We presented our papers at CDC 2020!

A Safety Constrained Control Framework for UAVs in GPS Denied Environment

Wenbin Wan, Hunmin Kim, Naira Hovakimyan, Lui Sha, and Petros G. Voulgaris

Presentation Slides  |  Presentation Video  |  Paper

Abstract: Unmanned aerial vehicles (UAVs) suffer from sensor drifts in GPS denied environments, which can lead to potentially dangerous situations. To avoid intolerable sensor drifts in the presence of GPS spoofing attacks, we propose a safety constrained control framework that adapts the UAV at a path re-planning level to support resilient state estimation against GPS spoofing attacks. The attack detector is used to detect GPS spoofing attacks and provides a switching criterion between the robust control mode and emergency control mode. An attacker location tracker (ALT) is developed to track the attacker’s location and estimate the spoofing device’s output power by the unscented Kalman filter (UKF) with sliding window outputs. Using the estimates from ALT, we design an escape controller (ESC) based on the model predictive controller (MPC) such that the UAV escapes from the effective range of the spoofing device within the escape time.

Safe Feedback Motion Planning: A Contraction Theory and L1-Adaptive Control Based Approach

Arun Lakshmanan†, Aditya Gahlawat†, and Naira Hovakimyan

Presentation Slides  |  Presentation Video  |  Paper

Abstract: Autonomous robots that are capable of operating safely in the presence of imperfect model knowledge or external disturbances are vital in safety-critical applications. In this paper, we present a planner-agnostic framework to design and certify safe tubes around desired trajectories that the robot is always guaranteed to remain inside. By leveraging recent results in contraction analysis and L1-adaptive control we synthesize an architecture that induces safe tubes for nonlinear systems with state and time-varying uncertainties. We demonstrate with a few illustrative examples how contraction theory-based L1-adaptive control can be used in conjunction with traditional motion planning algorithms to obtain provably safe trajectories.

†These authors contributed equally to this work.

Adaptive Robust Quadratic Programs Using Control Lyapunov and Barrier Functions

Pan Zhao, Yanbing Mao, Chuyuan Tao, Naira Hovakimyan, and Xiaofeng Wang

Presentation Slides  |  Presentation Video  |  Paper

Abstract: This paper presents adaptive robust quadratic program (QP) based control using control Lyapunov and barrier functions for nonlinear systems subject to time-varying and state-dependent uncertainties. An adaptive estimation law is proposed to estimate the pointwise value of the uncertainties with pre-computable estimation error bounds. The estimated uncertainty and the error bounds are then used to formulate a robust QP, which ensures that the actual uncertain system will not violate the safety constraints defined by the control barrier function. Additionally, the accuracy of the uncertainty estimation can be systematically improved by reducing the estimation sampling time, leading subsequently to reduced conservatism of the formulated robust QP. The proposed approach is validated in simulations on an adaptive cruise control problem and through comparisons with existing approaches.