Parkour presents a highly challenging task for legged robots, requiring them to traverse various terrains with agile and smooth locomotion. This necessitates comprehensive understanding of both the robot's own state and the surrounding terrain, despite the inherent unreliability of robot perception and actuation. Current state-of-the-art methods either rely on complex pre-trained high-level terrain reconstruction modules or limit the maximum potential of robot parkour to avoid failure due to inaccurate perception. In this paper, we propose a one-stage end-to-end learning-based parkour framework: Parkour with Implicit-Explicit learning framework for legged robots (PIE) that leverages dual-level implicit-explicit estimation. With this mechanism, even a low-cost quadruped robot equipped with an unreliable egocentric depth camera can achieve exceptional performance on challenging parkour terrains using a relatively simple training process and reward function. While the training process is conducted entirely in simulation, our real-world validation demonstrates successful zero-shot deployment of our framework, showcasing superior parkour performance on harsh terrains.

The longitudinal control of the platoon of connected and automated vehicles (CAVs) has gained extensive attention in recent transportation research. A majority of existing results are based on linearized third-order vehicular models, under the premise that a complete priori knowledge of vehicle dynamics is available. This article focuses on a general class of third-order nonlinear CAVs with parametric uncertainty and unknown external disturbance which cannot be linearized. A vehicle-to-vehicle (V2V) communication-based cooperative adaptive backstepping control scheme is proposed, in which unknown parameters and disturbance bounds are estimated on-line. Since the transfer function of linear systems cannot be applied to nonlinear systems to guarantee string stability, asymmetric time-varying constraints are employed to prevent the spacing errors from growing up. A realistic example is considered to verify the feasibility of the control algorithm.

This article focuses on the investigation of finite-time dissipative state estimation for Markov jump neural networks. First, in view of the subsistent phenomenon that the state estimator cannot capture the system modes synchronously, the hidden Markov model with partly unknown probabilities is introduced in this article to describe such asynchronization constraint. For the upper limit of network bandwidth and computing resources, a novel dynamic event-triggered transmission mechanism, whose threshold parameter is constructed as an adjustable diagonal matrix, is set between the estimator and the original system to avoid data collision and save energy. Then, with the assistance of Lyapunov techniques, an event-based asynchronous state estimator is designed to ensure that the resulting system is finite-time bounded with a prescribed dissipation performance index. Ultimately, the effectiveness of the proposed estimator design approach combining with a dynamic event-triggered transmission mechanism is demonstrated by a numerical example.

This article is devoted to the investigation of reduced-order dissipative filtering for Takagi–Sugeno (T–S) fuzzy Markov jump systems with the event-triggered mechanism. For the proposed event-triggered mechanism, its threshold parameter is constructed as a special diagonal matrix which can improve system performance by flexibly adjusting the matrix elements. Due to the impact of the sampling behaviors and the environmental disturbance, the asynchronization between the filter and the estimated system is considered in this article, which can be characterized by the hidden Markov model. Through handling the linear matrix inequalities (LMIs) with some slack matrices, event-triggered fuzzy filters are designed to guarantee the resulting system is stochastically stable and strictly dissipative. The proposed filter parameters are obtained by solving LMIs. Ultimately, both the effectiveness and advantages of the proposed reduced-order filter with the event-triggered mechanism are verified by a practical example.

In this paper, the problem of resilient observer-based robust control is considered for discrete-time Markov jump systems subject to asynchronous models and extended dissipativity. The model uncertainty is in the interval type, which has an ability to describe parameter fluctuating phenomenon more accurately than using the norm-bounded uncertainty. A hidden Markov chain is employed to depict the mismatch between the original system and the observer-based controller. Conditions are provided to ensure the stability of the resulting closed-loop system with a desired dissipation performance regardless of the uncertainties. An example is presented to illustrate the effectiveness and potential of the proposed new design techniques.

Iterative learning control (ILC) is applied to remote control systems in which communication channels from the plant to the controller are subject to random data dropout and communication delay. Through analysis, it is shown that ILC can achieve asymptotical convergence along the iteration axis, as far as the probabilities of the data dropout and communication delay are knowna priori. Owing to the essence of feedforward‐based control ILC can perform trajectory‐tracking tasks while both the data‐dropout and the one‐step delay phenomena are taken into consideration. Theoretical analysis and simulations validate the effectiveness of the ILC algorithm for network‐based control tasks.