The quality of machining is dependent on the machine’s dynamic behavior throughout the operating process. Because of the loads or vibration during operation, the rigidity of the machine structure can be reduced. Therefore, the study of advances in the dynamic characteristics has great significance for the development of machine tools, especially for high-speed machines. This paper presents the design and analysis of a rigid gantry structure with a spindle speed in the range of (6.000 ÷ 24.000)rpm, corresponding to the natural frequency of the machine structure more than (100 ÷ 400)Hz. Use CAE (computer-aided engineering) analysis software to analyze the natural frequency of machine structure. The research results show that the machine structure will have good stiffness, high vibration resistance and avoid resonance to achieve the best machining surface. In addition, it is the basis for selection of cutting mode suitable for the machining process in order to improve the reliability and efficiency of work of the machine structure and the accuracy of the processed products.
This paper considers an evolutionary extreme learning machine (ELM) based on chemical reaction optimization (CRO) to overcome the drawbacks of ELM, such as the unavoidable existence of a set of unnecessary or non-optimal hidden biases and input weights. By using CRO algorithm to determine the hidden biases and input weights according to both the norm of output weights and the root mean squared error, the classification performance of optimized ELM can be improved. The experimental results on some real benchmark problems show that the proposed method can achieve higher classification accuracy than both other compared evolutionary ELMs and original ELM.
In this paper, an artificial chemical reaction optimization algorithm (ACROA) and neural network based adaptive control scheme for robot manipulator is proposed to obtain the expected trajectory tracking. A radial basis function neural network (RBFNN) is applied to approximate the uncertainties. The network parameters in initial stage are optimized by utilizing ACROA. The RBFLN weights are achieved based on adaptive tuning law in Lyapunov stability theory. Thus, the system is convergent and stable, and the control performances of the system are improved. The simulation results of two-link robot manipulator are represented to validate the efficiency of the proposed control method.
In this paper, a sliding mode control (SMC) system based on combining chemical reaction optimization (CRO) algorithm with radial basis functional link net (RBFLN) for an n-link robot manipulator is proposed to achieve the high-precision position tracking. In the proposed scheme, a three-layer RBFLN with powerful approximation ability is employed to approximate the uncertainties, such as parameter variations, friction forces, and external disturbances, and to eliminate chattering phenomenon of the SMC. In order to achieve the expected performance in the initial phase as well as the improved convergence rate, the RBFLN parameters need to be optimized in advance. Therefore, the initial parameters of the RBFLN are optimized offline by CRO algorithm instead of random selection. Furthermore, the RBFLN weights are determined online according to adaptive tuning laws in the sense of a projection algorithm and the Lyapunov stability theorem to guarantee the stability and convergence of the system. The simulation results of three-link de-icing robot manipulator (DIRM) are provided to verify the robustness and effectiveness of the proposed methodology.
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