On the basis of the classical computed torque control method, a new composite nonlinear feedback design method for robot manipulators with uncertainty is presented. The resulting controller consists of the composite nonlinear feedback control and robust control. The core is to use the robust control for online approximation of the system’s uncertainty as a compensation term for the composite nonlinear feedback controller. The design method of the new controller is given, and the convergence of the closed-loop system is proved. The simulation results show that the proposed scheme can make the uncertain robot system have strong robustness and anti-interference ability.
This article focuses on realizing the chaos control of a permanent magnet synchronous motor by combining a pseudo-linear inverse system of the permanent magnet synchronous motor and synthetical sliding mode control. First, the permanent magnet synchronous motor dimensionless nonlinear mathematical model is established, and its chaos is analyzed by the Lyapunov exponent method. The permanent magnet synchronous motor parameter range when chaos appears is obtained. Then, the inverse system decoupling method is used to analyze the reversibility of the permanent magnet synchronous motor system, and the permanent magnet synchronous motor inverse system is obtained, which is compounded with the original system into a pseudo-linear inverse system that consists of two independent subsystems, including a first-order d-axis current system and a second-order rotational speed system, to decouple the permanent magnet synchronous motor system. Third, the first-order d-axis subsystem is controlled by sliding mode control with a hyperbolic tangent function as the switching function, and the second-order speed subsystem is controlled by super-twisting sliding mode control with a hyperbolic tangent function as the switching function, which is called the synthetical sliding mode control. The permanent magnet synchronous motor pseudo-linear inverse system is controlled by using the synthetical sliding mode to realize the chaos control of the permanent magnet synchronous motor. Finally, three kinds of permanent magnet synchronous motor chaos control systems are established in MATLAB/Simulink software, and the experimental tests are implemented. The results show that the proposed permanent magnet synchronous motor chaos control system has good performance, which can effectively eliminate chattering in sliding mode control and control chaos in the permanent magnet synchronous motor system.
An adaptive optics (AO) system with Stochastic Parallel Gradient Descent (SPGD) algorithm and a 61-element deformable mirror is simulated to restore the image of a turbulence-degraded extended object. SPGD is used to search the optimum voltages for the actuators of the deformable mirror. We try to find a convenient image performance metric, which is needed by SPGD, merely from a gray level distorted image and without any additional optics elements. Simulation results show the gray level variance function acts more promising than other metrics, such as metrics based on the gray level gradient of each pixel. The restoration capability of the AO system is investigated with different images and different turbulence strength wave-front aberrations using SPGD with the above resultant image quality criterion. Numerical simulation results verify the performance metric is effective and the AO system can restore those images degraded by different turbulence strengths successfully.
Recognition of obstacle type based on visual sensors is important for navigation by unmanned surface vehicles (USV), including path planning, obstacle avoidance, and reactive control. Conventional detection techniques may fail to distinguish obstacles that are similar in visual appearance in a cluttered environment. This work proposes a novel obstacle type recognition approach that combines a dilated operator with the deep-level features map of ResNet50 for autonomous navigation. First, visual images are collected and annotated from various different scenarios for USV test navigation. Second, the deep learning model, based on a dilated convolutional neural network, is set and trained. Dilated convolution allows the whole network to learn deep features with increased receptive field and further improves the performance of obstacle type recognition. Third, a series of evaluation parameters are utilised to evaluate the obtained model, such as the mean average precision (mAP), missing rate and detection speed. Finally, some experiments are designed to verify the accuracy of the proposed approach using visual images in a cluttered environment. Experimental results demonstrate that the dilated convolutional neural network obtains better recognition performance than the other methods, with an mAP of 88%.
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