]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[6]  Fan Yu,et al.  Predictive controller design for electromagnetic suspension based on mixed logical dynamical model , 2012 .

[7]  F. Williams,et al.  Discrete Time-delay Optimal Control of Structures under Seismic Excitations using the Balanced Reduction Scheme , 2009 .

[8]  Peter W. Gibbens,et al.  Efficient Model Predictive Control Algorithm for Aircraft , 2011 .

[9]  Alessandro Gasparetto,et al.  Active Position and Vibration Control of a Flexible Links Mechanism Using Model-Based Predictive Control , 2010 .

[10]  Brett Ninness,et al.  Fast Linear Model Predictive Control Via Custom Integrated Circuit Architecture , 2012, IEEE Transactions on Control Systems Technology.

[11]  Cleve B. Moler,et al.  Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later , 1978, SIAM Rev..

[12]  Alberto Bemporad,et al.  The explicit linear quadratic regulator for constrained systems , 2003, Autom..

[13]  S. O. Reza Moheimani,et al.  Model Predictive Control Applied to Constraint Handling in Active Noise and Vibration Control , 2008, IEEE Transactions on Control Systems Technology.

[14]  Peter J. Gawthrop,et al.  Continuous-time generalized predictive control (CGPC) , 1990, Autom..

[15]  Hans Joachim Ferreau,et al.  An online active set strategy to overcome the limitations of explicit MPC , 2008 .

[16]  Alberto Bemporad,et al.  An algorithm for multi-parametric quadratic programming and explicit MPC solutions , 2003, Autom..

[17]  Carlo L. Bottasso,et al.  Adaptive planning and tracking of trajectories for the simulation of maneuvers with multibody models , 2006 .

[18]  Pantelis Sopasakis,et al.  A global piecewise smooth Newton method for fast large-scale model predictive control , 2011, Autom..

[19]  Peter J. Gawthrop,et al.  CONTINUOUS-TIME GENERALIZED PREDICTIVE CONTROL (CGPC) , 1990 .

[20]  Stephen P. Boyd,et al.  Fast Model Predictive Control Using Online Optimization , 2010, IEEE Transactions on Control Systems Technology.

[21]  W. Zhong,et al.  A Precise Time Step Integration Method , 1994 .

[22]  Steven X. Ding,et al.  Data-driven monitoring for stochastic systems and its application on batch process , 2013, Int. J. Syst. Sci..

[23]  Jan Tommy Gravdahl,et al.  Explicit Model Predictive Control for Large-Scale Systems via Model Reduction , 2008 .

[24]  W. Zhong,et al.  On precise integration method , 2004 .

[25]  P. Williams,et al.  Dynamics and Control of Spinning Tethers for Rendezvous in Elliptic Orbits , 2006 .

[26]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .

[27]  Torsten Jeinsch,et al.  A Survey of the Application of Basic Data-Driven and Model-Based Methods in Process Monitoring and Fault Diagnosis , 2011 .

[28]  Edward N. Hartley,et al.  Model predictive control system design and implementation for spacecraft rendezvous , 2012 .

[29]  Wan-Xie Zhong,et al.  Discrete-time H∞ Full-information Control of Structural Systems with Control Delay , 2010 .

[30]  Eric C. Kerrigan,et al.  A sparse and condensed QP formulation for predictive control of LTI systems , 2012, Autom..