In this paper, we consider the cooperative adaptive cruise control problem of connected autonomous vehicles networked by heterogeneous wireless channel transmission. The cooperative adaptive cruise control model with variable input delays is established to describe the varying time-delays induced from vehicular actuators and heterogeneous channel transmission. Then a set of decentralized time-delay feedback cooperative adaptive cruise control controllers is computed in such way that each vehicle evaluates its own adaptive cruise control strategy using only neighborhood information. In order to establish string stability of the connected vehicle platoon with the decentralized controllers, the sufficient conditions are obtained in the form of linear matrix inequalities. The scenarios, consisting of four different cars with three heterogeneous wireless channels, are used to demonstrate the effectiveness of the presented method.
The problem of stabilizing constrained nonlinear systems while optimizing performance is investigated in this paper. The tool of weak control Lyapunov functions (WCLFs) is introduced to construct a tuning Sontag's controller where some adjustable parameters are optimized with respect to given performances in a receding horizon fashion. Two algorithms are presented and the corresponding closed-loop systems with input constraints are proven to be stable in some regions by using the LaSalle's theorem and the properties of WCLFs. Moreover, the inverse optimality result of the controller is achieved. Finally, two open-loop unstable examples are used to illustrate the performance and effectiveness of the results obtained here.
In this paper, the adaptive cruise control problem of autonomous vehicles is considered and we propose a novel predictive cruise control approach to improve driving safety and comfort of the host vehicle. The main idea of the approach is that the predicted acceleration commands of the host vehicle are stair-likely pre-planned to satisfy their changes along the same direction within the prediction horizon. The predictive cruise controller is then computed by online solving a finite horizon constrained optimal control problem with a decision variable. Besides explicitly handling safety constraints of vehicles, the obtained controller has abilities to efficiently attenuate peaks of the cruise commands while reducing computational load of online solving the optimization problem. Hence, the ride comfort and safety performances of vehicles are improved in terms of softening acceleration response and constraint satisfaction. Moreover, the ride comfort, following and safety performances of vehicles are summed with varying weights to cope with various traffic scenarios. Some classical cases are adopted to evaluate the proposed adaptive cruise control algorithm in terms of ride comfort, car-following ability and computational demand.
Adaptive cruise control of autonomous vehicles can be posed as a multi-objective optimization problem where several conflicting criteria, e.g., fuel economy, tracking capability, ride comfort, and safety, need to be satisfied simultaneously. In order to reconcile these conflicting criteria, this paper presents a novel multi-objective predictive cruise control (MOPCC) approach in the feasible perturbation-based real-time iterative optimization framework. The longitudinal dynamics of vehicles are described as nonlinear car-tracking models. The new cost function for MOPCC is defined as the distance of the criteria vector to the vector of separately minimized criteria (i.e., a utopia point of the criteria). The weight-free MOPCC is then obtained by solving a constrained nonlinear optimal control problem in receding horizon fashion. Due to the difficulty in solving the optimization problem, the integrated perturbation analysis and sequential quadratic programming (InPA-SQP) is employed to compute the cruise controller. The merit of the proposed MOPCC is that it can systematically handle different cruise scenarios regardless of the weights of the predictive cruise control (PCC) criteria. Several driving cases are used to demonstrate the effectiveness and benefits of the proposed approach via comparing to weighted PCC approaches.
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