In this paper, an improved adaptive unscented particle filter (IAUPF) algorithm is proposed to address the shortcomings of the unscented particle filter (UPF) algorithm on the state estimation of distribution network, such as vulnerability to process noise and low quality of the importance density function, in order to obtain a more accurate state estimation result and reduce the effect of unknown system noise in the dynamic state estimation. The IAUPF can estimate the mean and variance of the system noise and so increase the filtering accuracy of the system with unknown noise by employing a novel statistical estimator for the noise parameter and modifying the scale correction factor in real-time. The simulation results on the IEEE 33-node system show that, as opposed to the conventional UPF algorithm, the proposed IAUPF can address the issue of decreasing estimation accuracy due to unknown system noise in the filtering process and ensure high precision of state estimation when the system experiences abrupt changes.
This paper is concerned with the design of fractional order PI controller for permanent magnet synchronous motor based on the intelligent optimization algorithm. The implementation of fractional order differential operator and particle swarm optimization are briefly introduced firstly, and then the transfer function of permanent magnet synchronous motor is presented in the paper. Furthermore, the parameter tuning of fractional order PI controller based on improved particle swarm optimization is proposed. Finally, the simulation results show that the performance of fractional PI controller is superior to the integer order controllers.
This paper is concerned with the path planning of the coal mine robot. A new workspace model is presented to describe the complex coal mine environment. Thus, the cost of a path is composed of not only the distance of the path but also some hybrid costs that can be linked to the criteria of path optimization. To overcome the drawbacks of conventional ant colony optimization (ACO) algorithm, an improved ACO algorithm is developed to tackle the issues of path planning of coal mine robot based on the new workspace model. Some simulation experiments are carried out on the path planning of coal mine robot, and the validity and superiority of the new approach can be confirmed by the simulation results.
Stochastic configuration network (SCN) is a powerful prediction model whose performance is significantly influenced by the configuration of the network parameters. To improve the prediction accuracy of the network, a cooperative stochastic configuration network (CSCN) based on a novel differential evolutionary sparrow search algorithm (DESSA), termed as DESSA-CSCN, is proposed. In the CSCN, the number of hidden layer nodes is adaptively adjusted according to the number of iterations, and the parameters of hidden nodes are cooperatively optimized by using a population-based metaheuristic algorithm. A sparse matrix is introduced to mitigate parameter overfitting caused by the increased number of hidden layer nodes. During parameter optimization, the fitness function is constructed by using the supervision mechanism of the SCN, and the DESSA is utilized as the metaheuristic algorithm to update the weights and biases. In order to verify the effectiveness of the DESSA-CSCN, several simulation experiments have been conducted. The performance of the DESSA is evaluated by the CEC2017 test suit, and the simulation results show that the DESSA exhibits better convergence accuracy and can jump out of local optima more effectively than other algorithms. The performance of the DESSA-CSCN is evaluated by 4 datasets from KEEL, and the simulation results indicate that the DESSA-CSCN achieves better prediction accuracy and faster prediction speed than other models.
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