In this work, a novel distributed fault-tolerant control strategy is proposed for cooperative operation of high-speed trains (HSTs) subject to unmodeled dynamics, time-varying external disturbances, input saturation and actuator partial failures. The cooperative control of HSTs is first transformed to the consensus problem of a multi-agent system. Then, the distributed control laws that are equipped with a command filter and an adaptive neural network are developed to achieve the consensus task among the HSTs with predefined displacement and speed profiles. Further, an auxiliary dynamical system is introduced to compensate for the influence of input saturation. Moreover, an adaptive failure compensation strategy is designed to deal with the unknown and time-varying actuator faults. The convergence of the proposed controller is analyzed rigorously by applying the Lyapunov theorem, and the effectiveness is demonstrated by numerical simulations.

This paper investigates the path tracking control problem of autonomous vehicles subject to modelling uncertainties and external disturbances. The problem is approached by employing a 2-degree of freedom vehicle model, which is reformulated into a newly defined parametric form with the system uncertainties being lumped into an unknown parametric vector. On top of the parametric system representation, a novel robust adaptive learning control (RALC) approach is then developed, which estimates the system uncertainties through iterative learning while treating the external disturbances by adopting a robust term. It is shown that the proposed approach is able to improve the lateral tracking performance gradually through learning from previous control experiences, despite only partial knowledge of the vehicle dynamics being available. It is noteworthy that a novel technique targeting at the non-square input distribution matrix is employed so as to deal with the under-actuation property of the vehicle dynamics, which extends the adaptive learning control theory from square systems to non-square systems. Moreover, the convergence properties of the RALC algorithm are analysed under the framework of Lyapunov-like theory by virtue of the composite energy function and the λ-norm. The effectiveness of the proposed control scheme is verified by representative simulation examples and comparisons with existing methods.

A novel rule-based control strategy is proposed for the energy management of parallel hybrid electric vehicles (HEVs): the torque-leveling threshold-changing strategy (TTS). In contrast to the most commonly used heuristic electric assist control strategy (EACS) that is designed based on the load following approach, the TTS proposes and applies the new fundamental concept of torque leveling. This mechanism operates the engine with a constant torque when the engine is active, thus ensuring the engine works at an efficient operating point. The TTS additionally extends and uses a design concept that has previously been proposed in the context of series HEVs, the threshold-changing mechanism, to operate the HEV in a charge-sustaining manner. By exploiting this new set of design principles for parallel HEVs, the TTS realizes energy source control sharing behavior that is reminiscent to optimization-based methods. To show its effectiveness, the TTS is implemented to a through-the-road (TTR) HEV and benchmarked against two conventional control strategies: dynamic programming (DP) and the EACS. The results show that the TTS, despite its simplicity, is able to deliver comparable fuel economy as the global optimization approach DP and thus achieve significant improvement compared to the EACS. In addition, to facilitate real-time application, a simplified version of the TTS (STTS) is also developed, which is able to deliver similar performance as the TTS but is more simple to implement in practice.

In this paper, the distributed cooperative control of virtual coupling high-speed trains (HSTs) subject to full-state constraints, actuator limitations, dynamical uncertainties and environmental disturbances is investigated. Targeting at the full-state constraints in cooperative operation of HSTs, a distributed nonlinear state-dependent function (DNSDF) is first proposed to convert the state-constrained problem of the leader-following consensus control to the boundedness problem of DNSDF. Then, the distributed control law of each train is designed by combining the command filtering backstepping method and the adaptive neural network approximation technique. Meanwhile, combined with the DNSDF, a novel auxiliary dynamical system (ADS) is designed to compensate for the adverse effects of actuator input saturation, and thus ensure the closed-loop stability of the HSTs system when the state constraints and the input saturation are considered simultaneously. By utilizing the Lyapunov theory, the convergence of the proposed controller is analyzed. Finally, the feasibility and effectiveness of the proposed control scheme are verified by simulations. Note to Practitioners —This work was motivated by the problem of cooperative control for virtual coupling HSTs with different initial states, actuator input saturation, full-state constraints, etc. The proposed approach addresses the train position and speed constraints by applying DNSDF, which can ensure train coordinated operation with relative braking distance and further reduce the tracking interval between trains. Specifically, the upper bound of the train position constraint varies with the real-time position of the preceding train and the relative speed of the adjacent trains, rather than a constant value. More importantly, an ADS is designed based on the DNSDF, which guarantees the stability of the controller when the input saturation and state constraints exist simultaneously, and avoids the chattering phenomenon of the train control input. The simulation results show that the trains can operate cooperatively at any initial speed within the speed limitations. In future, we will focus on the cooperative control of HSTs with communication delay, and the energy saving optimization cooperative control of HSTs.

In this work, the adaptive iterative learning control (AILC) for a generic class of non-square nonlinear systems is investigated in presence of unknown control gain matrices and non-parametric iteration-varying uncertainties. Differently from the existing approaches, the present work develops a unified, structurally simple and user-friendly AILC method, which is effective to handle nonlinear systems with parametric or non-parametric uncertainties, square or non-square input matrices, known or unknown control directions. From the design point of view, the proposed approach extends the AILC approach to non-square systems with unknown control gain matrices, which contributes significantly not only to refine the theory of AILC, but also to widen its application scope. The convergence of the proposed control algorithms are analysed rigorously by using the composite energy function methodology, and their effectiveness have been verified through an illustrated example.

This work presents a novel design framework of adaptive iterative learning control (ILC) approach for a class of uncertain nonlinear systems. By using the closed-loop reference model that can be viewed as an observer, the proposed adaptive ILC approach can be adapted to deal with the output tracking problem of nonlinear systems with unavailable system states. In the systems considered, two classes of uncertainties are taken into account, including parametric input disturbances and input distribution uncertainties. To facilitate the controller design and convergence analysis, the composite energy function (CEF) methodology is employed. The design framework in this brief is novel and widely applicable, which extends the CEF-based ILC approach to output tracking control of nonlinear systems without requiring full knowledge of state information and complicated observer design process. To show the effectiveness of the proposed design framework and control algorithms, two numerical examples are illustrated.