Adaptive admittance tracking control for interactive robot with prescribed performance

An adaptive control approach is presented in this paper for tracking desired trajectories in interactive manipulators. The controller design incorporates prescribed performance functions (PPFs) to improve dynamic performance. Notably, the performance of the output error is confined in an envelope characterized by exponential convergence, leading to convergence to zero. This feature ensures a prompt response from admittance control and establishes a reliable safety framework for interactions. Simulation results provide practical insights,demonstrating the viability of the control scheme proposed in this paper.

Flexible assistance strategy of lower limb rehabilitation exoskeleton based on admittance model

Exercise training based on exoskeleton is an effective rehabilitation method for stroke patients. However, some rehabilitation exoskeletons still have poor wearable performance and obvious human-robot impedance during training, which easily cause secondary injuries to the patients. In this study, a variable admittance control strategy is proposed to improve the operator's wearable comfort, which can adapt to different operators by regulating admittance parameters. The admittance controller has two feedback loops: the position inner-loop and the admittance outer-loop. Meanwhile, the exoskeleton Lagrange model is constructed with unknown friction disturbance. By using Lyapunov technique and backstepping with state observer, the joint position error of exoskeleton is convergence into a zero neighborhood. The effectiveness of the proposed admittance controller is verified by both simulation and experiment. The tracking error of hip and knee joint is less than 4 degrees while the human-robot interaction torque is constrained in a tolerable range of the operator.

Adaptive patient-cooperative compliant control of lower limb rehabilitation robot

With the increase in the number of stroke patients,there is a growing demand for rehabilitation training.Robot-assisted training is expected to play a crucial role in meeting this demand.To ensure the safety and comfort of patients during rehabilitation training,it is important to have a patient-cooperative compliant control system for rehabilitation robots.In order to enhance the motion compliance of patients during rehabilitation training,a hierarchical adaptive patient-cooperative compliant control strategy that includes patient-passive exercise and patient-cooperative exercise is proposed.A low-level adaptive backstepping position controller is selected to ensure accurate tracking of the desired trajectory.At the high-level,an adaptive admittance controller is employed to plan the desired trajectory based on the interaction force between the patient and the robot.The results of the patient-robot cooperation experiment on a rehabilitation robot show a significant improvement in tracking trajectory,with a decrease of 76.45%in the dimensionless squared jerk(DSJ)and a decrease of 15.38%in the normalized root mean square deviation(NRMSD)when using the adaptive admittance controller.The proposed adaptive patient-cooperative control strategy effectively enhances the compliance of robot movements,thereby ensuring the safety and comfort of patients during rehabilitation training.

Adaptive Human-Robot Collaboration Control Based on Optimal Admittance Parameters

In order to help the operator perform the human-robot collaboration task and optimize the task performance,an adaptive control method based on optimal admittance parameters is proposed.The overall control structure with the inner loop and outer loop is first established.The tasks of the inner loop and outer loop are robot control and task optimization,respectively.An inner-loop robot controller integrated with barrier Lyapunov function and radial basis function neural networks is then proposed,which makes the robot with unknown dynamics securely behave like a prescribed robot admittance model sensed by the operator.Subsequently,the optimal parameters of the robot admittance model are obtained in the outer loop to minimize the task tracking error and interaction force.The optimization problem of the robot admittance model is transformed into a linear quadratic regulator problem by constructing the human-robot collaboration system model.The model includes the unknown dynamics of the operator and the task performance details.To relax the requirement of the system model,the integral reinforcement learning is employed to solve the linear quadratic regulator problem.Besides,an auxiliary force is designed to help the operator complete the specific task better.Compared with the traditional control scheme,the security performance and interaction performance of the human-robot collaboration system are improved.The effectiveness of the proposed method is verified through two numerical simulations.In addition,a practical human-robot collaboration experiment is carried out to demonstrate the performance of the proposed method.

Task-space Whole-body Control with Variable Contact Force Control for Position-controlled Humanoid Adaptation to Unknown Disturbance

Whole-body control is beneficial for improving the disturbance adaptation of humanoid robots,since it can simultaneously optimize desired joint torque,joint acceleration,and contact force while considering whole-body dynamics and other physical limits.However,the lack of torque feedback information prevents the position-controlled humanoids from utilizing whole-body control directly,because it enhances the difficulty of guaranteeing desired contact force which is important for maintaining stability.In this paper,a whole-body control that integrates task-space inverse dynamics and variable contact force control is proposed for position-controlled humanoids to enhance the robot’s adaptability toward the unknown disturbance.The task-space inverse dynamics generates the desired joint acceleration and contact force with the consideration of whole-body dynamics and other limits to track the references.The variable contact force control modifies references related to Center of Mass(CoM)and end effectors to ensure reasonable contact force tracking performance,thereby assuring good tracking performance of CoM and momentum to maintain robot stability.Simulations and experiments of balancing and walking under unknown disturbance have been successfully conducted on a position-controlled humanoid robot,BHR-7P3,with the proposed method.

Design and Implementation of a Rehabilitation Upper-limb Exoskeleton Robot Controlled by Cognitive and Physical Interfaces

This paper presents an upper limb exoskeleton that allows cognitive(through electromyography signals)and physical user interaction(through load cells sensors)for passive and active exercises that can activate neuroplasticity in the rehabilitation process of people who suffer from a neurological injury.For the exoskeleton to be easily accepted by patients who suffer from a neurological injury,we used the ISO9241-210:2010 as a methodology design process.As the first steps of the design process,design requirements were collected from previous usability tests and literature.Then,as a second step,a technological solution is proposed,and as a third step,the system was evaluated through performance and user testing.As part of the technological solution and to allow patient participation during the rehabilitation process,we have proposed a hybrid admittance control whose input is load cell or electromyography signals.The hybrid admittance control is intended for active therapy exercises,is easily implemented,and does not need musculoskeletal modeling to work.Furthermore,electromyography signals classification models and features were evaluated to identify the best settings for the cognitive human–robot interaction.

Ant Lion Algorithm for Optimized Controller Gains for Power Quality Enrichment of Off-grid Wind Power Harnessing Units

The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the voltage and maintains the frequency within the limits while working with both linear and nonlinear loads for varying wind speeds.The admittance algorithm is simple and easy to implement and works very efficiently to generate the triggering signals for the controller of the WPHU.The wind power harnessing unit comprising of a squirrel cage induction generator,a star-delta transformer,a battery storage system and the control unit are modeled using Matlab/Simulink R2019.An isolated transformer with a star-delta configuration connects the load and the generator circuit with the controller to reduce the dc bus voltage and mitigate current in the neutral line.The response of the system during the dynamic loading depends on the best possible compensator proportional-integral(PI)gains.The antlion optimization algorithm is compared with particle swarm optimization and grey wolf optimization and is found to have the advantages of good convergence,high efficiency and fast calculating speed.It is therefore used to extract the optimal values of frequency and voltage PI gains.The simulation results of the control algorithm for the WPHU are validated in a real-time environment in a dSpace1104 laboratory set up.This algorithm is proven to have a quick response,maintain the required frequency,suppress the current harmonics,regulate voltage,help in balancing the load and compensating for the neutral current.