Sparsity-promoting iterative learning control for resource-constrained control systems

We propose novel iterative learning control algorithms to track a reference trajectory in resource-constrained control systems. In many applications, there are constraints on the number of control actions, delivered to the actuator from the controller, due to the limited bandwidth of communication channels or battery-operated sensors and actuators. We devise iterative learning techniques that create sparse control sequences with reduced communication and actuation instances while providing sensible reference tracking precision. Numerical simulations are provided to demonstrate the effectiveness of the proposed control method.

[1]  Daniel E. Quevedo,et al.  Sparse Packetized Predictive Control for Networked Control Over Erasure Channels , 2013, IEEE Transactions on Automatic Control.

[2]  David H. Owens,et al.  Singular value distribution of non-minimum phase systems with application to iterative learning control , 2013, 52nd IEEE Conference on Decision and Control.

[3]  Edward N. Hartley,et al.  Terminal spacecraft rendezvous and capture with LASSO model predictive control , 2013, Int. J. Control.

[4]  Marc Teboulle,et al.  Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems , 2009, IEEE Transactions on Image Processing.

[5]  Burak Demirel.  Architectures and Performance Analysis of Wireless Control Systems , 2015 .

[6]  David H. Owens,et al.  Iterative learning control - An optimization paradigm , 2015, Annu. Rev. Control..

[7]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[8]  Euhanna Ghadimi,et al.  Accelerating Convergence of Large-scale Optimization Algorithms , 2015 .

[9]  A.G. Alleyne,et al.  A survey of iterative learning control , 2006, IEEE Control Systems.

[10]  M. Tomizuka,et al.  Precision Positioning of Wafer Scanners Segmented Iterative Learning Control for Nonrepetitive Disturbances [Applications of Control] , 2007, IEEE Control Systems.

[11]  M Maarten Steinbuch,et al.  Using iterative learning control with basis functions to compensate medium deformation in a wide-format inkjet printer , 2014 .

[12]  Gregory B. Passty.  Ergodic convergence to a zero of the sum of monotone operators in Hilbert space , 1979 .

[13]  E. Rogers,et al.  Iterative learning control using optimal feedback and feedforward actions , 1996 .

[14]  Michael J. Grimble,et al.  Iterative Learning Control for Deterministic Systems , 1992 .

[15]  Levent Guvenc,et al.  Control of Mechatronic Systems , 2017 .

[16]  Sergio Grammatico,et al.  Reducing actuator switchings for motion control of autonomous underwater vehicles , 2012, 2013 American Control Conference.

[17]  Qingze Zou,et al.  A review of feedforward control approaches in nanopositioning for high-speed spm , 2009 .

[18]  Patrick L. Combettes,et al.  Signal Recovery by Proximal Forward-Backward Splitting , 2005, Multiscale Model. Simul..

[19]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[20]  F. Miyazaki,et al.  Bettering operation of dynamic systems by learning: A new control theory for servomechanism or mechatronics systems , 1984, The 23rd IEEE Conference on Decision and Control.

[21]  Charles R. Johnson,et al.  Matrix Analysis, 2nd Ed , 2012 .

[22]  O. Nelles,et al.  An Introduction to Optimization , 1996, IEEE Antennas and Propagation Magazine.

[23]  Daniel E. Quevedo,et al.  Maximum Hands-Off Control: A Paradigm of Control Effort Minimization , 2014, IEEE Transactions on Automatic Control.

[24]  Jan Swevers,et al.  Iterative learning control for nonlinear systems with input constraints and discontinuously changing dynamics , 2011, Proceedings of the 2011 American Control Conference.

[25]  Jan M. Maciejowski,et al.  ℓasso MPC: Smart regulation of over-actuated systems , 2012, 2012 American Control Conference (ACC).

[26]  Daniel E. Quevedo,et al.  Discrete-time hands-off control by sparse optimization , 2016, EURASIP J. Adv. Signal Process..

[27]  Andrew G. Alleyne,et al.  Cross-coupled iterative learning control of systems with dissimilar dynamics: design and implementation , 2011, Int. J. Control.