The global asymptotic stabilization problem is investigated for a class of stochastic nonlinear time-varying delay systems under the weaker condition on nonlinear functions. The new parameter-dependent state and output feedback controllers are, respectively, proposed. Based on the stochastic time-delay system stability criterion, by tactfully introducing a suitable Lyapunov-Krasovskii functional, the globally asymptotically stable in probability of the closed-loop system is guaranteed by rigorous proof. As a practical application, the stochastic model of a two-stage chemical reactor system is established by reasonably introducing the Gaussian white noise. The developed approach is applied to the control design for this practical system. The simulation results demonstrate the efficiency of the proposed design approach.

This paper addresses a distributed estimator design problem for linear systems deployed over sensor networks within a multiple communication channels (MCCs) framework. A practical scenario is taken into account such that the channel used for communication can be switched and the switching is governed by a Markov chain. With the existence of communicational imperfections and external disturbances, an estimation algorithm is proposed such that the developed distributed estimators are able to give accurate state estimates against the channel switching phenomenon. The distributed estimation framework is applied to a chemical process to illustrate the effectiveness of the proposed methodology and the superiority of the MCCs framework featured by channel switching.

One of the major obstacles along the way of electric vehicles' (EVs') wider global adoption is their limited driving range. Extreme cold or hot environments can further impact the EV's range as a significant amount of energy is needed for cabin and battery temperature regulation while the battery's power and energy capacity are also impeded. To overcome this issue, we present an optimal control strategy based on nonlinear model predictive control (NMPC) for integrated thermal management (ITM) of the battery and cabin of EVs, where the proposed NMPC simultaneously optimizes the EV range and cabin comfort in real time. Firstly, the components of the designed ITM system are introduced and control-oriented modeling is done. Secondly, to demonstrate and validate the benefits of the proposed ITM, an optimal control problem is defined and dynamic programming (DP) is employed to find the global optimal solution. Thirdly, for practical implementation, NMPC-based control strategy is developed, where the cost function design and weights calibration are done in comparison with DP global optimal solution. Weight-tuning results show that our NMPC-based approach can achieve close driving range maximization as compared to the DP benchmark while ensuring cabin comfort. The developed NMPC-based ITM strategy is further illustrated by comparing its performance to two additional benchmark strategies, i.e., rule-based control and cabin heating only. Finally, our simulation results also identify several important factors that impact the benefits of the proposed NMPC-based ITM, which are used to summarize the operating conditions under which the proposed ITM is critically needed.

Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with enhanced safety, energy efficiency, and sustainability. One typical control strategy for CAVs is the so-called cooperative adaptive cruise control (CACC) where vehicles drive in platoons and cooperate to achieve safe and efficient transportation. In this study, we formulate CACC as a multi-agent reinforcement learning (MARL) problem. Diverging from existing MARL methods that use centralized training and decentralized execution which require not only a centralized communication mechanism but also dense inter-agent communication during training and online adaptation, we propose a fully decentralized MARL framework for enhanced efficiency and scalability. In addition, a quantization-based communication scheme is proposed to reduce the communication overhead without significantly degrading the control performance. This is achieved by employing randomized rounding numbers to quantize each piece of communicated information and only communicating non-zero components after quantization. Extensive experimentation in two distinct CACC settings reveals that the proposed MARL framework consistently achieves superior performance over several contemporary benchmarks in terms of both communication efficiency and control efficacy. In the appendix, we show that our proposed framework's applicability extends beyond CACC, showing promise for broader intelligent transportation systems with intricate action and state spaces.