Adaptive Synergetic Motion Control for Wearable Knee-Assistive System: A Rehabilitation of Disabled Patients

Anti-disturbance control design of Exoskeleton Knee robotic system for rehabilitative care

In this study, Active Disturbance Rejection Control (ADRC) has been designed for motion control of knee-joint based on exoskeleton medical robot. The extended state observer (ESO) is the main part of ADRC structure, which is responsible for estimating both actual states and system uncertainties. The proposed control scheme has adopted two versions of observers as disturbance estimators: linear extended state observer (LESO) and nonlinear extended state observer (NESO). The efficacy of proposed ADRC is strongly related to the performance of used ESO. As such, a comparison study has been conducted to evaluate the performance of two ADRCs in terms of disturbance-rejection capability and robustness to variation in system parameters under two version of ESO (LSO and NLESO). In order to enhance the dynamic performance of ADRC, Particle Swarm Optimization (PSO) algorithm has been used to optimally tune the design parameters of control scheme. To show the effectiveness of proposed LESO-based ADRC and NLESO-based ADRC, numerical simulation have been conducted. The proposed controllers have tested for an uncertain exoskeleton-knee system, where a 20% change in parameters was permitted over their nominal values. The results indicate that the ADRC algorithm based on LESO outperforms the one based on NESO in terms of disturbances rejection ability and robustness to parameters' variations.

Space Robot Sensor Noise Amelioration Using Trajectory Shaping

Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller's bandwidth. A variety of input trajectories can be used to drive a controller's attempt to ameliorate the control-structural interactions where feedback is provided by low-quality, noisy sensors. Traditionally, step functions are used as the ideal input trajectory. However, step functions are not ideal in many applications, as they are discontinuous. Alternative input trajectories are explored in this manuscript and applied to an example system that includes a flexible appendage attached to a rigid main body. The main body is controlled by a reaction wheel. The equations of motion of the flexible appendage, rigid body, and reaction wheel are derived. A benchmark feedback controller is developed to account for the rigid body modes. Additional filters are added to compensate for the system's flexible modes. Sinusoidal trajectories are autonomously generated to feed the controller. Benchmark feedforward whiplash compensation is additionally implemented for comparison. The method without random errors with the smallest error is the sinusoidal trajectory method, which showed a 97.39% improvement when compared to the baseline response when step trajectories were commanded, while the sinusoidal method was inferior to traditional step trajectories when sensor noise and random errors were present.

A Deep Learning Approach for Biped Robot Locomotion Interface Using a Single Inertial Sensor

In this study, we introduce a novel framework that combines human motion parameterization from a single inertial sensor, motion synthesis from these parameters, and biped robot motion control using the synthesized motion. This framework applies advanced deep learning methods to data obtained from an IMU attached to a human subject's pelvis. This minimalistic sensor setup simplifies the data collection process, overcoming price and complexity challenges related to multi-sensor systems. We employed a Bi-LSTM encoder to estimate key human motion parameters: walking velocity and gait phase from the IMU sensor. This step is followed by a feedforward motion generator-decoder network that accurately produces lower limb joint angles and displacement corresponding to these parameters. Additionally, our method also introduces a Fourier series-based approach to generate these key motion parameters solely from user commands, specifically walking speed and gait period. Hence, the decoder can receive inputs either from the encoder or directly from the Fourier series parameter generator. The output of the decoder network is then utilized as a reference motion for the walking control of a biped robot, employing a constraint-consistent inverse dynamics control algorithm. This framework facilitates biped robot motion planning based on data from either a single inertial sensor or two user commands. The proposed method was validated through robot simulations in the MuJoco physics engine environment. The motion controller achieved an error of ≤5° in tracking the joint angles demonstrating the effectiveness of the proposed framework. This was accomplished using minimal sensor data or few user commands, marking a promising foundation for robotic control and human-robot interaction.

Synergetic-PI controller based on genetic algorithm for DPC-PWM strategy of a multi-rotor wind power system

This work designs a powerful new nonlinear control technique using synergetic control (SC), proportional-integral (PI) controller, and genetic algorithm (GA) for multi-rotor wind energy (MRWE) conversion systems, whereby an asynchronous generator (AG) is used to achieve optimal energy extraction. The direct power control (DPC) technique is used based on the proposed SC-PI-GA (SPI-GA) technique to control the AG-based MRWE system, where this new nonlinear control technique is used to achieve stable control characteristics under random changes in wind speed and to provide great robustness against modeling uncertainties. Moreover, the pulse width modulation (PWM) technique is used to control the AG inverter due to its simplicity and ease of implementation. In this proposed DPC-SPI-GA technique, we need to measure current and voltage to estimate the active power and the reactive power. Also, inner loops are not used in this proposed DPC-SPI-GA technique as is the case in the field-oriented control (FOC) technique, where the proposed system in this work is characterized by an integrated structure. Three different tests are proposed to study and verify the behavior of the designed DPC-SPI-GA strategy compared to the traditional DPC technique.