This paper investigates the step tracking control problem for discrete-time nonlinear systems in a networked environment with a limited capacity. The nonlinear system is represented by a Takagi-Sugeno (T-S) fuzzy system, and a network-induced delay is incorporated in the modeling of the connection link. In order to compensate for the network link effects and eliminate the tracking error, we employ some techniques mainly used in the predictive control and the integral control. Moreover, a quadratic cost function which includes terms related to the performance of the system and the actuating capacity is used. We assume that the lumped network-induced delay lies within a known set, and that the occurrence probability for each element in the set is known a priori. Then, the delay information will be incorporated into the delay-dependent tracking controllers. The parameters for the tracking controller are derived by solving an optimization problem. A networked inverted pendulum is used to illustrate the efficacy of the proposed design method.

Electric vehicle (EV) charging can negatively impact electric distribution networks by exceeding equipment thermal ratings and causing voltages to drop below standard ranges. In this paper, we develop a decentralized EV charging control scheme to achieve ''valley-filling'' (i.e., flattening demand profile during overnight charging), meanwhile meeting heterogeneous individual charging requirements and satisfying distribution network constraints. The formulated problem is an optimization problem with a nonseparable objective function and strongly coupled inequality constraints. We propose a novel shrunken-primal-dual subgradient algorithm to support the decentralized control scheme, derive conditions guaranteeing its convergence, and verify its efficacy and convergence with a representative distribution network model.

In order to provide the ancillary service for smart grid, this paper proposes a modelling and control protocol design approach for the aggregation of heterogeneous thermostatically controlled loads (TCLs). A 2-D state bin is proposed to model the second-order TCL dynamics in a population model. Detailed procedure of calculating the transition probability in the system matrix is provided. In the controller design, a model predictive control (MPC) scheme is proposed to obtain the optimal control actions along the prediction horizon. In addition, implementation of the control signal for adjusting TCLs' statuses are also investigated with practical situations considered. Simulation results reveal the feasibility and efficacy of the proposed modelling and control approach when applied on a large population of TCLs. Some factors that may affect the service performance are also discussed in this paper.

Using aggregated heterogeneous thermostatically controlled loads (TCLs) to provide ancillary services in a smart grid is a promising technique. This paper proposes an improved population model with lockout time considered to describe the aggregate dynamics. Based on that, a distributed model predictive control (DMPC) scheme is investigated to control a population of TCLs for the regulation service. Full derivations of the system model and the DMPC scheme are provided. A flexibility penalty is developed and added to the objective function to preserve the regulation flexibility. Simulation results, tested by real data, reveal the feasibility and effectiveness of the proposed method.