Abstract
In order to cut down influence on the uncertainty disturbances of a linear motion single axis robot machine, such as the external load force, the cogging force, the column friction force, the Stribeck force, and the parameters variations, the micrometer backstepping control system, using an amended recurrent Gottlieb polynomials neural network and altered ant colony optimization (AACO) with the compensated controller, is put forward for a linear motion single axis robot machine drive system mounted on the linear-optical ruler with 1 um resolution. To achieve high-precision control performance, an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is proposed to estimate the lumped uncertainty. Besides this, a novel error-estimated law of the compensated controller is also proposed to compensate for the estimated error between the lumped uncertainty and the amended recurrent Gottlieb polynomials neural network with the adaptive law. Meanwhile, the AACO is used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. The main contributions of this paper are: (1) The digital signal processor (DSP)-based current-regulation pulse width modulation (PWM) control scheme being successfully applied to control the linear motion single axis robot machine drive system; (2) the micrometer backstepping control system using an amended recurrent Gottlieb polynomials neural network with the compensated controller being successfully derived according to the Lyapunov function to diminish the lumped uncertainty effect; (3) achieving high-precision control performance, where an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is successfully applied to estimate the lumped uncertainty; (4) a novel error-estimated law of the compensated controller being successfully used to compensate for the estimated error; and (5) the AACO being successfully used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. Finally, the effectiveness of the proposed control scheme is also verified by the experimental results.
Highlights
A linear motion single axis robot machine that can achieve rapid rates of acceleration by use of electromagnetic force has few features which are of merit [1,2,3], such as being simple fabric, having no adverse reaction, little friction, elated velocity, elated pushed force, and elated precision in a long-distance location and so on
The motivation of the proposed micrometer backstepping control system, by means of the amended recurrent Gottlieb polynomials neural network and altered ant colony optimization (AACO) with the compensated controller for a linear motion single axis robot machine mounted with a linear optical-ruler sensor with 1 um precision and three Hall sensors, provides an estimated method and error compensation mechanism which can be used to enhance the robustness of the system under parameter variations and external force disturbances to raise the control precision
This paper presents the micrometer backstepping control system using an amended recurrent Gottlieb polynomials neural network and AACO with the compensated controller, which has an error estimated law with an adaptive law, to control the linear motion single axis robot machine drive system so as to enhance the robustness of the system under the parameter variations and the external load force disturbances
Summary
A linear motion single axis robot machine that can achieve rapid rates of acceleration by use of electromagnetic force has few features which are of merit [1,2,3], such as being simple fabric, having no adverse reaction, little friction, elated velocity, elated pushed force, and elated precision in a long-distance location and so on. The adaptive backstepping controllers, combined with some neural networks [13,14,15] are generally applied to control the nonlinear systems so as to estimate some uncertainties and enhance system robustness These methods are only limited to the bounded region, and have never showed any compensated mechanics or technology. The motivation of the proposed micrometer backstepping control system, by means of the amended recurrent Gottlieb polynomials neural network and AACO with the compensated controller for a linear motion single axis robot machine mounted with a linear optical-ruler sensor with 1 um precision and three Hall sensors, provides an estimated method and error compensation mechanism which can be used to enhance the robustness of the system under parameter variations and external force disturbances to raise the control precision
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