Adaptive Robust Servo Control for Vertical Electric Stabilization System of Tank and Experimental Validation

A tracking stability control problem for the vertical electric stabilization system of moving tank based on adaptive robust servo control is addressed.This paper mainly focuses on two types of possibly fast timevarying but bounded uncertainty within the vertical electric stabilization system:model parameter uncertainty and uncertain nonlinearity.First,the vertical electric stabilization system is constructed as an uncertain nonlinear dynamic system that can reflect the practical mechanics transfer process of the system.Second,the dynamical equation in the form of state space is established by designing the angular tracking error.Third,the comprehensive parameter of system uncertainty is designed to estimate the most conservative effects of uncertainty.Finally,an adaptive robust servo control which can effectively handle the combined effects of complex nonlinearity and uncertainty is proposed.The feasibility of the proposed control strategy under the practical physical condition is validated through the tests on the experimental platform.This paper pioneers the introduction of the internal nonlinearity and uncertainty of the vertical electric stabilization system into the settlement of the tracking stability control problem,and validates the advanced servo control strategy through experiment for the first time.

A Model-Driven Approach to Enhance Faster-than-Nyquist Signaling over Nonlinear Channels

In order to increase the capacity of future satellite communication systems,faster-than-Nyquist(FTN)signaling is increasingly consideredI..Existing methods for compensating for the high power amplifier(HPA)nonlinearity require perfect knowledge of the HPA model.To address this issue,we analyze the FTN symbol distribution and propose a complex-valued deep neural network(CVDNN)aided compensation scheme for the HPA nonlinearity,which does not require perfect knowledge of the HPA model and can learn the HPA nonlinearity during the training process.A model-driven network for nonlinearity compensation is proposed to further enhance the performance.Furthermore,two training sets based on the FTN symbol distribution are designed for training the network.Extensive simulations show that the Gaussian distribution is a good approximation of the FTN symbol distribution.The proposed model-driven network trained by employing a Gaussian distribution to approximate an FTN signaling can achieve a performance gain of 0.5 dB compared with existing methods without HPA's parameters at the receiver.The proposed neural network is also applicable for non-linear compensation in other systems,including orthogonal frequency-division multiplexing(OFDM).

Dynamic parameter identification and nonlinear friction compensation method for safety perception of heavy explosion-proof robots

This article focuses on the problem of how to accurately calculate the joint control torques when the explosion-proof robot performs collision detection without sensors and gives a complete solution.Nonlinear joint frictions are incorporated into the dynamic model of a robotic manip-ulator to improve calculation accuracy.A genetic algorithm is used to optimise the excitation trajectories to fully stimulate the robot dynamic characteristics.Effective and applicable data filtering and smoothing methods are proposed and the Iteratively Reweighted Least-Squares method based on the error term is applied to identify the robot dynamic parameters.Compared with Ordinary Least-Squares method,the proposed algorithm improves the accuracy of joint control torques estimation.