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A novel data driven predictive control method for networked control systems (NCSs) is presented, where the network links from sensors to controllers and from controllers to actuators are subject to random packet dropouts. In order to make full use of the I/O data of the controlled system to improve system performance, an improved model-free adaptive predictive control (iMFAPC) method is proposed by...
This paper proposes a novel model free adaptive predictive control strategy for phase splits of urban traffic network. The accurate model of the traffic network is difficult to establish. By using dynamic linearization approach, the nonlinear time-varying traffic network is described as an equivalent simplified data model. Then, model free adaptive predictive control (MFAPC) scheme is developed by...
Terminal iterative learning control (TILC) has been developed to track a single desired point at the terminal end of operation interval over iterations. In this paper, the feedback control knowledge of previous time instants is utilized via an equivalent dynamical predictive model to update the input signals for the TILC problem. The proposed scheme consists of a control input updating law with feedback...
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