Research on Stability Control Based on the Wheel Speed Difference for the AT Vehicles

This paper utilizes a linear two-degree-of-freedom vehicle model to calculate the nominal value of the vehicle’s nondrive-wheel speed difference and investigates methods of estimating the yaw acceleration and sideslip angular speed. A vehicular dynamic stability control system utilizing this nondrive-wheel speed difference is then developed, which can effectively improve a vehicle’s dynamic stability at a very low cost. Vehicle cornering processes on roads of different frictions and with different vehicle speeds are explored via simulation, with speed control being applied when vehicle speed is high enough to make the vehicle unstable. Driving simulator tests of vehicle cornering capacity on roads of different friction coefficients are also conducted

Game-Theoretic Safety Assurance for Human-Centered Robotic Systems

In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible.This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence

Robust Vehicle Stability Control with an Uncertain Driver Model

We present the design of a robust lateral stability controller to track yaw rate and lateral velocity reference signals while avoiding front and rear tire force saturation. The controller takes into account the driver’s intent at the design stage by treating it as a measured disturbance. The uncertainty in the driver’s input is modeled as a set–valued function of the vehicle states. The control design is based on a hybrid piecewise affine bicycle model with input–dependent and state–dependent uncertainties. The performance of the controller and the importance of driver behavior modeling are demonstrated through experimental tests on ice with aggressive driver maneuvers