Establishment Method of Shape Standard Curve Based on Stepwise Optimization and Its Compensation Models

There are very few researches on the shape standard curve currently,and they merely remain on the level of description of the general concept and production experiences,lacking of the in-depth theoretical analysis,and the concrete principle,method and steps for determining the shape standard curve are not put forward,therefore,they are not applicable in industrial production.This is the weakest spot in the research on the basic shape theory.In this paper,the basic shape standard curve and the transverse distribution curve of the exit thickness are attained with stepwise optimization,which is based on the theoretical calculation method of the shape standard curve of strip mills proposed by authors.By calculating the shape discrimination model and the shape forecast model separately,the simultaneous iterative calculation by the previous method is avoided,and the speed and stability of calculation are improved.The compensation models of the transverse temperature difference of the strip,the shape detection roller deflection and the shape of the strip coil are established,respectively,meantime,the basic shape standard curves are compensated,and the relatively perfect theoretical establishment method of the shape standard curve is formed.The simulation and calculation are done on a 1 220 mm five-stand cold strip tandem mill.The simulation and calculation result shows that the principle,method and steps for determining the shape standard curve are correct and feasible,and the correctness of theoretical analysis and calculation is verified.This paper proposes an idea and a method for the establishment of the shape standard curve in the rolling processes of cold strip mills,which develop the theory and model of the shape standard curve and improve the quality and efficiency of the shape control in the rolling processes of cold strip mills.

Compensation Model for Shape Measuring of Cold Strip Rolling

Some unavoidable factors in the process of cold strip shape measurement interfere with the shape meter, so the shape measuring results cannot reflect the true shape of the strip and the measuring precision is low. The influ- ences of the measuring error of the strip edges, the transverse temperature difference of the strip, the deflection of shape detection roller, and the shape of the strip coil on the shape measuring results were analyzed in detail, and the corresponding compensation models were established. The simulation calculation and analysis were carried out on a cold strip mill, and a number of disciplinarian cognitions were obtained.

Neural Network Robust Control Based on Computed Torque for Lower Limb Exoskeleton

The lower limb exoskeletons are used to assist wearers in various scenarios such as medical and industrial settings.Complex modeling errors of the exoskeleton in different application scenarios pose challenges to the robustness and stability of its control algorithm.The Radial Basis Function(RBF)neural network is used widely to compensate for modeling errors.In order to solve the problem that the current RBF neural network controllers cannot guarantee the asymptotic stability,a neural network robust control algorithm based on computed torque method is proposed in this paper,focusing on trajectory tracking.It innovatively incorporates the robust adaptive term while introducing the RBF neural network term,improving the compensation ability for modeling errors.The stability of the algorithm is proved by Lyapunov method,and the effectiveness of the robust adaptive term is verified by the simulation.Experiments wearing the exoskeleton under different walking speeds and scenarios were carried out,and the results show that the absolute value of tracking errors of the hip and knee joints of the exoskeleton are consistently less than 1.5°and 2.5°,respectively.The proposed control algorithm effectively compensates for modeling errors and exhibits high robustness.

Control algorithm for vehicle height adjustable system of hydro-pneumatic suspension based on LuGre model

In order to control the vehicle body position precisely,1/4 nonlinear mathematical model of hydro-pneumatic suspension is established,and the influence of the frictional force in a hydraulic cylinder is analyzed.The friction characteristics are described based on the LuGre model when the piston of a hydraulic actuator is operated at a low speed.Due to the fact parameters of the friction model are effected by the system condition,an adaptive friction compensation（AFC）controller is designed through the Backstepping method,and a dual-observer has been implemented to estimate the friction state.The global asymptotic convergence of a closed-loop system is proven by the Lyapunov theorem.The simulation results show that the positional accuracy of the adaptive friction compensation yiedls a significant improvement in the vehicle height adjustment as compared to the PID control,demonstrating the effectiveness of the adaptive fiction compensation method in the vehicle height adjustable system of the hydro-pneumatic suspension.

Tests and error analysis of a self-positioning shearer operating at a manless working face

Self-positioning of a shearer is the key technology for mining with a man-less working face. In an underground coal mine all radio navigation; satellite positioning or celestial navigation methods have their limitations. We analyzed an inertial navi-gation system intended to guide the movement a shearer and designed a self-positioning device for the shearer. Simulation tests were also performed on the system. We analyzed the errors observed in these tests to show that the main reason for the low preci-sion of the self-positioning system is accumulated error in the inertial sensor. A Kalman filtering algorithm used in combination with the shearer motion model effectively reduces the measurement errors of the self-positioning system by compensating for gyroscopic drift. Finally, we built an error compensation model to reduce accumulated errors using continuous correction to provide self-positioning of the shearer within a certain range of accuracy.

A method for aircraft magnetic interference compensation based on small signal model and LMS algorithm

Aeromagnetic interference could not be compensated effectively if the precision of parameters which are solved by the aircraft magnetic field model is low. In order to improve the compensation effect under this condition, a method based on small signal model and least mean square（LMS） algorithm is proposed. According to the method, the initial values of adaptive filter＇s weight vector are calculated with the solved model parameters through small signal model at first,then the small amount of direction cosine and its derivative are set as the input of the filter, and the small amount of the interference is set as the filter＇s expected vector. After that, the aircraft magnetic interference is compensated by LMS algorithm. Finally, the method is verified by simulation and experiment. The result shows that the compensation effect can be improved obviously by the LMS algorithm when original solved parameters have low precision. The method can further improve the compensation effect even if the solved parameters have high precision.

Steel Temperature Compensating Model With Multi-Factor Coupling Based on Ladle Thermal State

Combined with the parameters of the production process of a steel factory, numerical simulations for a new ladle from preheating to turnover are conducted using the finite element analysis system software （ANSYS）. The measured data proved that the simulated results are reliable. The effects of preheating time, thermal cycling times, and empty package time on steel temperature are calculated, an ideal preheating time is provided, besides, based on the analysis of a single factor and use the nonlinear analysis method, a steel temperature compensating model with di- versified coupling factors is proposed, with the largest error of the present coupling model at 1. 462 ~C, and the er- rors between actual and target steel temperature in tundish after the model is applied to practical production are basi- cally controlled within -4-6 ~C, which can meet the accuracy of the manufacturer and has a practical guiding significance for the production in steelmaking workshops.

Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm

A parameter estimation method based on an improved Whale Optimization Algorithm is proposed in this paper to identify the parameters of a static var compensator(SVC)model.First,a mathematical model of SVC is established.Then,the reverse learning strategy and Levy flight disturbance strategy are introduced to improve the whale optimization algorithm,and the improved whale optimization algorithm is applied to the parameter identification of the static var compensator model.Finally,a stepwise identification method,by analyzing the local sensitivities of parameters,is proposed which solves the problem of low accuracy caused by multi-parameter identification.This method provides a new estimation strategy for accurately identifying the parameters of the static var compensator model.Estimation results show that the parameter estimation method can be an effective tool to solve the problem of parameter identification for the SVC model.

Robust Control for Static Loading of Electro-hydraulic Load Simulator with Friction Compensation

Load simulator is a key test equipment for aircraft actuation systems in hardware-in-the-loop-simulation. Static loading is an essential function of the load simulator and widely used in the static/dynamic stiffness test of aircraft actuation systems. The tracking performance of the static loading is studied in this paper. Firstly, the nonlinear mathematical models of the hydraulic load simulator are derived, and the feedback linearization method is employed to construct a feed-forward controller to improve the force tracking performance. Considering the effect of the friction, a LuGre model based friction compensation is synthesized, in which the unmeasurable state is estimated by a dual state observer via a controlled learning mechanism to guarantee that the estimation is bounded. The modeling errors are attenuated by a well-designed robust controller with a control accuracy measured by a design parameter. Employing the dual state observer is to capture the different effects of the unmeasured state and hence can improve the friction compensation accuracy. The tracking performance is summarized by a derived theorem. Experimental results are also obtained to verify the high performance nature of the proposed control strategy.

Precise control of a magnetically suspended double-gimbal control moment gyroscope using differential geometry decoupling method

Precise control of a magnetically suspended double-gimbal control moment gyroscope （MSDGCMG） is of vital importance and challenge to the attitude positioning of spacecraft owing to its multivariable, nonlinear and strong coupled properties. This paper proposes a novel linearization and decoupling method based on differential geometry theory and combines it with the internal model controller （IMC） to guarantee the system robustness to the external disturbance and parameter uncertainty. Furthermore, by introducing the dynamic compensation for the inner-gimbal rate-servo system and the magnetically suspended rotor （MSR） system only, we can eliminate the influence of the unmodeled dynamics to the decoupling control accuracy as well as save costs and inhibit noises effectively. The simulation results verify the nice decoupling and robustness performance of the system using the proposed method.

Optimal Predictive Impedance Control in the Presence of Uncertainty for a Lower Limb Rehabilitation Robot

As an innovative concept,an optimal predictive impedance controlle is introduced here to control a lower limb rehabilitation robo in the presence of uncertainty.The desired impedance law is considered to propose a conventional model-based impedance controller for the LLRR.However,external disturbances,model imperfection,and parameters uncertainties reduce the performance of the controller in practice.In order to cope with these uncertainties,an optimal predictive compensator is introduced as a solution for a proposed convex optimization problem,which is performed on a forward finite-length horizon.As a result,the LLRR has the desired behavior even in an uncertain environment.The performance and efficiency of the proposed controller are verified by the simulation results.