In this article, a novel reinforcement learning (RL) method is developed to solve the optimal tracking control problem of unknown nonlinear multiagent systems (MASs). Different from representative RL-based algorithms, an internal reinforce Q-learning (IrQ-L) proposed, in which reward (IRR) function introduced for each agent improve its capability receiving more long-term information local environment. IrQL designs, Q-function defined on basis IRR and iterative algorithm learn optimally...

In this paper, an event-triggered optimal tracking control of discrete-time multi-agent systems is addressed by using reinforcement learning. contrast to traditional learning-based methods for coordination and with a time-triggered mechanism, mechanism proposed update the controller only when designed events are triggered, which reduces computational burden transmission load. The stability analysis closed-loop described. Further, implement scheme, actor-critic neural network learning...

This paper reviews recent progress in model identification-based learning and optimal control its applications to multi-agent systems (MASs). First, a class of learning-based method, namely adaptive dynamic programming (ADP), is introduced, the existing results using ADP methods solve problems are reviewed. Then, this investigates various kinds identification analyzes feasibility combining method with unknown systems. In addition, expounds current fields single-agent (SASs) MASs. Finally,...

This article investigates an output antisynchronization problem of multiagent systems by using input-output data-based reinforcement learning approach. Till now, most the existing results on problems required full-state information and exact system dynamics in controller design, which is always invalid practical scenarios. To address this issue, a new representation constructed just available input/output data from system. Then, novel value iteration algorithm proposed to compute optimal...

Lower limb exoskeleton (LLE) has received considerable interests in strength augmentation, rehabilitation and walking assistance scenarios. For assistance, the LLE is expected to have capability of controlling affected leg track unaffected leg's motion naturally. An important issue this scenario that system needs deal with unpredictable disturbance from patient, which requires controller ability adapt different wearers. This paper proposes a novel Data-Driven Reinforcement Learning (DDRL)...

More recently, lower limb exoskeletons (LLE) have gained considerable interests in strength augmentation, rehabilitation, and walking assistance scenarios. For assistance, the LLE is expected to control affected leg track unaffected leg's motion naturally. A critical issue this scenario that exoskeleton system needs deal with unpredictable disturbance from patient, controller has ability adapt different wearers. To end, a novel data-driven optimal (DDOC) strategy proposed hemiplegic patients...

This brief addresses the optimal consensus control problem of a class nonlinear leader-follower multi-agent systems (MASs), where dynamics and states leader are unknown. A distributed Kreisselmeiers Regressor Extension Mixing (KREM)-based scheme is developed. Specifically, parameter estimation-based observer first proposed, which can estimate not only dynamic parameters but also for each agent. allows to transform into an tracking leader's state. To solve optimization problem, only-critic...

Given the prevalence of frequent highway accidents, investigating potential connected automated vehicles (CAVs) to enhance both traffic efficiency and safety emerges as a pivotal concern. This study addresses how CAVs can harness information for informed decision-making, proposing novel natural lane-changing model rooted in extreme value theory accommodate mixed flow. Initially, this research develops acceleration, deceleration, randomization rules cellular automata two-lane flow, grounded...

Firstly, this paper analyzes the technology system and characteristics of Internet things. Secondly, mainly studies intelligent algorithm data transmission in Agricultural According to different energy efficiency limited battery capacity rechargeable sensor nodes, a new clustering routing protocol is proposed. Meanwhile, order solve problem fault-tolerant repair maintain quality routing, proposes strategy (CR-WSN). Compared with CRAS algorithm, they have same point that node more remaining...

This paper proposes a novel identifier-critic (IC) learning control strategy for completely unknown nonlinear system. Different from the existing results, proposed IC is capable of obtaining optimal under relaxed persistence excitation (PE). A neural network (NN) based identifier established to approximate system dynamics. After that, an only-critic NN framework solve Hamiltonian-Jacobi-Bellman (HJB) equation such that policy obtained. To estimate weights both and critic simultaneously...

Rescue route planning for emergency vehicles (EVs) is one of the most important tasks response. However, traditional methods face challenges in addressing complex information modern traffic environments. To ensure prompt arrival EVs, this paper proposes a deep reinforcement learning-based method to optimize rescue routes EVs on highway. Specifically, align with transportation business, an map based road network node graph first constructed. improve convergence speed algorithm, action masking...

In recent years, lower limb exoskeleton has attracted extensive attention in academic and engineering research. the rehabilitation motion training scenario, it is of concern that should have ability to control its own leg movements order assist a patient with natural anthropomorphic gaits. A paralysis leg, incapable controlling coordinating well enough produce desirable gait. Thus, critical problem this scenario needs track desired gait for adopt different walking patterns. To end, paper...

This paper presents an adaptive learning structure based on neural networks (NNs) to solve the optimal robust control problem for nonlinear continuous-time systems with unknown dynamics and disturbances. First, a system identifier is introduced approximate matrices disturbances help of NNs parameter estimation techniques. To obtain solution problem, critic proposed compute controller. Unlike existing identifier-critic methods, novel tuning laws Kreisselmeier’s regressor extension mixing...

Go is a very interest game with complex rules. Since computer came up, there were no efficient arithmetic yet, because it difficult for traditional way to solve problems like this. But things changed since BP-Neural Network into the world. Neural proper problem. This paper suggests solution in AI program based on BP-neural network, and discussed details.

In this paper, an adaptive reinforcement learning (RL) based controller is developed to solve assistance control problem for Lower Limb Exoskeleton (LLE) aid hemiplegic individuals in walking. The communication interaction relation between both two lower-limbs and patient's unaffected leg modelled the context of leader-follower (LF) framework. walking LLE with patients converted optimal problem. To handle problem, a discounted cost function designed terms local tracking error, then policy...