Action Governor for Discrete-Time Linear Systems With Non-Convex Constraints

This letter introduces an add-on, supervisory scheme, referred to as Action Governor (AG), for discrete-time linear systems to enforce exclusion-zone avoidance requirements. It does so by monitoring, and minimally modifying when necessary, the nominal control signal to a constraint-admissible one. The AG operates based on set-theoretic techniques and online optimization. This letter establishes its theoretical foundation, discusses its computational realization, and uses two simulation examples to illustrate its effectiveness.

[1]  Mohamed Fezari,et al.  Viability-Based Guaranteed Safe Robot Navigation , 2019, J. Intell. Robotic Syst..

[2]  Milan Simic,et al.  Sampling-Based Robot Motion Planning: A Review , 2014, IEEE Access.

[3]  Alberto Bemporad,et al.  Predictive Control for Linear and Hybrid Systems , 2017 .

[4]  Huei Peng,et al.  Enhancing the Performance of a Safe Controller Via Supervised Learning for Truck Lateral Control , 2017, Journal of Dynamic Systems, Measurement, and Control.

[5]  Carlos Bordons Alba,et al.  Model Predictive Control , 2012 .

[6]  W. Marsden.  I and J , 2012 .

[7]  Franco Blanchini,et al.  Set invariance in control , 1999, Autom..

[8]  David Q. Mayne,et al.  Optimized robust control invariance for linear discrete-time systems: Theoretical foundations , 2007, Autom..

[9]  Emanuele Garone,et al.  Constrained Control of UAVs in Geofencing Applications , 2018, 2018 26th Mediterranean Conference on Control and Automation (MED).

[10]  Emanuele Garone,et al.  Output Admissible Sets and Reference Governors: Saturations Are Not Constraints! , 2020, IEEE Transactions on Automatic Control.

[11]  G. Swaminathan.  Robot Motion Planning , 2006 .

[12]  Paulo Tabuada,et al.  Control Barrier Function Based Quadratic Programs for Safety Critical Systems , 2016, IEEE Transactions on Automatic Control.

[13]  Emanuele Garone,et al.  A feedback charge strategy for Li-ion battery cells based on Reference Governor , 2019, Journal of Process Control.

[14]  Alessandro Casavola,et al.  Collision avoidance command governor for multi‐vehicle unmanned systems , 2014 .

[15]  E. Gilbert,et al.  Theory and computation of disturbance invariant sets for discrete-time linear systems , 1998 .

[16]  Stefano Di Cairano,et al.  Reference and command governors for systems with constraints: A survey on theory and applications , 2017, Autom..

[17]  Walter Lucia,et al.  A Hybrid Command Governor Scheme for Rotary Wings Unmanned Aerial Vehicles , 2020, IEEE Transactions on Control Systems Technology.

[18]  Manfred Morari,et al.  Multi-Parametric Toolbox 3.0 , 2013, 2013 European Control Conference (ECC).

[19]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[20]  Sasa V. Rakovic,et al.  Parameterized Robust Control Invariant Sets for Linear Systems: Theoretical Advances and Computational Remarks , 2010, IEEE Transactions on Automatic Control.

[21]  Huei Peng,et al.  Fast Trajectory Planning and Robust Trajectory Tracking for Pedestrian Avoidance , 2017, IEEE Access.

[22]  Tianshu Chu,et al.  Safe Reinforcement Learning: Learning with Supervision Using a Constraint-Admissible Set , 2018, 2018 Annual American Control Conference (ACC).

[23]  Alberto Bemporad,et al.  Reference governor for constrained nonlinear systems , 1998, IEEE Trans. Autom. Control..

[24]  Paulo Tabuada,et al.  Correct-by-Construction Adaptive Cruise Control: Two Approaches , 2016, IEEE Transactions on Control Systems Technology.

[25]  Francis Eng Hock Tay,et al.  Barrier Lyapunov Functions for the control of output-constrained nonlinear systems , 2009, Autom..

[26]  Marko Bacic,et al.  Model predictive control , 2003 .