This study investigates the fault-tolerant control problem for affine nonlinear systems with time-varying actuator gain and bias faults. In order to handle the actuator faults and guarantee the approximate optimal per...
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This study investigates the fault-tolerant control problem for affine nonlinear systems with time-varying actuator gain and bias faults. In order to handle the actuator faults and guarantee the approximate optimal performance of the nominal non-linear dynamics, the approximate dynamic programming method is used to design a sliding mode fault-tolerant control policy. First, the actuator faults are estimated using a disturbance observer and a novel adaptive scheme. Based on the fault estimations, an integral sliding function is constructed and the reachability condition is derived. Then, an actor-critic algorithm with new weight tuning laws is given to learn the bounded nearly optimal control policy for the nominal dynamics. The convergence of the neural network weights is presented based on a Lyapunov analysis method. Finally, the simulation results are given to verify the efficacy of the developed method.
This study proposes an approximatedynamicprogramming (ADP) method for a stochastic home energy management system (HEMS) that aims to minimise the electricity cost and discomfort of a household under uncertainties. T...
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This study proposes an approximatedynamicprogramming (ADP) method for a stochastic home energy management system (HEMS) that aims to minimise the electricity cost and discomfort of a household under uncertainties. The study focuses on a HEMS that optimally schedules heating, ventilation, and air conditioning, water heater, and electric vehicle, while accounting for uncertainties in outside temperature, hot water usage, and non-controllable net load. The authors approach the ADP-based HEMS via an effective combination of Sobol sampling backward induction and a K-D tree nearest neighbour techniques for the value function approximation. A subset of possible states is sampled and used to create an approximation of the value of being in aggregated states. They compare the ADP approach with other prevailing HEMS methods, including dynamicprogramming (DP) and mixed-integer linear programming (MILP), in a model predictive control framework. Simulation results show that the proposed ADP approach can yield near-optimal appliance schedules under uncertainties when finely discretised. Merits and drawbacks of the proposed ADP method in comparison with DP and MILP are also revealed.
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