This paper is focused on Ai Social Enterprise Opportunities in Romanian Ecosystem, on exploring the emerging grounds of Digital Innovation Management. Clarity is needed in order to pursue goal-oriented initiatives, ou...
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Convolutional Sparse Representation (CSR) approximates images with the convolutional sum of dictionary filters and corresponding sparse coefficients. To improve classification accuracy of Convolutional Neural networks...
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Cyber-physical power system (CPPS), with its bi-directional power and information flows, is considered as the next generation of widely distributed and automated electrical power network. However, CPPS is vulnerable t...
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This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime. Conventional risk-aware control typically assumes that all specifications are pr...
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This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime. Conventional risk-aware control typically assumes that all specifications are predefined and remain unchanged during runtime. In this paper, we propose a novel, provably correct model predictive control scheme for linear systems with additive unbounded stochastic disturbances that dynamically evaluates the feasibility of runtime signal temporal logic specifications and automatically reschedules the control inputs accordingly. The control method guarantees the probabilistic satisfaction of newly accepted specifications without sacrificing the satisfaction of the previously accepted ones. The proposed control method is validated by a robotic motion planning case study.
An accurate classification method is highly required in the development of a fault detection system. Various deep-learning techniques have recently been used for fault classification. However, optimally training deepe...
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The performance and sustain ability of modern organizations heavily rely on knowledge. In order to be able to manage it, organizations use dedicated tools - knowledge management systems, which enhance knowledge-relate...
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ERTMS and ALSN signalling systems are two of the major signalling systems that are under operation in the world. With the introduction of technical specifications for interoperability, ERTMS promises increased passeng...
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This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies as...
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This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies assuming that the precise model of the leader is globally or distributively accessible to all or some of the followers, the leader's precise dynamical model is entirely inaccessible to all the followers in this paper. A data-based learning algorithm is first proposed to reconstruct the leader's unknown system matrix online. A distributed predictor subject to communication delays is further devised to estimate the leader's state, where interaction delays are allowed to be nonidentical. Then, a learning-based local controller, together with a discounted performance function, is projected to reach the optimal output synchronization. Bellman equations and game algebraic Riccati equations are constructed to learn the optimal solution by developing a model-based reinforcement learning(RL) algorithm online without solving regulator equations, which is followed by a model-free off-policy RL algorithm to relax the requirement of all agents' dynamics faced by the model-based RL algorithm. The optimal tracking control of HMASs subject to unknown leader dynamics and communication delays is shown to be solvable under the proposed RL algorithms. Finally, the effectiveness of theoretical analysis is verified by numerical simulations.
Recently, legged robots have attracted considerable attention as highly mobile rovers for planetary exploration. However, the surfaces of celestial bodies such as Mars and the Moon are primarily loose, causing slippag...
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Distributed matrix-scaled consensus is a kind of generalized cooperative control problem and has broad applications in the field of social network and *** paper addresses the robust distributed matrix-scaled consensus...
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Distributed matrix-scaled consensus is a kind of generalized cooperative control problem and has broad applications in the field of social network and *** paper addresses the robust distributed matrix-scaled consensus of perturbed multi-agent systems suffering from unknown *** discontinuous protocols are first proposed to drive agents to achieve cluster consensus and suppress the effect of *** protocols with time-varying gains obeying differential equations are also designed,which are completely distributed and rely on no global *** the boundary layer technique,smooth protocols are proposed to avoid the unexpected chattering effect due to discontinuous *** a cost,under the designed smooth protocols,the defined matrix-scaled consensus error tends to a residual set rather than zero,in which the residual bound is arbitrary small by choosing proper ***,distributed dynamic event-based matrix-scalar consensus controllers are also proposed to avoid continuous *** examples are provided to further verify the designed algorithms.
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