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Almost all dynamical systems experience inherent uncertainties such as environmental disturbance and sensor noise. This paper describes a new robust stochastic control methodology, which is capable of controlling the statistical nature of state variables of a nonlinear system to designed (attainable) statistical properties. First, an asymptotically stable and robust output tracking controller is designed...
In this paper, we consider non-linear transceiver designs for multiuser multi-input multi-output (MIMO) downlink in the presence of imperfections in the channel state information at the transmitter (CSIT). The base station (BS) is equipped with multiple transmit antennas and each user terminal is equipped with multiple receive antennas. The BS employs Tomlinson-Harashima preceding (THP) for inter-user...
This paper presents a novel algorithm of the robust iterative learning control for linear systems subject to time-invariant parametric uncertainties. The design problem is formulated as a min-max problem with a quadratic performance criterion. Then, we derive an upper-bound of the worst-case performance. Applying Lagrange duality to the minimization problem leads to a dual problem which can be reformulated...
We study a class of uncertain linear estimation problems in which the data are affected by random uncertainty. In this setting, we consider two estimation criteria, one based on minimization of the expected l1 or l2 norm residual and one based on minimization of the level within which the l1 or l2 norm residual is guaranteed to lie with an a-priori fixed probability (residual at risk). The random...
This paper demonstrates a methodology for the optimal use of phase shifting transformers (PSTs) for the minimisation of the risk of congestion on power system lines. The methodology proposes as a first step the use of Monte-Carlo simulation for the assessment of the power flow distributions in the lines. It is shown that by using PSTs these distributions are shifted by specific factors that can be...
The problems of interest are where the objective function must be evaluated using sampling techniques. The sampled function values are used to build an approximation to the response surface over the product space of uncertainties and design variables. This approximation is successively used in the integration over the space of uncertainties and in the optimization (minimization) over the space of...
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