Robust LQR-Based Neural-Fuzzy Tracking Control for a Lower Limb Exoskeleton System with Parametric Uncertainties and External Disturbances

Event-triggered adaptive control for upper-extremity therapeutic robot in active-assist mode: A simulation study

Upper extremity rehabilitation exercises are essential for individuals recovering from injuries or conditions that affect their arm and hand functionality. Robot-assisted therapy has gained popularity as it offers precise control, objective assessment, and customizable treatment programs. However, several challenges persist, including uncertainties in the patient’s and robot dynamics, limited communication, and the need to maintain a compliant patient-robot interaction. Therefore, an event-triggered adaptive backstepping (ETAB) admittance control strategy is proposed in this work to address these challenges. Initially, the framework of the robot-assisted therapeutic process is briefly explained. The architecture of the proposed control strategy is formulated with two control modules. Thereafter, the adaptive backstepping technique is employed with an online adaptation law to deal with dynamic uncertainties. Moreover, the problem of limited communication is handled using the proposed design of a Lyapunov-based event-triggered mechanism. The admittance controller is integrated to maintain a compliant patient-robot interaction and consider the participation of the patient in the therapeutic sessions. The effectiveness of the proposed control strategy is verified using an end-effector type rehabilitation robot performing two different rehabilitation exercises. Furthermore, a comparative performance analysis is carried out with the proportional-integral-derivative controller (PID) and the adaptive sliding mode controller (ASMC). Based on the simulation runs, the proposed controller has shown promising tracking behavior, appropriate compliant interaction, and considerable reduction in the transmitted signals during the passive and active-assist training exercises.

Retracted: Robust LQR-Based Neural-Fuzzy Tracking Control for a Lower Limb Exoskeleton System with Parametric Uncertainties and External Disturbances

[This retracts the article DOI: 10.1155/2021/5573041.].

Investigating the Path Tracking Algorithm Based on BP Neural Network

In this paper, we propose an adaptive path tracking algorithm based on the BP (back propagation) neural network to increase the performance of vehicle path tracking in different paths. Specifically, based on the kinematic model of the vehicle, the front wheel steering angle of the vehicle was derived with the PP (Pure Pursuit) algorithm, and related parameters affecting path tracking accuracy were analyzed. In the next step, BP neural networks were introduced and vehicle speed, radius of path curvature, and lateral error were used as inputs to train models. The output of the model was used as the control coefficient of the PP algorithm to improve the accuracy of the calculation of the front wheel steering angle, which is referred to as the BP-PP algorithm in this paper. As a final step, simulation experiments and real vehicle experiments are performed to verify the algorithm's performance. Simulation experiments show that compared with the traditional path tracking algorithm, the average tracking error of BP-PP algorithm is reduced by 0.025 m when traveling at a speed of 3 m/s on a straight path, and the average tracking error is reduced by 0.27 m, 0.42 m, and 0.67 m, respectively, at a speed of 1.5 m/s with a curvature radius of 6.8 m, 5.5 m, and 4.5 m, respectively. In the real vehicle experiment, an electric patrol vehicle with an autonomous tracking function was used as the experimental platform. The average tracking error was reduced by 0.1 m and 0.086 m on a rectangular road and a large curvature road, respectively. Experimental results show that the proposed algorithm performs well in both simulation and actual scenarios, improves the accuracy of path tracking, and enhances the robustness of the system. Moreover, facing paths with changes in road curvature, the BP-PP algorithm achieved significant improvement and demonstrated great robustness. In conclusion, the proposed BP-PP algorithm reduced the interference of nonlinear factors on the system and did not require complex calculations. Furthermore, the proposed algorithm has been applied to the autonomous driving patrol vehicle in the park and achieved good results.

Engineering design strategies for force augmentation exoskeletons: A general review

In the industrial and military sector, work activities are required transporting or supporting heavy loads manually, affecting this the human spinal column due to the weight of the loads or the repetition of this labor. In this regard, the use of force-enhancing exoskeletons is a potential solution to this issue. Therefore, this article summarizes the state of the art in relevant contributions to structural design, control systems, actuators, and performance metrics to evaluate the proper functioning of exoskeletons used for load support and transfer. This is essential to address current and new open problems in these applications, and this includes reducing the metabolic cost and enhancing the loading force in exoskeletons, in which challenges such as structural design and kinetic interactions between the human and the robot are presented. The systematic review of the strategies found in the literature helps addressing these challenges in an orderly way. The proposal of some alternative solutions could help to solving some of the challenges mentioned above, as well as further research to improve the design of these devices is necessary.

Comparison Between Some Nonlinear Controllers for the Position Control of Lagrangian-type Robotic Systems

This work addresses the set-point control problem of the position of fully-actuated Lagrangian-type robotic systems by means of some nonlinear control laws. We adopt four different nonlinear control laws: the PD plus gravity compensation controller, the PD plus desired gravity compensation controller, the computed-torque controller and the augmented PD plus gravity compensation controller. An in-depth comparison between these control laws and their application is achieved. Indeed, using some properties, we design some conditions on the feedback gains of the nonlinear controllers ensuring the stability in the closed loop of the zero-equilibrium point and its uniqueness. At the end of this work, we adopt a planar two-degree-of-freedom manipulator robot to illustrate via simulation the difference between and the efficiency of the adopted nonlinear controllers.

Force Transmission Analysis and Optimization of Bowden Cable on Body in a Flexible Exoskeleton

The Bowden cable is a significant force transmission equipment for a flexible exoskeleton. However, the previous researches of Bowden cable had emphasized on the data from experimenting test board, instead of on human body, which produced the inaccurate assisting analysis of the flexible exoskeleton. In this paper, a flexible exoskeleton for assisting knee extension was proposed, which provided an on-body condition. Then, the friction force and its influencing factors between the wire rope and sheath of the Bowden cable from the motor to the anchor of knee have been analyzed. The segment models of force transmission with the concern of three kinds of friction modes were established, and the relationship between various lengths and bending angles of Bowden cable was fitted to the equations of curve. Furthermore, the association rule between the force transmission and the lengths of Bowden cable was obtained, based on which, the optimal force transmission efficiency was 78.68% when the length value of the Bowden cable was 475 mm. A flexible exoskeleton prototype was assembled; then, the experiments with force transmission and metabolic cost have been developed. The results showed that the force transmission efficiency had strong association with the lengths of Bowden cable, instead of the transmission velocities. Furthermore, this knee assistance exoskeleton reduced the net metabolic cost of the testees during walking. These experiments results corroborated the force transmission modeling and simulation of the Bowden cable on body we proposed in this paper.

Assistive Mobility Control of a Robotic Hip-Knee Exoskeleton for Gait Training

In this paper, we present an assistive mobility control for a robotic hip-knee exoskeleton intended for gait training. The robotic hip-knee exoskeleton is designed with an active flexion/extension and a passive abduction/adduction at each hip joint and an active flexion/extension at each knee joint to comply with the movement of lower limbs. While facilitating walking with the robotic exoskeleton, model-free linear extended state observer (LESO)-based controllers are proposed for gait control, in which the LESO is used to deal with each user's different lower limb parameters and unknown exerted torques. Walking and ascending experiments were conducted to evaluate the performance of the proposed methods, and the results are shown with respect to walking parameters. Moreover, a preliminary study for an extended application to the recovery of normal gaits that relieves the freezing of gait (FOG) in Parkinson's disease (PD) patients is also investigated in the paper.