Network-level traffic signal control is an effective way to increase throughput and reduce congestion. The max-pressure algorithm, known for maximizing network throughput, has been widely studied. However, it requires accurate queue length and turn ratio measurements, and its theoretical guarantee is limited to feasible demand (i.e., demand within the capacity region) under the assumption of infinite queue capacity. To overcome these limitations, this study proposes a distributed joint admission and signal control algorithm for finite-capacity networks with both connected and regular vehicles. By using feedback from connected vehicles, the algorithm estimates queue lengths and turn ratios, reducing reliance on precise measurements. It also adaptively adjusts input flow rates to prevent oversaturation and ensure demand feasibility, even under high-demand conditions, while optimizing signal phases to ensure analytic performance. Using a Lyapunov drift optimization approach, we analytically prove a$[O(1/V), O(V)]$tradeoff between throughput and delay and establish degradation bounds that quantify the impact of queue length estimation errors on network performance. Simulations in a network with 256 origin-destination pairs show up to a 16.3% increase in throughput and reduced delays, especially in high-demand settings. The method also demonstrates strong resilience to sudden demand changes and incidents, ensuring quick recovery.

The rapid growth of online ride hailing services has greatly improved passenger convenience. Existing methods that combine travel time prediction with order matching mainly focus on interactions between adjacent road segments, while ignoring latent relations between non-adjacent segments. In addition, global matching for mixed orders wastes computation on invalid and low-quality solutions. To address these issues, this paper proposes an online assignment framework for mixed ride hailing orders. First, a Graph Convolutional Neural Network with physical and virtual graphs is developed to extract heterogeneous road network features and predict travel time. Second, graph clustering and bipartite matching are combined to group and match mixed orders. Experiments on the urban road network within Beijing’s Fifth Ring Road show that, compared with baseline methods, the proposed method achieves higher travel time prediction accuracy and improves both the feasibility of matching results and online solving efficiency.

Using the backstepping technique, this paper formulates innovative adaptive finite-time stabilizing controllers for uncertain nonlinear systems featuring nonuniform input quantization and asymmetric, time-varying output constraints. These novel controllers leverage the consistent characteristics of both hysteresis quantizers and logarithmic quantizers. Quantization errors, when consistent, become unbounded and contingent on control input, rendering them incompatible with the growth conditions of nonlinear systems. Consequently, the developed adaptive controllers eliminate the reliance on growth conditions, effectively addressing the impact of unbounded quantization errors on finite-time stability. This adaptability allows the controllers to function effectively with systems employing either hysteresis quantizers or logarithmic quantizers. The paper establishes the convergence of these controllers through the finite-time Lyapunov stability theorem. It also provides a comprehensive guideline for tuning settling time, enabling fine-grained control over finite-time convergence and adjustable tracking error performance. Additionally, the controllers rigorously maintain system output within predefined limits. Their effectiveness and low computational burden are demonstrated through three comparative numerical simulations and a practical simulation in collision-free trajectory tracking control of an autonomous vehicle platoon using the vehicle motion software CarSim. These simulations confirm the advanced performance of the adaptive controllers.Note to Practitioners—This paper introduces an innovative approach to control uncertain nonlinear systems encountering intricate input quantization and output constraints. Employing the sophisticated backstepping technique, the authors present adaptive finite-time-stabilizing controllers engineered to address nonuniform input quantization and asymmetric, time-varying output restrictions. What distinguishes these controllers is their reliance on the consistent behavior exhibited by hysteresis and logarithmic quantizers. This unique feature equips them to effectively counteract unbounded quantization errors influenced by control input. Most notably, these controllers eliminate the conventional growth conditions typically demanded by nonlinear systems. As a result, they extend their applicability to a broad spectrum of systems employing either hysteresis or logarithmic quantizers. The research also provides practitioners with a valuable guideline for precisely adjusting settling time. This enables the attainment of desired convergence rates while permitting adaptable tracking error performance. Additionally, these controllers guarantee that the system’s output adheres to predefined limits. The practical significance of this study is highlighted through three comparative numerical simulations and a real-world application simulation. This real-world simulation involves collision-free trajectory tracking control of an autonomous vehicle platoon, executed using the vehicle motion software CarSim. These simulations unequivocally demonstrate the effectiveness and low computational burden of the developed controllers, thereby establishing them as a valuable resource for practitioners facing complex control challenges in various domains.

Notwithstanding the successful design of state-of-the-art cooperative control protocols to accomplish formation tracking for multiple unmanned aerial vehicles (UAVs), the assurance of performance optimality cannot be guaranteed in the face of complex disturbances affecting these multi-UAV systems. In order to surmount this challenge, this research endeavor aims to establish a feedforward-feedback learning-based optimal control methodology to facilitate cooperative UAV formation tracking in the presence of intricate disturbances. To be more precise, by leveraging backstepping-based feedback control, the problem of UAV formation tracking is transformed into an equivalent optimal regulation problem. Consequently, a learning-based feedforward control scheme is devised, wherein the cooperative policy iteration algorithm is formulated based on a two-player zero-sum game. The critic-only echo state network (ESN) is employed to approximate the optimal feedforward control policies, with the inclusion of an online adaptive tuning law and compensation terms to alleviate the persistence of excitation condition and eliminate the need for an initial admissible control. As a result, the closed-loop stability is guaranteed in terms of uniformly ultimately boundedness for tracking errors and ESN weights.Note to Practitioners—In real-world scenarios, the flight of multiple UAVs is invariably affected by intricate disturbances, resulting in compromised tracking precision. There is an urgent need to enhance resistance to disturbances and ensure optimal performance for cooperative formation tracking of multiple UAVs. Beyond the capabilities of model-based controllers, the integration of reinforcement learning has shown promise in achieving robust control actions. By introducing the cooperative policy iteration algorithm based on a two-player zero-sum game, the tracking performances of UAV formation can be further optimized. In order to facilitate the practical application of reinforcement learning in UAV systems, our proposed algorithm addresses the persistency of excitation condition by incorporating innovative compensation terms into the ESN tuning law. Furthermore, we resolve the requirement for initial admissible control by introducing a novel piecewise compensation term into the ESN tuning law, which is based on a newly proposed Lyapunov function.

With the rapid development of V2V and V2I communication technologies and autonomous control systems, autonomous vehicles (AVs) are gaining increasing popularity. Small-spacing AV platoons offer advantages such as enhanced road capacity and energy efficiency. However, in non-ideal communication environments, packet loss can cause partial loss of trajectory information, resulting in discontinuous tracking. This may induce significant transients and trigger actuator saturation, aggravating traffic disturbances. In bidirectional platoons, where control signals propagate in both directions, the impact of such disruptions is further amplified due to mutual vehicle interdependence. This paper addresses these challenges by considering asymmetric actuator saturation, discontinuous tracking trajectories, and non-zero initial spacing errors in bidirectional AV platoons. A continuous control law is designed based on coupled sliding mode control, and Lyapunov stability theory is employed to ensure both trajectory tracking stability and string stability. Our contributions include the development of a modified spacing policy that not only eliminates large transients and string instability caused by non-zero initial spacing errors but also ensures rapid convergence to the desired spacing within a finite and adjustable time frame. Furthermore, a variant sigmoid function is introduced to actively smooth the discontinuous tracking trajectories, thereby reducing communication demands and suppressing transients. An auxiliary system is also designed to manage actuator saturation effectively, ensuring provable stability and fully leveraging actuator capabilities. Results demonstrate that the control strategy achieves both trajectory tracking stability and string stability, while also enabling rapid tracking performance and maintaining small spacing errors by making full use of actuator potential.

Due to limited mileage and high schedule constraints, electric buses need to maximize not only energy efficiency but also mobility flexibility, especially in lanes where multiple bus lines merge. Connected-autonomous electric buses (CAEBs) indeed reduce inter-vehicle gaps to minimize the impact of bus stops on road capacity, but lead to frequent acceleration and deceleration to ensure safety. Therefore, this paper regards the CAEBs in the merged lane of bus lines as a whole platoon to study the cooperative control algorithm aiming at the tradeoff between energy saving and mobility. Model predictive control and optimal control are combined to design CAEB control inputs where saturation inputs, safe inter-vehicle spacing constraints, and external disturbances are integrated. This paper finds sufficient conditions for the unique solution of the non-convex optimization objective caused by the higher-order energy terms. In addition, this paper proves the semi-negative characterization of the symmetry matrix of higher-order energy terms to realize the asymptotic stability of CAEB platoons. Comparative simulations show that the cooperative control algorithm effectively trades off mobility and energy consumption even in emergency scenarios, and achieves a 25% reduction in energy consumption with only a 2.2% reduction in mobility.

Connected vehicles (CVs) are conductive to promoting the transition from purely regular vehicles to purely connected autonomous vehicles where CVs are regarded as regular vehicles equipped with driver assistance systems (DASs). CVs can share status information (i.e., position, velocity, etc.) between each other through vehicle-to-vehicle communication technology, and DASs can provide CV drivers with motion suggestions (e.g., optimal velocity, etc.) based on the shared information. However, CV drivers may not completely follow these suggestions, and may combine them with their own driving experience and perception of traffic information which may be influenced by the interference of vehicles on the adjacent lane. Hence, this paper proposes a two-lane car-following model to simulate CVs under connected environment. The proposed model incorporates the compliance rate of CV drivers to DASs and considers the interference of vehicles on the adjacent lane to CV drivers by introducing the visual angle and its change rate of CV drivers. Linear stability analysis and numerical simulations of homogeneous and heterogeneous traffic flow are performed. Results show that the increases in the penetration rate of CVs and the compliance rate of CV drivers promote traffic stability, while the interference of vehicles on the adjacent lane reduces traffic stability.

Autonomous vehicle (AV) platoons, especially those with the bidirectional communication topology, have significant practical value, as they not only increase link capacity and reduce vehicle energy consumption, but also reduce the consumption of communication resources. Small gaps between AVs in a platoon easily lead to emergency braking or even collisions between consecutive AVs. This paper applies barrier Lyapunov functions to collision avoidance between AVs in a bidirectional platoon during trajectory tracking. Based on backstepping technique, an adaptive collision-free platoon trajectory tracking control algorithm is developed to distributedly design control laws for each AV in the platoon. The control algorithm does not need to introduce additional car-following models to simulate AV driving, and only needs to integrate the position trajectories of consecutive AVs to avoid inter-vehicle collisions. Two sign functions are introduced into the control laws of each AV to ensure strong string stability for bidirectional AV platoons. Moreover, uncertainties and external disturbances in vehicle motion are effectively compensated by introducing adaptation laws. Strong string stability is rigorously proved. CarSIM-based comparison simulations verify the effectiveness of the proposed control algorithm in avoiding inter-vehicle collisions, compensating for uncertainties in vehicle motion, and suppressing the amplification of spacing errors along the platoon.

As inter-vehicle communication and automatic driving technology continue to develop, but are not yet popular, regular vehicles, connected vehicles and connected autonomous vehicles (CAVs) will coexist on the road for a long time. This mixed traffic environment highlights the need to theoretically analyze the impacts of some connected and autonomous technologies (i.e., accurate detection technology, inter-vehicle communication technology, data storage technology and inter-vehicle cooperation technology) on the stable operation of heterogeneous traffic. According to the characteristics of the vehicles equipped with different technologies, this paper extends the corresponding car-following models based on the optimal velocity model. Through these analytical models, these connected and autonomous technologies are quantified and the linear stability analyses are conducted. Numerical simulation shows that the inter-vehicle communication between three vehicles, and two previous time-step data storage or two future time-step inter-vehicle cooperation are sufficient to stabilize the mixed traffic. As CAV penetration rates increase, the stability of heterogeneous traffic is improved. Furthermore, the stability of heterogeneous traffic is weakened when the size of the largest single fleet increases. These theoretical results can serve as a quantitative tool for scholars and vehicle designers before drawing any qualitative conclusions of related technologies on heterogeneous fleet stability to avoid wasting resources such as data storage capacity and inter-vehicle communication ranges.