Safe Learning Reference Governor for Constrained Systems with Application to Fuel Truck Rollover Avoidance

This paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable; and this approach enables the reference governor to gradually improve command tracking performance through learning while enforcing the constraints during learning and after learning is completed. The learning can be performed either on a black-box type model of the system or directly on the hardware. After introducing the LRG algorithm and outlining its theoretical properties, this paper investigates LRG application to fuel truck rollover avoidance. Through simulations based on a fuel truck model that accounts for liquid fuel sloshing effects, we show that the proposed LRG can effectively protect fuel trucks from rollover accidents under various operating conditions.

[1]  Aaron D. Ames,et al.  Adaptive Safety with Control Barrier Functions , 2019, 2020 American Control Conference (ACC).

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

[3]  L. Haan.  Estimation of the Minimum of a Function Using Order Statistics , 1980 .

[4]  Andreas Krause,et al.  Safe Model-based Reinforcement Learning with Stability Guarantees , 2017, NIPS.

[5]  J. Kocijan,et al.  Gaussian process model based predictive control , 2004, Proceedings of the 2004 American Control Conference.

[6]  Rajesh Rajamani,et al.  Vehicle dynamics and control , 2005 .

[7]  Mridul Gautam,et al.  Review of Parameters Affecting Stability of Partially Filled Heavy-Duty Tankers , 1999 .

[8]  H. Norman Abramson,et al.  Representation of Fuel Sloshing in Cylindrical Tanks by an Equivalent Mechanical Model , 1961 .

[9]  J D Pinter,et al.  Global Optimization in Action—Continuous and Lipschitz Optimization: Algorithms, Implementations and Applications , 2010 .

[10]  Torsten Koller,et al.  Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning , 2019, ArXiv.

[11]  Anouck Girard,et al.  Tanker Truck Rollover Avoidance Using Learning Reference Governor , 2021 .

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

[13]  Ilya V. Kolmanovsky,et al.  Constraint Handling in a Fuel Cell System: A Fast Reference Governor Approach , 2007, IEEE Transactions on Control Systems Technology.

[14]  David Ingram,et al.  Capturing the Motion of the Free Surface of a Fluid Stored within a Floating Structure , 2018, Water.

[15]  Mikael Norrlöf,et al.  An adaptive iterative learning control algorithm with experiments on an industrial robot , 2002, IEEE Trans. Robotics Autom..

[16]  Aaron D. Ames,et al.  Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems* , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[17]  Zong Tian,et al.  Truck acceleration behavior study and acceleration lane length recommendations for metered on-ramps , 2016 .

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

[19]  S. Shankar Sastry,et al.  Provably safe and robust learning-based model predictive control , 2011, Autom..

[20]  Angela P. Schoellig,et al.  Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking , 2016, Int. J. Robotics Res..

[21]  B. P. Zhang,et al.  Estimation of the Lipschitz constant of a function , 1996, J. Glob. Optim..

[22]  Nan Li,et al.  A Reference Governor for Nonlinear Systems With Disturbance Inputs Based on Logarithmic Norms and Quadratic Programming , 2020, IEEE Transactions on Automatic Control.

[23]  Robert Shorten,et al.  A methodology for the design of robust rollover prevention controllers for automotive vehicles: Part 2-Active steering , 2007, 2007 American Control Conference.

[24]  Sadao Kawamura,et al.  Analysis on dynamics of underwater robot manipulators based on iterative learning control and time-scale transformation , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[25]  Dennis S. Bernstein,et al.  Output-Feedback RLS-Based Model Predictive Control* , 2020, 2020 American Control Conference (ACC).

[26]  Ahmad F. Taha,et al.  Nonlinear Dynamic Systems Parameterization Using Interval-Based Global Optimization: Computing Lipschitz Constants and Beyond , 2020, ArXiv.

[27]  Ilya Kolmanovsky,et al.  Reference Governor Strategies for Vehicle Rollover Avoidance , 2016, IEEE Transactions on Control Systems Technology.

[28]  Martin Guay,et al.  Robust and Adaptive Model Predictive Control of Nonlinear Systems , 2015 .

[29]  Ion Stiharu,et al.  Cargo load shift and its influence on tank vehicle dynamics under braking and turning , 2002 .

[30]  W. Marsden.  I and J , 2012 .

[32]  Gábor Orosz,et al.  End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks , 2019, AAAI.

[33]  Nan Li,et al.  Model-free Learning to Avoid Constraint Violations: An Explicit Reference Governor Approach , 2019, 2019 American Control Conference (ACC).

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

[35]  Anouck Girard,et al.  Coordinated control of active steering and differential braking using extended command governor for rollover avoidance , 2017 .

[36]  Nan Li,et al.  Model-free Learning for Safety-critical Control Systems: A Reference Governor Approach , 2020, 2020 American Control Conference (ACC).

[37]  Marcel J. Sidi,et al.  Spacecraft Dynamics and Control: A Practical Engineering Approach , 1997 .

[38]  Ren Yuanyuan,et al.  Equivalent Mechanical Model for Lateral Liquid Sloshing in Partially Filled Tank Vehicles , 2012 .

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

[40]  Lily Elefteriadou,et al.  REVIEW OF TRUCK CHARACTERISTICS AS FACTORS IN ROADWAY DESIGN , 2003 .