In this paper, a novel data-driven model-free adaptive predictive control method based on lazy learning technique is proposed for a class of discrete-time single-input and single-output nonlinear systems. The feature of the proposed approach is that the controller is designed only using the input-output (I/O) measurement data of the system by means of a novel dynamic linearization technique with a new concept termed pseudogradient (PG). Moreover, the predictive function is implemented in the controller using a lazy-learning (LL)-based PG predictive algorithm, such that the controller not only shows good robustness but also can realize the effect of model-free adaptive prediction for the sudden change of the desired signal. Further, since the LL technique has the characteristic of database queries, both the online and offline I/O measurement data are fully and simultaneously utilized to real-time adjust the controller parameters during the control process. Moreover, the stability of the proposed method is guaranteed by rigorous mathematical analysis. Meanwhile, the numerical simulations and the laboratory experiments implemented on a practical three-tank water level control system both verify the effectiveness of the proposed approach.

In this paper, a novel model-free adaptive control (MFAC) algorithm based on a dual successive projection (DuSP)-MFAC method is proposed, and it is analyzed using the introduced DuSP method and the symmetrically similar structures of the controller and its parameter estimator of MFAC. Then, the proposed DuSP-MFAC scheme is successfully implemented in an autonomous car "Ruilong" for the lateral tracking control problem via converting the trajectory tracking problem into a stabilization problem by using the proposed preview-deviation-yaw angle. This MFAC-based lateral tracking control method was tested and demonstrated satisfactory performance on real roads in Fengtai, Beijing, China, and through successful participation in the Chinese Smart Car Future Challenge Competition held in 2015 and 2016.

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.

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.

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.