Performance verification of different control schemes in human lower extremity rehabilitation robot

Hasan, Sk; Dhingra, Anoop K.

doi:10.1016/j.rico.2021.100028

Cited by 5 publications

(9 citation statements)

References 29 publications

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“…The trajectory tracking performance of the proposed LQR is compared to PID, Computed Torque Controller (CTC), and Sliding Mode Controller (SMC) to evaluate its effectiveness. A more detailed description of CTC, PID, and SMC is available (Hasan and Dhingra, 2021). Table 5 shows the joint-wise maximum trajectory tracking errors, peak torque requirements (Nm), peak power requirements, combined peak power requirement, and the total energy consumption.…”

Section: Comparison Between Lqr and Other Control Schemesmentioning

confidence: 99%

Developing a Linear Quadratic Regulator for Human Lower Extremity Exoskeleton Robot

Hasan¹,

Dhingra²

2022

Journal of Mechatronics and Robotics

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During the last two decades, exoskeleton robot-assisted neurorehabilitation has received a lot of attention. The major reason for active research in robot-assisted rehabilitation is its ability to provide various types of physical therapy at different stages of physical and neurological recovery. The performance of the robot-assisted physical therapy is greatly influenced by the robot motion control system. Robot dynamics are nonlinear, but many linear control schemes can adequately handle the nonlinear dynamics with the help of feedback linearization techniques. In this study, the dynamic model of the human lower extremities was developed. A state-space form of the human lower extremity nonlinear dynamic model is presented. LuGre friction model was used to simulate the robot joint friction. A Linear Quadratic Regulator (LQR) was designed to control the human lower extremity dynamics. Dynamic simulations were carried out in the Matlab-Simulink environment. The designed controller's tracking performance was demonstrated in the presence of joint friction. The developed controller's tracking performance is assessed by comparing the results obtained using LQR with other linear and nonlinear controllers (PID, Computed torque control, and Sliding mode control). For performance verification, the same robot dynamics, friction model, and trajectories were used. The stability of the developed control system is also analyzed.

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Section: Comparison Between Lqr and Other Control Schemesmentioning

confidence: 99%

Developing a Linear Quadratic Regulator for Human Lower Extremity Exoskeleton Robot

Hasan¹,

Dhingra²

2022

Journal of Mechatronics and Robotics

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“…The potential energy of a rigid body is determined by its location Equations (11) and (12), and the kinetic energy of a rigid body is proportional to its velocity Equations ( 13) and ( 14). After determining the total energy, robot dynamic equations of F I G U R E 1 3 Simultaneous trajectory tracking performance of the developed radial basis function network controller HASAN -235 motion were developed using the Lagrange energy method Equation (15).…”

Section: Dynamic Modelling Of the Exoskeleton Robotmentioning

confidence: 99%

“…A linearised plant can be controlled by any type of linear control algorithm. The following are some examples of modelbased robot schemes that are often utilised in robot control applications: computer torque control [15], adaptive control [16], model reference computed torque control [17] and sliding mode control [18]. As it is impossible to precisely quantify the payload of an exoskeleton robot, the robot controller needs to be strong enough to handle parametric variations.…”

Section: Introductionmentioning

confidence: 99%

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Radial basis function‐based exoskeleton robot controller development

Hasan

2022

IET Cyber-Syst and Robotics

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The realisation of a model-based controller for a robot with a higher degree of freedom requires a substantial amount of computational power. A high-speed CPU is required to maintain a higher sampling rate. Multicore processors cannot boost the performance or reduce the execution time as the programs are sequentially structured. The neural network is a great tool to convert a sequentially structured program to an equivalent parallel architecture program. In this study, a radial basis function (RBF) neural network is developed for controlling 7 degrees of freedom of the human lower extremity exoskeleton robot. A realistic friction model is used for modelling joint friction. High trajectory tracking accuracies have been obtained. Evidence of computational efficiency has been observed. The stability analysis of the developed controller is presented. Analysis of variance is used to assess the controller's resilience to parameter variation. To show the effectiveness of the developed controller, a comparative study was performe between the developed RBF network-based controller and Sliding Mode Controller, Computed Torque Controller, Adaptive controller, Linear Quadratic Regulator and Model Reference Computed Torque Controller.

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“…Experiment results guarantee that a model-free PID controller can be most suitable for maneuvering a therapeutic exoskeleton robot to provide rehabilitation therapy. It is worth mentioning that several researchers have carried out simulation studies and experiments with the PID controller for maneuvering exoskeleton robots for rehabilitation of human upper or lower limbs [ 40 , 41 , 42 , 43 ]. However, to the authors’ knowledge, none of the prior work thoroughly investigated the effectiveness of a well-established conventional PID controller to provide robot-aided rehabilitation to human subjects’ varying conditions from the controller performance perspective.…”

Section: Introductionmentioning

confidence: 99%

Robustness and Tracking Performance Evaluation of PID Motion Control of 7 DoF Anthropomorphic Exoskeleton Robot Assisted Upper Limb Rehabilitation

Ahmed

Islam

Brahmi

et al. 2022

Sensors

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Upper limb dysfunctions (ULD) are common following a stroke. Annually, more than 15 million people suffer a stroke worldwide. We have developed a 7 degrees of freedom (DoF) exoskeleton robot named the smart robotic exoskeleton (SREx) to provide upper limb rehabilitation therapy. The robot is designed for adults and has an extended range of motion compared to our previously designed ETS-MARSE robot. While providing rehabilitation therapy, the exoskeleton robot is always subject to random disturbance. Moreover, these types of robots manage various patients and different degrees of impairment, which are quite impossible to model and incorporate into the robot dynamics. We hypothesize that a model-independent controller, such as a PID controller, is most suitable for maneuvering a therapeutic exoskeleton robot to provide rehabilitation therapy. This research implemented a model-free proportional–integral–derivative (PID) controller to maneuver a complex 7 DoF anthropomorphic exoskeleton robot (i.e., SREx) to provide a wide variety of upper limb exercises to the different subjects. The robustness and trajectory tracking performance of the PID controller was evaluated with experiments. The results show that a PID controller can effectively control a highly nonlinear and complex exoskeleton-type robot.

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Performance verification of different control schemes in human lower extremity rehabilitation robot

Cited by 5 publications

References 29 publications

Developing a Linear Quadratic Regulator for Human Lower Extremity Exoskeleton Robot

Developing a Linear Quadratic Regulator for Human Lower Extremity Exoskeleton Robot

Radial basis function‐based exoskeleton robot controller development

Robustness and Tracking Performance Evaluation of PID Motion Control of 7 DoF Anthropomorphic Exoskeleton Robot Assisted Upper Limb Rehabilitation

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