Shida Liu

To address the challenges of longitudinal cooperative platoon control for multiple heavy‐duty trucks with time‐delay compensation under constraints, this paper presents a novel model‐free adaptive control algorithm termed TD‐cMFAC. The algorithm employs a pseudo partial derivative (PPD), a time‐varying parameter, to linearize the nonlinear dynamics of the multivehicle cooperative system using dynamic linearization techniques. Afterward, a dedicated TD‐cMFAC controller is then designed. To compensate for system time delays, a hybrid mechanism integrating a Smith predictor and a tracking differentiator (TD) is developed. The controller explicitly accounts for the input and output constraints inherent in practical control processes. The primary advantage of the TD‐cMFAC algorithm is its reliance solely on the input/output data of the multivehicle system throughout the control process while maintaining robust performance in the presence of time delays and constraints. Theoretical stability analysis confirms the robustness of the proposed method. By utilizing a MATLAB/Simulink and TruckSim co‐simulation interface, the effectiveness of the control strategy is demonstrated under complex driving is demonstrated, and its practical applicability is further validated through field tests conducted with an autonomous driving platform using heavy‐duty trucks on a test road in Tianjin, China.

This paper proposes a novel reinforcement learning‐based model‐free adaptive control (RL‐MFAC) algorithm for the path tracking of autonomous vehicles. By using dynamic linearization techniques, lateral and longitudinal dynamic vehicle processes are dynamically linearized, and then an MFAC controller is designed. To optimize the MFAC controller parameters for better performance, RL, with its powerful self‐learning capabilities, is introduced. The proposed control strategy offers rigorous stability. Through strict semi‐physical simulation validation on the CarSim‐MATLAB/Simulink software integration platform, this method demonstrates good performance in vehicle longitudinal and lateral control simulations, validating the effectiveness of the algorithm.

Multi-axle electric heavy-duty trucks face significant challenges in maintaining braking stability and achieving real-time control during regenerative braking due to their large mass and complex inter-axle coupling dynamics. To address these issues, this paper proposes an improved model predictive control (IMPC) strategy that enhances computational efficiency and control responsiveness through an instantaneous response mechanism. The approach integrates a first-order error attenuation term within the MPC framework and employs an extended Kalman filter to estimate tire–road friction in real time, enabling adaptive adjustment between energy recovery and stability objectives under varying road conditions. A control barrier function constraint is further introduced to ensure smooth and safe regenerative braking. Simulation results demonstrate improved energy recovery efficiency and faster convergence, while real-vehicle tests confirm that the IMPC maintains superior real-time performance and adaptability under complex operating conditions, reducing average computation time by approximately 14% compared with conventional MPC and showing strong potential for practical deployment.

In this work, an improved model‐free adaptive control algorithm combined with data compensation (IMFAC‐DC) is proposed for the attitude control of a two‐wheeled balancing vehicle with time delay and data dropout. First, the method introduces a dynamic linearization technique based on a pseudo partial derivative (PPD) with time‐varying parameter factors to dynamically linearize the dynamic process of the two‐wheeled balancing vehicle. Second, to address the existing time delay issue in the system, an improved model‐free adaptive controller is designed by proposing an adaptive factor coefficient and combining it with the Smith prediction method. Third, considering the presence of data dropout in practical systems, a novel data compensation mechanism is proposed to address this issue. The IMFAC‐DC algorithm utilizes only the input–output data of the controlled object to complete controller design. It combines the advantages of MFAC and the Smith predictor, and it exhibits good control performance for systems with time delay and data dropout. A series of experimental results based on an actual two‐wheeled balancing vehicle with time delay and data dropout are used to verify the effectiveness of the proposed method. A strict mathematical proof verifies the stability of the proposed method.

In this paper, an enhanced model-free adaptive control algorithm considering time delay is proposed for the path tracking problem of autonomous vehicles. First, a path tracking mechanism based on the preview-deviation-yaw angle is proposed, which transforms the path tracking problem into a control problem of the preview-deviation-yaw angle. A novel partial form dynamic linearization (PFDL) technique is then employed to transform the vehicle dynamic models into a discrete-time data model with a time-varying pseudogradient (PG), and the proposed controller (PFDL-EMFAC) is designed based on this data model. Moreover, a compensation mechanism is designed for the system time delay by combining the Smith predictor and tracking differentiator (TD). Notably, implementing the controller does not involve any model information; it is a purely data-driven control method. Furthermore, the convergence of the proposed controller is proven via mathematical analysis. The validity of the proposed controller was validated through CarSim-MATLAB cosimulation, and its applicability was verified via the Ankai HFF6668GEV1 autonomous driving platform on a test road in Hefei, China.

This paper studies the consensus problem for a class of unknown heterogeneous nonlinear multi-agent systems via a network with random packet dropouts. Based on the dynamic linearization technique, novel model-free adaptive consensus protocols with the data compensation mechanism are designed for both leaderless and leader-following cases. The advantage of this approach is that only neighborhood input and output data of the agents are required in the protocol design. For the stability analysis, a new Squeeze Theorem based method is developed to derive the theoretic results instead of the traditional contraction mapping principle used in model-free adaptive control. It is shown that the consensus can be achieved for both leaderless and leader-following cases if the communication topology is strongly connected. Finally, numerical simulations verifying the correctness of the theoretical results are given.

A novel robust model-free adaptive control (R-DMFAC) algorithm is proposed to address the path tracking control problem of autonomous vehicles in the presence of external measurement disturbances. First, the preview-deviation-yaw angle based tracking method is proposed, which transforms the path tracking problem into the preview-deviation-yaw angle control problem. Second, a novel dynamic linearization technique is employed to convert the nonlinear dynamical model, based on preview-deviation-yaw angle, into a linear data model with pseudo partial derivative (PPD), and the proposed algorithm (PFDL-EMFAC) is designed based on this data model. Furthermore, a measurement disturbance suppression scheme is designed by introducing the decreasing factor. Notably, implementing the algorithm does not involve any model information; it is a purely data-driven control algorithm. Finally, the joint simulation results of MATLAB-Panosim platform demonstrate that the maximum tracking error of the autonomous vehicle controlled by the R-DMFAC in different scenarios can be reduced to 0.5-0.7 m, verifying the effectiveness of the control algorithm.

To address the stable grasping control issue in manipulator grasping systems, this manuscript proposes an improved multiverse optimizer‐based anti‐saturation model‐free adaptive control (IMVO‐AS‐MFAC) algorithm. Initially, the manuscript converts the manipulator grasping system into an equivalent data model through dynamic linearization techniques. Then, based on the dynamic linearization model, the IMVO‐AS‐MFAC controller is designed. To address the actuator saturation problem that commonly occurs during the clamping process of manipulator grasping systems, a saturation parameter is introduced into the IMVO‐AS‐MFAC algorithm. Meanwhile, the controller parameters are optimized using an improved multiverse optimizer algorithm, which involves modifications to the initial population distribution and location update strategy. The improved algorithm demonstrates more competitive optimization performance compared to the traditional multiverse optimizer. The major advantage of the IMVO‐AS‐MFAC algorithm lies in the fact that only the input and output data of the manipulator grasping system are required throughout the entire control process, and the controller parameters are derived using an optimization algorithm rather than relying on empirical knowledge. Furthermore, rigorous mathematical analysis confirms the stability of the IMVO‐AS‐MFAC approach, and its effectiveness is validated through semi‐physical experiments conducted in an environment integrating the MATLAB/Simulink module and the RecurDyn platform.