This paper develops a new high-gain observer design method for nonlinear systems that has a lower gain compared to the standard high-gain observer. This new observer, called HG/LMI observer is obtained by combining the standard high gain methodology with the LMI-based observer design technique. Through analytical developments, the paper shows how the new observer provides lower gains, shows how it applies to systems with multi nonlinear functions and analyzes performance in the presence of measurement noise and/or delayed output measurements. A numerical example is given to illustrate the increasing advantage of the new HG/LMI observer with increase in the observer's "compromise index". Finally, the applicability and performance of the observer is demonstrated for a real-world application consisting of a train's magnetic levitation system.

This paper focuses on the development and use of a nonlinear observer for tracking of vehicle motion trajectories while using a radar sensor. Previous results on vehicle tracking have typically used an interacting multiple model filter that needs different models for different modes of vehicle motion. This paper uses a single nonlinear vehicle model that can be used for all modes of vehicle motion. Previous nonlinear observer design results from the literature do not work for the nonlinear system under consideration due to the wide range of operating conditions that need to be accommodated. Hence, a new nonlinear observer design technique that utilizes better bounds on the coupled nonlinear functions in the dynamics is developed. The exponential stability of the observer is established using Lyapunov techniques. The observer design with the developed technique is then implemented in both simulations and experiments. Experimental results show that the observer can simultaneously and accurately estimate longitudinal position, lateral position, and velocity and orientation variables for the vehicle from radar measurements. Results are presented both for tracking of vehicle maneuvers on highways and of maneuvers on urban roads at traffic intersections where turns and significant changes in vehicle orientation can occur.

This paper focuses on the detection of cyber-attacks on a wireless communication channel and simultaneous radar sensor health monitoring for a connected vehicle. A semi-autonomous adaptive cruise control (SA-ACC) vehicle is considered which has wireless communication with its immediately preceding vehicle in the same lane. The wireless connectivity enables the vehicle to operate at small time-gap distances without creating string instability. However, the reliability of the wireless connectivity is critical for ensuring safe vehicle operation. The presence of two unknown inputs related to both sensor failure and cyber-attack seemingly poses a difficult estimation challenge which is addressed in this paper. The dynamic system is first represented in descriptor system form. An observer with estimation error dynamics decoupled from the cyber-attack signal is developed. The performance of the observer is first extensively evaluated in simulations. The estimation system is able to detect either a fault in the radar or a cyber-attack. Further, the proposed control strategy achieves resilient adaptive cruise control system operation by switching to larger time-gaps and a non-connected controller when a cyber-attack is detected. Finally, experimental data from a real vehicle on the I-94 Highway in Minneapolis, MN, USA is also used to evaluate the performance of the proposed algorithms. The fundamental estimation algorithm developed herein can be extended in the future to enable cyber-attack detection in more complex connected vehicle architectures.

This work addresses the distributed consensus tracking problem for an extended class of high-order nonlinear multiagent networks with guaranteed performances over a directed graph. The adding one power integrator methodology is skillfully incorporated into the distributed protocol so as to tackle high powers in a distributed fashion. The distinguishing feature of the proposed design, besides guaranteeing closed-loop stability, is that some transient-state and steady-state metrics (e.g., maximum overshoot and convergence rate) can be preselected a priori by devising a novel performance function. More precisely, as opposed to conventional prescribed performance functions, a new asymmetry local tracking error-transformed variable is designed to circumvent the singularity problem and alleviate the computational burden caused by the conventional transformation function and its inverse function, and to solve the nondifferentiability issue that exists in most existing designs. Furthermore, the consensus tracking error is shown to converge to a residual set, whose size can be adjusted as small as desired through selecting proper parameters, while ensuring closed-loop stability and preassigned performances. One numerical and one practical example have been conducted to highlight the superiority of the proposed strategy.

This article addresses the problem of state estimation and simultaneous learning of the vehicle's tire model on autonomous vehicles. The problem is motivated by the fact that lateral distance measurements are typically available on modern vehicles while tire models are difficult to identify and also vary with time. Tire forces are modeled in the estimator using a neural network in which no a priori assumptions on the type of model need to be made. A neuro-adaptive observer that provides asymptotically stable estimation of the state vector and of the neural network weights is developed. The developed observer is evaluated using both MATLAB simulations with a low-order model as well as with an unknown high-order model in the commercial software CarSim. Cornering and lane change maneuvers are used to learn the tire model over an adequately large range of slip angles. Performance with the low-order vehicle model is excellent with near-perfect estimation of states as well as the tire force nonlinear characteristics. The performance with the unknown high-order CarSim model is also found to be good with the tire model being estimated correctly over the range of slip angles excited by the executed vehicle maneuvers. The developed technology can enable a new approach to obtaining tire models that are otherwise difficult to identify in practice and depend on empirical characterizations.