This paper is concerned with the human-machine shared control issue for vehicle lane keeping systems via a new fuzzy observer-based event-triggered method. In order to capture system nonlinear and uncertain characteristics such as nonlinear tire dynamics, varying velocity and driver behavioral uncertainties, Takagi-Sugeno fuzzy approach is employed to model the global driver-vehicle-road system. After system modeling, the fuzzy observer-based control structure is considered because a full states information is not available in practical driving environment. Then, most existing human-machine shared control methods are based on the periodic sampling communication mechanism. However, since the network bandwidth is limited, the above mechanism may cause oversampling and communication congestion. Thus, an adaptive event-triggered mechanism is introduced between the observer and the controller to mitigate the communication burden and improve the bandwidth utilization. Based on Lyapunov functional theory, a set of sufficient conditions are given to calculate desired human-machine shared controllers. Finally, simulation tests are implemented on Matlab/Simulink-CarSim platform and simulation results illustrate that the proposed method can achieve a favorable improvement in the lane keeping capability, the driver handling comfort and the network bandwidth utilization.

This letter is one of the latest research reports from IEEE TIV's Decentralized and Hybrid Workshops (DHW) on Ethics, Responsibility, and Sustainability (ERS). In the last year, we organized 1 distributed/decentralized and hybrid symposia (DHS), 2 DHWs, and 7 seminars. The following is a brief review of the findings from our DHS, DHWs, and Seminars, in particularly about a novel emission regulatory framework for intelligent transportation systems (ITS) and smart cities (SCs) in ERS. Vehicle emissions are one of the main anthropogenic contributors to global warming and atmospheric pollution. However, effective regulation of transportation emissions is still challenging, even in intelligent transportation systems and smart cities. To realize accurate estimations and credible predictions, a parallel framework is proposed in this letter, consisting of a parallel transportation level and a parallel vehicle level. Through low-cost computational experiments and parallel executions, both accurate estimation and emissionaware optimal planning can be achieved. Unlike previous emission models, modern aftertreatment systems (ATS) are considered as a core module in this novel modular integration model (MIM). An application case on $\mathrm{CO}_{2}$ emissions is conducted to validate the fundamental function of this framework. Compared with two well-known models, MIM shows better performance than two baselines, and the relative errors are below 4% even with unknown engine states.

Cabin heating requirement and engine efficiency degradation in cold weather lead to considerable increase in fuel consumption of hybrid electric vehicles (HEVs). This article focuses on the development of a real-time control framework for integrated power and thermal management (i-PTM) of a connected HEV through incorporating vehicle speed preview informed by traffic connectivity. To evaluate the ceiling of the fuel economy improvement via i-PTM, forward dynamic programming (FDP) is adopted as a benchmark study to find the global optimization solution and then implemented into a centralized model predictive controller (CMPC). Regarding the slow response associated with the coupled electric-thermal dynamics in the HEV, a two-layer hierarchical MPC (HMPC) framework is developed, which exploits vehicle speed preview predictions over short and long prediction horizons to optimize fuel consumption while satisfying power and cabin heating demand. The simulation results show that the proposed HMPC provides near-optimal solutions in reference to the benchmark while reduces up to 79% computation burden compared with CMPC. Additionally, compared to a baseline power management (PM) controller, up to 5.46% improvement on fuel economy can be achieved by HMPC for congested driving scenarios. Finally, the real-time feasibility of the HMPC is assessed in a dSPACE rapid prototyping system.

This article investigates the event-triggered fuzzy filtering issue for vehicle sideslip angle estimation with consideration of data quantization and dropouts. First, an uncertain Takagi–Sugeno fuzzy model is developed to describe vehicle nonlinear dynamics resulted from nonlinear tire dynamics, varying velocity, uncertain mass, and yaw moment inertia. Then, an adaptive event-triggered scheme is introduced between the sensor and the filter for the decision of releasing sampled data to economize limited network resource. Moreover, the network-induced constraints, such as delay, data quantization, and dropouts, are taken into account to improve the robustness of the filtering method. Based on the Lyapunov stability theory, a new event-triggered asynchronous fuzzy filtering method is proposed by establishing an augmented Lyapunov–Krasovskii functional candidate and applying integral inequalities in the derivation. Finally, simulation results are presented to verify the advantages of the proposed method in comparison with the existing results.

Obstacle avoidance is essential for an omnidirectional mobile robotic arm (OMRA) to accomplish a given task in a complex environment. A hybrid orthogonal repetitive motion and obstacle avoidance (HORMAOA) scheme is developed and analyzed to address the problem of the OMRA not being able to accurately return to the starting position after completing the obstacle avoidance task. Compared with the traditional obstacle avoidance schemes, the HORMAOA scheme decouples joint space error and Cartesian space error, which enables the OMRA to achieve obstacle avoidance accurately, physical limit avoidance, and repetitive motion tasks. Moreover, the HORMAOA scheme is transformed into a piecewise linear projection equation (PLPE) and solved using a linear-variational-inequality-based primal-dual neural network (LVI-PDNN), which can effectively obtain the optimal solution of the HORMAOA scheme. The validity and accuracy of the scheme are verified through illustrative examples, experiments, and comparisons.