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This article investigates the cooperative tracking control of multiple homogeneous uncertain nonholonomic wheeled mobile robots with state constraints. Transforming each mobile robot system into a chained form, a cooperative learning control scheme based on the adaptive neural network is proposed. Firstly, a virtual control law is designed for the kinematic model of the constrained chain system combined with the barrier Lyapunov function (BLF). Then, radial basis function neural networks (RBF NNs) are exploited to deal with the unknown nonlinear dynamics in the mobile robot system, a robust term is introduced to compensate for the NN approximation errors and the external disturbance, and the Moore–Penrose inverse is adopted to avoid the violation of state constraints. Communication network is used to realize the online sharing of NN weights of each mobile robot individuals, such that locally accurate identification of the unknown nonlinear dynamics with common optimal weights can be obtained. As a result, the learned knowledge can be reused in the cooperative learning control tasks and the trained network model has better generalization capabilities than the normal decentralized learning control. Finally, numerical simulation verifies the effectiveness of the control scheme.

In this article, the concept of context-aware is integrated into the design of decision agents. This makes intelligent printing system can intelligently and quickly configure all kinds of manufacturing resources based on different printed product features. Firstly, this paper introduces the current situation of the application of context-aware technology, and the corresponding context information is classified in the printing process. Then, the general framework of decision agent in intelligent situation printing system is given. The function of each component is introduced in detail. Finally, a particular step of that decision agent in preprocess context information is described. The system can meet the needs of scenario printing.

As the power source of electric vehicles, lithium-ion batteries directly affect the performance and life of electric vehicles. The charging strategy of lithium-ion battery directly affects the charging speed, energy consumption and life of the battery. In view of above problems, the multi-segment constant current charging strategy of lithium-ion battery is designed and the particle swarm optimization (PSO) algorithm is used to optimize it. Based on the PSO, the multi-segment constant current charging strategy can adjust the charging current according to the change of the internal resistance of the battery, so as to achieve the charging efficiency improvement. Multiple groups of comparative experiments of ternary lithium-ion batteries are designed to verify the optimization effect of proposed multi-segment constant current charging strategy.

We study the downlink beamformer design problem considering different real-time requirements of users. The problem is formulated mathematically as a non-convex optimization problem. The objective is to maximize the minimum signal to interference plus noise ratio (SINR) of the non-real-time service users, considering the SINR constraints of the real-time users and the total transmission power constraint. We carefully study the feasibility conditions for the original non-convex optimization problem. The results provide both theoretical and physical insights into the beamformer design problem. After a series of equivalence and relaxation of the original problem, the solution can be found via bisection feasibility checking and solving a set of geometric programming (GP) problems obtained from randomization. Combining the feasibility checking algorithm and the randomization procedure, we have our algorithm for computing the beamforming vector. Numerical results are provided for illustration.