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In this work, iterative learning model predictive control approaches are presented to resolve the control problem of trajectory based aircraft operation with time-of-arrival constraint. Firstly, we formulated this problem being an output tracking issue with along-track wind disturbance. Next, P-type point-to-point iterative learning control algorithms are designed to overcome the influence of repetitive along-track wind. In addition, a point-topoint iterative learning model predictive control algorithm with variable prediction step is also designed to eliminate the interference of non-repetitive gusts on the air route. Finally, numerical simulations are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Problems of constructing high-speed control systems of electric drives with switch-mode power converters are discussed in the present paper. Historical aspects of the development of these theoretical issues at the Department of Automated Electric Drives of the Moscow Power Engineering Institute and their importance are discussed. The described converters are control objects of a discrete type that should be considered in the creation of control systems. It is shown that dead-beat control in such systems in the unconstrained case makes a finite number of discreteness intervals equal to the system order. Two currently important principles in construction of a dead-beat control system-namely, ones using predictive models in a real and accelerated time scale-are considered. Since the operation quality of such systems is determined by the adequacy of the predictive model of the control object, the approaches to creating the self-adjustment algorithms allowing one to correct the model in operation are considered. [ABSTRACT FROM AUTHOR]

Power controlling on reliability-aware GPU clusters with dynamically variable voltage and speed is investigated as combinatorial optimization problem, namely the problem of minimizing task execution time with energy consumption constraint and the problem of minimizing energy consumption with system reliability constraint. The two problems have applied in general multiprocessor computing and real-time multiprocessing systems where energy consumption and system reliability both are important. These problems which emphasize the trade-off among performance, power and reliability have not been well studied before. In this research, a novel power control model is built based on Model Prediction Control theory. Maximum Entropy Method is used to determine partial ordering relation of control variable and to identify the quality of solutions. Our controller can cap the redundant energy consumption by dynamically transforming energy states of the nodes in GPU cluster. We compare our controller with the control scheme, which does not consider the system reliability. The experimental results demonstrate that the proposed controller is more reliable and valuable. [ABSTRACT FROM AUTHOR]