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Creators/Authors contains: "Yang, Yongliang"

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  1. ; ; ; (, IEEE Transactions on Cybernetics)
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  2. ; ; ; ; ; (, IEEE Transactions on Cybernetics)
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  3. ; ; ; ; (, IEEE Transactions on Neural Networks and Learning Systems)
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  4. ; ; ; ; (, IEEE Transactions on Systems, Man, and Cybernetics: Systems)
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  5. ; ; ; ; (, IEEE Transactions on Cybernetics)
    This article develops a novel distributed intermittent control framework with the ultimate goal of reducing the communication burden in containment control of multiagent systems communicating via a directed graph. Agents are assumed to be under disturbance and communicate on a directed graph. Both static and dynamic intermittent protocols are proposed. Intermittent H∞ containment control design is considered to attenuate the effect of the disturbance and the game algebraic Riccati equation (GARE) is employed to design the coupling and feedback gains for both static and dynamic intermittent feedback. A novel scheme is then used to unify continuous, static, and dynamic intermittent containment protocols. Finally, simulation results verify the efficacy of the proposed approach. 
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  6. ; ; ; ; ; (, 2019 IEEE 58th Conference on Decision and Control (CDC))
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  7. ; ; ; ; ; (, Proc. 2019 American Control Conference (ACC))
    This paper considers the control problem with constraints on full-state and control input simultaneously. First, a novel barrier function based system transformation approach is developed to guarantee the full-state constraints. To deal with the input saturation, the hyperbolic-type penalty function is imposed on the control input. The actor-critic based reinforcement learning technique is combined with the barrier transformation to learn the optimal control policy that considers both the full-state constraints and input saturations. To illustrate the efficacy, a numeric simulation is implemented in the end. 
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  8. ; ; ; ; ; (, International Joint Conference on Neural Networks (IJCNN))
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  9. ; ; ; ; ; (, IEEE Transactions on Neural Networks and Learning Systems)
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  10. ; ; ; ; (, 2018 Annual American Control Conference (ACC))
    This paper presents a model-free distributed event-triggered containment control scheme for linear multiagent systems. The proposed event-triggered scheme guarantees asymptotic stability of the equilibrium point of the containment error as well the avoidance of the Zeno behavior. To relax the requirement of complete knowledge of the dynamics, we combine an off-policy reinforcement learning algorithm in an actor critic structure with the event-trigger control mechanism to obtain the feedback gain of the distributed containment control protocol. A simulation experiment is conducted to verify the effectiveness of the approach. 
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