In this work, an enhanced model-free adaptive control with tracking differentiator (TD-EMFAC) method is proposed for the attitude control of a two-wheel balance vehicle. First, a dynamic linearization technique based on a pseudo partial derivative (PPD) with time-varying parameter factors is introduced to dynamically linearize the dynamic process of the two-wheel balance vehicle. Second, a model free adaptive controller is designed. At the same time, the controller considers the time delay of the system in the design process. By proposing an adaptive factor coefficient and combining it with the Smith prediction method, the designed controller can manage systems with time delays. The advantage of the proposed method is that the entire controller design process is based on the input and output data of the controlled object without requiring an accurate mathematical model of the controlled object. Third, a series of experimental results based on an actual two-wheel balance vehicle verify the effectiveness of the proposed method. A strict mathematical proof verifies the stability of the proposed method.

Fast-changing demand in real traffic systems always leads to asymmetrical traffic flow and queues, which aggravates congestion and energy waste. In this paper, the traffic signal control problem of multi-phase intersections was studied with fast-changing traffic flows. First, a novel model-free adaptive control-based symmetrical queuing balancing method was designed by using the full-format dynamic linearization (FFDL) technique. Second, in order to deal with the fast-changing traffic flow, a radial basis function neural network (RBFNN) was added to adjust parameters in a two-layer structure. Moreover, a variable cycle tuning algorithm was introduced to further reduce the time loss. Using the simulation, the proposed algorithm was compared with three other control strategies under low and high traffic demand, respectively, and the results showed the capability of the proposed algorithm.

The autonomous traffic system has imposed higher requirements on the speed estimation of connected vehicles, where the speed of connected vehicles, as one of the control conditions for refined traffic management, plays a crucial role in the evaluation and optimization of network performance. In this paper, we propose a multi-source speed measurement sensor network consensus filtering (MSCF) algorithm based on information weight for the problem of optimal speed consistency estimation for connected vehicles. Specifically, we first utilize dynamic linearization techniques and data-driven parameter identification algorithms to handle the derived state equations of connected vehicles. We then establish observation models for four different types of sensors and construct distributed direct and indirect measurement models by dynamically adjusting the information weights of sensor nodes. Following this, we combine the Kalman consistency filtering algorithm to derive the speed state estimation update rate and design a new state estimator to achieve the optimal consistent convergence estimation for connected vehicles’ speed. The MSCF algorithm can solve the problem of consistency filtering for noisy sensor data under observation- and communication-constrained conditions, enabling each sensor node to obtain a consistent convergence estimation value for the speed of the connected vehicle. The convergence of the algorithm is proved using the Lyapunov function. Through numerical simulation, the results are verified, indicating that compared to existing methods, this method can achieve a higher precision speed estimation effect.

This paper studies the containment control problem of heterogeneous multi-agent systems (MASs) with multiple leaders. The follower agent dynamics are assumed to be unknown and nonlinear. First, each follower is transformed into an incremental data description based on the dynamic linearization technique. Then, a distributed model-free adaptive containment control law is proposed such that all followers will be driven into the convex hull of the leaders. Furthermore, the algorithm is extended to the time-switching and dynamic leaders case. As a data-driven approach, the proposed controller design uses only the received input and output (I/O) data of these agents rather than agent mathematical models. Finally, to test the potential in real applications, three representative examples considering various environment factors, including external disturbances, are simulated to show the effectiveness and resilience of this method.

This paper presents a scheme for the feedforward–feedback longitudinal trajectory tracking control of buses. The scheme is specifically designed to address the periodic and repetitive nature of bus operations. First, the vehicle’s longitudinal dynamics are linearized along the iterative axis via full-form dynamic linearization (FFDL), and parameters such as the pseudo-gradient are estimated with data and a projection algorithm to grasp the dynamic characteristics of the system. To better handle complex real-world traffic conditions, we then propose the forward and backward structure. At the same time, the iterative axis design performance index is verified, and the forward partial control law, namely, model-free adaptive iterative learning control (MFAILC), is derived. In order to further enhance the robustness to disturbance and other factors, the control law of the feedback part is designed with active disturbance rejection control (ADRC). A key advantage of this control approach is its sole reliance on the data generated during vehicle operation, without the need for specific information about the controlled vehicle. This feature enables the method to be adaptable to different vehicle types and resilient to various disturbances. Finally, MATLAB simulations are used to verify the practicality of the proposed method.

The increasing complexity and persistent network security challenges in traffic signal control are key issues requiring urgent attention to meet growing traffic demands. To address these issues, this paper proposes a resilient distributed model-free adaptive traffic signal control strategy (CDL-DMFAC) that integrates controller dynamic linearization (CDL) with multi-agent modeling. In the proposed framework, each signal phase at an intersection is modeled as an independent agent, and a compact form dynamic linearization (CFDL) is employed to construct an unknown ideal controller, enabling balanced control of multi-phase queue lengths. Furthermore, a denial-of-service (DoS) attack compensation mechanism is designed to mitigate the negative impact of communication interruptions or delays on signal timing decisions. Experimental results show that CDL-DMFAC effectively suppresses queue growth and delay accumulation under various attack intensities, with its performance advantage becoming more pronounced as attack severity increases. Notably, under the most challenging scenario—high traffic demand with multiple intersections simultaneously subjected to DoS attacks—the proposed method achieves reductions of 28.3% in average queue length and 36.32% in average waiting time compared to conventional signal control methods. These results highlight the method’s strong resilience against attacks, operational stability, and potential for deployment in larger-scale urban traffic networks.