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Biomechanics And Motor Control Of Human Movement

Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller

To rehabilitate individuals with impaired upper limb (UL) functions due to neurological disorders, this research focuses on trajectory tracking control (representing passive rehabilitation exercise) of a 7 DOFs exoskeleton robot named ETS-MARSE. It is a redundant type of robotic manipulator having a very complex structure which is designed based on human UL joint articulations. The exoskeleton is constantly encountered with external disturbances and unknown dynamics such as friction forces, and backlash which is hard to model. Moreover, this type of robot needs to deal with the unknown dynamics of a wide range of subjects with different degrees of UL impairments. Therefore, to deal with this modeling uncertainty, in this paper we propose a novel adaptive neural network fast fractional integral terminal sliding mode control (ANFFITSMC) approach to maneuver the ETS-MARSE to provide passive arm movement therapy. To address the chattering phenomena which are observed in the fast fractional integral terminal sliding mode control (FFITSMC), a new adaptive radial basis function neural network (ARBFN) is incorporated with the FFITSMC. The Lyapunov theory is used in order to prove the stability of the proposed controller. Simulation results validated the efficient performance of the ANFFITSMC in terms of chattering reduction and trajectory tracking.

Adaptive Neural Network Fast Fractional Sliding Mode Control of a 7-DOF Exoskeleton Robot

This paper investigates the control of a 5-DOF upper-limb exoskeleton robot used for passive rehabilitation therapy. The robot is subject to uncertain dynamics, disturbance torques, unavailable full-state measurement, and different types of actuation faults. An adaptive nonlinear control scheme, which uses a new reaching law-based sliding mode control strategy, is proposed. This scheme incorporates a high-gain state observer with dynamic high-gain matrix and a fuzzy neural network (FNN) for state vector and nonlinear dynamics estimation, respectively. Using dynamic parameters, the scheme provides an efficient mean for simultaneously tackling the effects of FNN approximation errors, disturbance torques and actuation faults without any prior bounds knowledge and fault detection and diagnosis components. Using simulation results, it is shown that with the presented scheme, faster response, fewer oscillations during transient phase, good tracking accuracy, and chattering-free control torques with lower amplitudes are obtained.

Fuzzy neural network and observer-based fault-tolerant adaptive nonlinear control of uncertain 5-DOF upper-limb exoskeleton robot for passive rehabilitation

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

https://www.mdpi.com/1424-8220/21/6/2146/pdf

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review.

In this paper, mechanical design and control of an exoskeleton robot for shoulder rehabilitation after stroke are presented. Initially, mechanical design of a new 3 degrees of freedom (DOF) exoskeleton robot for shoulder joint rehabilitation is presented. All robot measurements are based on the properties of upper limb of an adult person. A new open circular mechanism is proposed for the third joint. Afterwards, direct and inverse kinematics, Jacobian matrix, singular points, and dynamics of the robot are presented. In order to study the ability of the robot to follow the optimized trajectories, sliding mode controller (SMC) is proposed to track desired trajectories. In most rehabilitation robots, the attention is on robot's mechanical design, so linear controllers are used to control the robot. However, rehabilitation robots are non-linear in nature and non-linear control methods are required that can reject uncertainties and are resistant to parameter changes. SMC is robust due to its nonlinear nature, and can reject uncertainties and disturbances applying on the system such as patient's hand tremor. The parameters of the SMC are tuned using Genetic Algorithm (GA). The main advantage of this robot compared to similar systems are being low weight, having a special mechanism for third joint that solves the known issues associated with long wiring and closed mechanisms, allowing translational degrees of freedom of the shoulder, ease of use, comfort for the patient and the tracking performance of the controllers.

Sliding mode control of an exoskeleton robot for use in upper-limb rehabilitation

Because of its ease of implementation, a linear PID controller is generally used to control robotic manipulators. Linear controllers cannot effectively cope with uncertainties and variations in the parameters; therefore, nonlinear controllers with robust performance which can cope with these are recommended. The sliding mode control (SMC) is a robust state feedback control method for nonlinear systems that, in addition having a simple design, efficiently overcomes uncertainties and disturbances in the system. It also has a very fast transient response that is desirable when controlling robotic manipulators. The most critical drawback to SMC is chattering in the control input signal. To solve this problem, in this study, SMC is used with a boundary layer (SMCBL) to eliminate the chattering and improve the performance of the system. The proposed SMCBL was compared with inverse dynamic control (IDC), a conventional nonlinear control method. The kinematic and dynamic equations of the IRB-120 robot manipulator were initially extracted completely and accurately, and then the control of the robot manipulator using SMC was evaluated. For validation, the proposed control method was implemented on a 6-DOF IRB-120 robot manipulator in the presence of uncertainties. The results were simulated, tested, and compared in the MATLAB/Simulink environment. To further validate our work, the results were tested and confirmed experimentally on an actual IRB-120 robot manipulator.

Dynamic analysis, simulation, and control of a 6-DOF IRB-120 robot manipulator using sliding mode control and boundary layer method

In this study, a robust neuro-adaptive controller for cable-driven parallel robots is proposed. The robust neuro-adaptive control system is comprised of a computation controller and a robust controller. The computation controller containing a neural-network-estimator with radial basis function activator is the principal controller and the robust controller is designed to achieve tracking performance. An on-line tuning method is derived to tune the parameters of the neural network for estimating the controlled system dynamic function. To investigate the effectiveness of the robust adaptive control, the design methodology is applied to control a cable-driven parallel robot. Simulation results demonstrate that the proposed robust adaptive control system can achieve favorable tracking performances for the robot.

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On The Design of The Robust Neuro-Adaptive Controller for Cable-driven Parallel Robots

This paper proposed an optimal fuzzy controller for damping the power system oscillation in multi-machine environment. In this strategy, the proposed controller is optimized by new chaotic invasive weed optimization algorithm. Furthermore, a new objective function has been considered to test the proposed controller in different load conditions which increase the stability of system after disturbances. For this purpose, the damping factor, damping ratio, and a combination of the damping factor and damping ratio were analyzed and compared with the proposed objective function. The effectiveness of the proposed strategy has been applied in two multi-machine power system test cases as three machine 9-bus IEEE standard and 10-machine 39-bus New England power systems. The eigenvalue analysis and nonlinear time-domain simulation results proof the effectiveness of the proposed strategy.

Optimal Fuzzy Controller Based on Chaotic Invasive Weed Optimization for Damping Power System Oscillation

This paper designs and implements a chattering-free terminal sliding mode control approach for a class of chaotic systems with unknown uncertainties. It considers sliding mode control (SMC) to deal with the dynamic model uncertainties of the chaos system, and uses a combination of SMC with an adaptive control approach to solve the upper boundaries problem of unknown model uncertainties and their estimation. Chattering is completely eliminated without over estimating the control gains by adopting an adaptive continuous barrier function in the dynamic switching function. Using the Lyapunov’s stability theory, it was shown that the proposed scheme can guarantee the convergence of system states to the vicinity of the sliding surface in finite time. Additionally, the adoption of a sliding surface with a nonlinear and integral switching function resulted in removing the reaching phase of the sliding surface and yielding a controller that is robust to uncertainties from the start. The effectiveness of the proposed control method was assessed using three scenarios implemented to a Liu’s uncertain chaotic system in MATLABSimulink environment. The obtained results confirmed the ability of the proposed approach to achieve continuous and smooth control rules for such chaotic systems. Among the main attributes of the proposed control method are its ability to completely eliminate chattering and yield a robust performance against model uncertainties and unknown external disturbances; common issues in chaotic systems.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09903618.pdf

Mojtaba Hadi Barhaghtalab

Papers

Sliding mode control of an exoskeleton robot for use in upper-limb rehabilitation

Dynamic analysis, simulation, and control of a 6-DOF IRB-120 robot manipulator using sliding mode control and boundary layer method

On The Design of The Robust Neuro-Adaptive Controller for Cable-driven Parallel Robots

Optimal Fuzzy Controller Based on Chaotic Invasive Weed Optimization for Damping Power System Oscillation

Chattering-Free Terminal Sliding Mode Control Based on Adaptive Barrier Function for Chaotic Systems With Unknown Uncertainties