A multivariable generalized predictive decoupling control algorithm with constraints is proposed for a series multivariable system composed of complex industrial plants. The tracking error caused by the coupling of the system can be reduced by changing the weight of the error, so that the coupling degree of the system can be reduced. The constraint conditions of the system variables are considered, and the control variables are obtained by quadratic programming. The simulation results of the proposed method and the conventional multivariable constrained generalized predictive control algorithm on a series plant show the superiority of the algorithm.
This paper proposes an improved wireless sensor network node location algorithm to locate terminal nodes of ZigBee wireless network in soybean farmland. The Gaussian data screening model was used to modify the measured distance of the received signal intensity, and the strategy of introducing variogram was adopted on the basis of standard particle swarm optimization. The advantage of each variogram was applied to the population in the process of algorithm search, so that the particles could jump out of the local optimal, ensure the global search traversal ability, and obtain the precise location of sensor nodes. The comparison between the standard particle swarm positioning algorithm without correction distance and the improved positioning algorithm in this paper shows that the improved positioning method has higher accuracy.
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