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Pneumatic soft actuators possess relatively fast response, inherent high flexibility, and achieve extraordinary shape morphing under large deformations. Conventionally, the entire body of soft pneumatic robots needs to be designed for specific applications. Herein, a soft pneumatic actuator design with structured fabrics as actuator skins, which has high bending stiffness variation that can accomplish multiple tasks and different deformation modes in a single body, is proposed. By adjusting the structured skin, the developed soft actuator can be tailored to achieve various deformations. It is experimentally shown that the bending stiffness of the actuator can be adjusted from 108 to 5654 N m−1. The blocking force of the actuator in bending is comparable with that of conventional fabric‐reinforced pneumatic actuators, while the actuator skins are reusable and programmable. In application, the actuators are used to construct a bionic soft gripper with multiple degrees of freedom. By switching between three grasping modes, the gripper successfully fulfills a series of tasks including lifting weights up to 1 kg and grasping objects ranging from a grain of grape to a large iron basin. This work opens up an avenue for designing structured skins for pneumatic robots with programmable deformation modes and versatile functions. [ABSTRACT FROM AUTHOR]

Cellular homeostasis requires tight coordination between nucleus and mitochondria, organelles that each possesses their own genomes. Disrupted mitonuclear communication has been found to be implicated in many aging processes. However, little is known about mitonuclear signaling regulator in sarcopenia which is a major contributor to the risk of poor health‐related quality of life, disability, and premature death in older people. High‐temperature requirement protein A2 (HtrA2/Omi) is a mitochondrial protease and plays an important role in mitochondrial proteostasis. HtrA2mnd2(−/−) mice harboring protease‐deficient HtrA2/Omi Ser276Cys missense mutants exhibit premature aging phenotype. Additionally, HtrA2/Omi has been established as a signaling regulator in nervous system and tumors. We therefore asked whether HtrA2/Omi participates in mitonuclear signaling regulation in muscle degeneration. Using motor functional, histological, and molecular biological methods, we characterized the phenotype of HtrA2mnd2(−/−) muscle. Furthermore, we isolated the gastrocnemius muscle of HtrA2mnd2(−/−) mice and determined expression of genes in mitochondrial unfolded protein response (UPRmt), mitohormesis, electron transport chain (ETC), and mitochondrial biogenesis. Here, we showed that HtrA2/Omi protease deficiency induced denervation‐independent skeletal muscle degeneration with sarcopenia phenotypes. Despite mitochondrial hypofunction, upregulation of UPRmt and mitohormesis‐related genes and elevated total reactive oxygen species (ROS) production were not observed in HtrA2mnd2(−/−) mice, contrary to previous assumptions that loss of protease activity of HtrA2/Omi would lead to mitochondrial dysfunction as a result of proteostasis disturbance and ROS burst. Instead, we showed that HtrA2/Omi protease deficiency results in different changes between the expression of nuclear DNA‐ and mitochondrial DNA‐encoded ETC subunits, which is in consistent with their transcription factors, nuclear respiratory factors 1 and 2, and coactivator peroxisome proliferator‐activated receptor γ coactivator 1α. These results reveal that loss of HtrA2/Omi protease activity induces mitonuclear imbalance via differential regulation of mitochondrial biogenesis in sarcopenia. The novel mechanistic insights may be of importance in developing new therapeutic strategies for sarcopenia. [ABSTRACT FROM AUTHOR]

Predictive cruise control concerns designing controllers for autonomous vehicles using the broadcasted information from the traffic lights such that the idle time around the intersection can be reduced. This study proposes a novel adaptive optimal control approach based on reinforcement learning to solve the predictive cruise control problem of a platoon of connected and autonomous vehicles. First, the reference velocity is determined for each autonomous vehicle in the platoon. Second, a data‐driven adaptive optimal control algorithm is developed to estimate the gains of the desired distributed optimal controllers without the exact knowledge of system dynamics. The obtained controller is able to regulate the headway, velocity, and acceleration of each vehicle in a suboptimal sense. The goal of trip time reduction is achieved without compromising vehicle safety and passenger comfort. Numerical simulations are presented to validate the efficacy of the proposed methodology. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the event‐triggered output consensus problem of heterogeneous linear multiagent systems characterized via fixed and switching directed graphs. With proper state‐dependent triggering functions, two new event‐triggered output consensus control schemes are proposed for each agent to achieve consensus. Notably, under the proposed control protocols, continuous communication among agents is not required in both controllers updating and triggering threshold detection, which means being completely continuous communication free. The communication instances are reduced significantly, and the periodic or high‐frequency communication is restrained. It is also ensured that events cannot be triggered infinitely in finite time (ie, the Zeno behavior is elegantly avoided). Meanwhile, the simulation examples are given to illustrate the theoretical analysis. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the connected cruise control problem for a platoon of human‐operated and autonomous vehicles. The autonomous vehicles can receive motional data, ie, headway and velocity information from other vehicles by wireless vehicle‐to‐vehicle communication. The use of wireless communications in information exchange between vehicles inevitably causes input delay in the platooning system. Meanwhile, unpredictable behaviors of the leading vehicle constitute exogenous disturbance for the system. An adaptive optimal control problem with input delay and disturbance is formulated, and a novel data‐driven control solution is proposed such that each vehicle in the platoon can achieve safe distance and desired velocity. By adopting an adaptive dynamic programming technique with sampled‐data system theory, a data‐driven adaptive optimal control approach is proposed for autonomous vehicles by the learning strategies of policy iteration without the accurate knowledge of the dynamics of all human drivers and vehicles. The efficacy of the proposed controller is substantiated by rigorous analysis and validated by simulation results in different scenarios. [ABSTRACT FROM AUTHOR]

This study addresses the adaptive and optimal control problem of a Quanser's 2‐degree‐of‐freedom helicopter via output feedback. In order to satisfy the requirement of digital implementation of flight controller, this study distinguishes itself through proposing a novel sampled‐data‐based approximate/adaptive dynamic programming approach. A policy iteration algorithm is presented that yields to learn a near‐optimal control gain iteratively by input/output data. The convergence of the proposed algorithm is theoretically ensured and the trade‐off between the optimality and the sampling period is rigorously studied as well. Finally, the authors show the performance of the proposed algorithm under bounded model uncertainties. [ABSTRACT FROM AUTHOR]