This paper is mainly devoted to the iterative learning control (ILC) design for a class of linear discrete time systems. By providing a 2D analysis of the learning process, the ILC design for such systems can be transformed into the problem of state feedback or output feedback control for 2-D systems described by Roesser models. Then, a Lyapunov approach can be used to obtain an ILC law that achieves asymptotical convergence of the tracking error. Sufficient stability conditions are provided in terms of linear matrix inequalities, which can determine learning gains as well. The theoretical results are also verified through simulation tests.
Based on the traditional principle of two-line voltage synthesis, a modulation strategy is proposed to improve the output performance of matrix converter by changing the switching mode. According to the switching mode of the new strategy, under the premise that the input current and output voltage are sinusoidal, the modulation time of each switch of the matrix converter in one cycle is derived from the relationship between input and output current and voltage. Under the same conditions, the proposed modulation strategy is compared with the traditional two-line voltage synthesis strategy, and the corresponding simulation data and some experimental waveforms are given. Based on the research of MATLAB software simulation, the control strategy of the dSPACE hardware real-time simulation platform is experimentally studied. The simulation and experimental results verify the correctness of the modulation strategy.
This paper first analyzes the Park Transform and Clarke Transform of 5 normal power quality disturbances and then proposes 3 characteristic indexes based on the two transforms. The indexes can be used to classify 5 single disturbances and 5 mixed disturbances exactly. The algorithm is simple and easy to be implemented by hardware.It can also be the basis of power quality index evaluation. At last, this paper simulates the disturbance classification method by Matlab, and the result indicates that the classification method proposed by this paper is accurate, effective, and has a good effect on classifying mixed disturbance.The misjudge rate is also low.
A robust iterative learning control algorithm is proposed for a class of intermittent systems with disturbances and uncertain initial conditions. Based on the contraction mapping approach, the convergence condition for the proposed algorithm is first given, and then the bounds on control input and output trajectories can be obtained. It is shown that these bounds depend on bounds on the initial condition errors and disturbances, and the bounds are zero in the absence of these disturbances. A numerical example is also given to verify the theoretical result.
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