Since the existing single-layer networked control systems have some inherent limitations and cannot effectively handle the problems associated with unreliable networks, a novel two-layer networked learning control sys...Since the existing single-layer networked control systems have some inherent limitations and cannot effectively handle the problems associated with unreliable networks, a novel two-layer networked learning control system (NLCS) is proposed in this paper. Its lower layer has a number of local controllers that are operated independently, and its upper layer has a learning agent that communicates with the independent local controllers in the lower layer. To implement such a system, a packet-discard strategy is firstly developed to deal with network-induced delay and data packet loss. A cubic spline interpolator is then employed to compensate the lost data. Finally, the output of the learning agent based on a novel radial basis function neural network (RBFNN) is used to update the parameters of fuzzy controllers. A nonlinear heating, ventilation and air-conditioning (HVAC) system is used to demonstrate the feasibility and effectiveness of the proposed system.展开更多
基金Supported by National Natural Science Foundation of China(60774059)Project of Science &Technology Commission of Shanghaiunicipality(061107031,06DZ22011,06ZR14131)+1 种基金the Sunlight Plan Following Project of Shanghai Municipal Education Commission and Shanghai Edu-ational Development Foundation(06GG10)Shanghai Leading Academic Disciplines(T0103)
文摘Since the existing single-layer networked control systems have some inherent limitations and cannot effectively handle the problems associated with unreliable networks, a novel two-layer networked learning control system (NLCS) is proposed in this paper. Its lower layer has a number of local controllers that are operated independently, and its upper layer has a learning agent that communicates with the independent local controllers in the lower layer. To implement such a system, a packet-discard strategy is firstly developed to deal with network-induced delay and data packet loss. A cubic spline interpolator is then employed to compensate the lost data. Finally, the output of the learning agent based on a novel radial basis function neural network (RBFNN) is used to update the parameters of fuzzy controllers. A nonlinear heating, ventilation and air-conditioning (HVAC) system is used to demonstrate the feasibility and effectiveness of the proposed system.
