PID control with gravity compensation for hydraulic 6-DOF parallel manipulator

Abstract A novel model-based controller for 6 degree-of-freedom (DOF) hydraulic driven parallel manipulator considering the nonlinear characteristic of hydraulic systems-proportional plus derivative with dynamic gravity compensation controller is presented, in order to improve control performance and eliminate steady state errors. In this paper, 6-DOF parallel manipulator is described as multi-rigid-body systems, the dynamic models including mechanical system and hydraulic driven system are built using Kane method and hydromechanics methodology, the numerical forward kinematics and inverse kinematics is solved with Newton-Raphson method and close-form solutions. The model-based controller is developed with feedback of actuator length, desired trajectories and system states acquired by forward kinematics solution as the input and servovalve current as its output. The hydraulic system is decoupled by local velocity compensation in inner control loop prerequisite for the controller. The performance revolving stability, accuracy and robustness of the proposed control scheme for 6-DOF parallel manipulator is analyzed in theory and simulation. The theoretical analysis and simulation results indicate the controller can improve the control performance and eliminate the steady state errors of 6-DOF hydraulic driven parallel manipulator

Assessing the quality of models of the ambient solar wind

In this paper we present an assessment of the status of models of the global Solar Wind in the inner heliosphere. We limit our discussion to the class of models designed to provide solar wind forecasts, excluding those designed for the purpose of testing physical processes in idealized configurations. In addition, we limit our discussion to modeling of the ‘ambient’ wind in the absence of coronal mass ejections. In this assessment we cover use of the models both in forecast mode and as tools for scientific research. We present a brief history of the development of these models, discussing the range of physical approximations in use. We discuss the limitations of the data inputs available to these models and its impact on their quality. We also discuss current model development trends

Online Adaptive Prediction of Human Motion Intention Based on sEMG

Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact 236±9 ms in advance. Meanwhile, the proposed feature extraction method can achieve 90.71±3.42% accuracy of sEMG reconstruction and can guarantee 73.70±5.01% accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to 87.65±3.83% and decreases the angle prediction error from 4.03∘ to 2.36∘. The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG

Modal Decoupled Dynamics Feed-Forward Active Force Control of Spatial Multi-DOF Parallel Robotic Manipulator

According to the parallel mechanism theory, this paper proposes a novel intelligent robotic spine brace for the treatment of scoliosis. Nevertheless, this type of parallel mechanism has the following disadvantages: strong dynamic coupling in task space or joint space, adverse effect of system’s gravity, and lower response frequency in roll and pitch orientations, which seriously affect the performance of the system. In order to solve those boring problems, this paper presents a novel active force control structure, modal space dynamic feed-forward (MSDF) force control strategy. Besides, this paper expresses the intelligent robotic brace system model including the dynamic and kinematic models and the electric actuator model with Kane strategy. The stability of the intelligent system with the novel control strategy is proved. In order to evaluate the performance of the presented MSDF force control method, this paper builds the parallel mechanism experimental platform. It can be seen from experimental results that the proposed motion control method solves these boring problems well