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1. Sℒ 1-Simplex: Safe Velocity Regulation of Self-Driving Vehicles in Dynamic and Unforeseen Environments

   https://doi.org/10.1145/3564273  

   Mao, Yanbing; Gu, Yuliang; Hovakimyan, Naira; Sha, Lui; Voulgaris, Petros (January 2023, ACM Transactions on Cyber-Physical Systems)

   Tarek Abdelzaher, Karl-Erik Arzen (Ed.)

   This article proposes a novel extension of the Simplex architecture with model switching and model learning to achieve safe velocity regulation of self-driving vehicles in dynamic and unforeseen environments. To guarantee the reliability of autonomous vehicles, an ℒ₁adaptive controller that compensates for uncertainties and disturbances is employed by the Simplex architecture as a verified high-assurance controller (HAC) to tolerate concurrent software and physical failures. Meanwhile, the safe switching controller is incorporated into the HAC for safe velocity regulation in the dynamic (prepared) environments, through the integration of the traction control system and anti-lock braking system. Due to the high dependence of vehicle dynamics on the driving environments, the HAC leverages the finite-time model learning to timely learn and update the vehicle model for ℒ₁adaptive controller, when any deviation from the safety envelope or the uncertainty measurement threshold occurs in the unforeseen driving environments. With the integration of ℒ₁adaptive controller, safe switching controller and finite-time model learning, the vehicle’s angular and longitudinal velocities can asymptotically track the provided references in the dynamic and unforeseen driving environments, while the wheel slips are restricted to safety envelopes to prevent slipping and sliding. Finally, the effectiveness of the proposed Simplex architecture for safe velocity regulation is validated by the AutoRally platform. 

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2. A High-Gain Observer Approach to Robust Trajectory Estimation and Tracking for a Multirotor Unmanned Aerial Vehicle

   https://doi.org/10.1115/1.4065758  

   Boss, Connor J; Srivastava, Vaibhav (January 2025, Journal of Dynamic Systems, Measurement, and Control)

   Abstract Using the context of trajectory estimation and tracking for multirotor unmanned aerial vehicles (UAVs), we explore the challenges in applying high-gain observers to highly dynamic systems. The multirotor will operate in the presence of external disturbances and modeling errors. At the same time, the reference trajectory is unknown and generated from a reference system with unknown or partially known dynamics. We assume the only measurements that are available are the position and orientation of the multirotor and the position of the reference system. We adopt an extended high-gain observer (EHGO) estimation framework to estimate the unmeasured multirotor states, modeling errors, external disturbances, and the reference trajectory. We design a robust output feedback controller for trajectory tracking that comprises a feedback linearizing controller and the EHGO. The proposed control method is rigorously analyzed to establish its stability properties. Finally, we illustrate our theoretical results through numerical simulation and experimental validation in which a multirotor tracks a moving ground vehicle with an unknown trajectory and dynamics and successfully lands on the vehicle while in motion. 

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3. A Hybrid Model Reference Adaptive Control System for Multi-Rotor Unmanned Aerial Vehicles

   https://doi.org/10.2514/6.2024-0755  

   Gramuglia, Mattia; Kumar, Giri Mugundan; L'Afflitto, Andrea (January 2024, SciTech)

   AIAA (Ed.)

   In this paper, a novel model reference adaptive control (MRAC) architecture for nonlinear, time-varying, hybrid dynamical systems is applied for the first time to design the control system of a multi-rotor unmanned aerial vehicle (UAV). The proposed control system is specifically designed to address problems of practical interests involving autonomous UAVs transporting unknown, unsteady payloads and subject to instantaneous variations both in their state and in their dynamics. These variations can be due, for instance, to the payload’s dynamics, impacts between the payload and its casing, and sudden payload dropping and pickup. The proposed hybrid MRAC architecture improves the UAV’s trajectory tracking performance over classical MRAC also in the presence of motor failures. The applicability of the proposed framework is validated numerically through the first use of the high-fidelity simulation environment PyChrono for autonomous UAV control system testing. 

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4. Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems

   Yiheng Lin, Yang Hu (January 2021, Advances in Neural Information Processing Systems 34 (NeurIPS 2021))

   We study predictive control in a setting where the dynamics are time-varying and linear, and the costs are time-varying and well-conditioned. At each time step, the controller receives the exact predictions of costs, dynamics, and disturbances for the future k time steps. We show that when the prediction window k is sufficiently large, predictive control is input-to-state stable and achieves a dynamic regret of O(λkT), where λ<1 is a positive constant. This is the first dynamic regret bound on the predictive control of linear time-varying systems. We also show a variation of predictive control obtains the first competitive bound for the control of linear time-varying systems: 1+O(λk). Our results are derived using a novel proof framework based on a perturbation bound that characterizes how a small change to the system parameters impacts the optimal trajectory. 

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5. Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning

   https://doi.org/10.1109/ICRA46639.2022.9812309  

   Leu, Jessica; Wang, Yebin; Tomizuka, Masayoshi; Di Cairano, Stefano (May 2022, 2022 International Conference on Robotics and Automation (ICRA))

   This paper presents an integrated motion planning system for autonomous vehicle (AV) parking in the presence of other moving vehicles. The proposed system includes 1) a hybrid environment predictor that predicts the motions of the surrounding vehicles and 2) a strategic motion planner that reacts to the predictions. The hybrid environment predictor performs short-term predictions via an extended Kalman filter and an adaptive observer. It also combines short-term predictions with a driver behavior cost-map to make long-term predictions. The strategic motion planner comprises 1) a model predictive control-based safety controller for trajectory tracking; 2) a search-based retreating planner for finding an evasion path in an emergency; 3) an optimization-based repairing planner for planning a new path when the original path is invalidated. Simulation validation demonstrates the effectiveness of the proposed method in terms of initial planning, motion prediction, safe tracking, retreating in an emergency, and trajectory repairing. 

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