Nonlinear control of an exoskeleton seven degrees of freedom robot to realize an active and passive rehabilitation tasks

This doctoral thesis proposes the developments of an exoskeleton robot used to rehabilitate patients with upper-limb impairment, named ETS-MARSE robot. The developments included in this work are the design, and validation of a kinematic inverse solution and nonlinear control strategy for an upper limb exoskeleton robot. These approaches are used in passive and active rehabilitation motion in presence of dynamics and kinematics uncertainties and unexpected disturbances. Considering the growing population of post-stroke victims, there is a need to improve accessibility to physiotherapy by using the modern robotic rehabilitation technology. Recently, rehabilitation robotics attracted a lot of attention from the scientific community since it is able to overcome the limitations of conventional physical therapy. The importance of the rehabilitation robot lies in its ability to provide intensive physiotherapy for a long period time. The measured data of the robot allows the physiotherapist to accurately evaluate the patient’s performance. However, these devices are still part of an emerging area and present many challenges compared to the conventional robotic manipulators, such as the high nonlinearity, dimensional (high number of DOFs) and unknown dynamics (uncertainties). These limitations are provoked due to their complex mechanical structure designed for human use, the types of assistive motion, and the sensitivity of the interaction with a large diversity of human wearers. As a result, these conditions make the robot system vulnerable to dynamic uncertainties and external disturbances such as saturation, friction forces, backlash, and payload. Likewise, the interaction between human and the exoskeleton make the system subjected to external disturbances due to different physiological conditions of the subjects like the different weight of the upper limb for each subject. During a rehabilitation movement, the nonlinear uncertain dynamic model and external forces can turn into unknown function that can affect the performance of the exoskeleton robot. The main challenges addressed in this thesis are firstly to design a human inverse kinematics solution to perform a smooth movement similar to natural human movement (human-like motion). Secondly, to develop controllers characterized by a high-level of robustness and accuracy without any sensitivity to uncertain nonlinear dynamics and unexpected disturbances. This will give the control system more flexibility to handle the uncertainties and parameters’ variation in different modes of rehabilitation motion (passive and active)

Maximum power point tracking controller using Lyapunov theorem of wind turbine under varying wind conditions

Due to the instantaneous variation in wind speed, it is necessary to identify the optimal rotational speed that ensures maximum energy efficiency and system stability. We proposed a controller based on the Lyapunov theorem to extract the maximum power from wind speed and to ensure the overall stability of the controlled system under random operating conditions imposed by wind speed and parameter variations. The control of the Tip speed ratio is based on the Lyapunov theorem (TSR_LT), which is a controller based on Lyapunov's theory and the definition of a positive, energetic function, to ensure the stability of the system being controlled, the dynamics of this function must be negative. The viability of this work is demonstrated by MATLAB-based mathematical and simulation models and a comparison with the results obtained using proportional integral (PI) controller-based tip speed ratio control (TSR_PI controller). The simulation results demonstrate the controller's effectiveness

A Novel Algorithm for Controlling Active and Reactive Power Flows of Electric Vehicles in Buildings and Its Impact on the Distribution Network

In the literature, many optimization algorithms were developed to control electrical loads, especially Electric Vehicles (EVs) in buildings. Despite the success of the existing algorithms in improving the power profile of charging EVs and reducing the total electricity bill of the end-users, these algorithms didn&rsquo t show significant contribution in improving the voltage profile on the network, especially with the existence of highly inductive loads. The control of the active power may not be sufficient to regulate the voltage, even if sophisticated optimization algorithms and control strategies are used. To fill the gap in the literature, we propose a new algorithm that is able to control both the active and reactive power flows using electric vehicles in buildings and homes. The algorithm is composed of two parts the first part uses optimization to control the active power and minimize the electricity bill, while the second part controls the reactive power using the bidirectional converter in the EV in a way that the voltage profile on the distribution transformer respects its limits. The new approach is validated through a comparative study of four different scenarios, (i) without EV, (ii) with EV using uncoordinated charging, (iii) with EV using coordinated charging, (iv) with EV using our proposed algorithm. Results show that our algorithm has maintained the voltage within the recommended limits, and it has minimized the peak load, the electricity cost, and the techno-economic losses on the network. Document type: Articl

Design and Development of an Upper Limb Rehabilitative Robot with Dual Functionality

The design of an upper limb rehabilitation robot for post-stroke patients is considered a benchmark problem regarding improving functionality and ensuring better human–robot interaction (HRI). Existing upper limb robots perform either joint-based exercises (exoskeleton-type functionality) or end-point exercises (end-effector-type functionality). Patients may need both kinds of exercises, depending on the type, level, and degree of impairments. This work focused on designing and developing a seven-degrees-of-freedom (DoFs) upper-limb rehabilitation exoskeleton called ‘u-Rob’ that functions as both exoskeleton and end-effector types device. Furthermore, HRI can be improved by monitoring the interaction forces between the robot and the wearer. Existing upper limb robots lack the ability to monitor interaction forces during passive rehabilitation exercises; measuring upper arm forces is also absent in the existing devices. This research work aimed to develop an innovative sensorized upper arm cuff to measure the wearer’s interaction forces in the upper arm. A PID control technique was implemented for both joint-based and end-point exercises. The experimental results validated both types of functionality of the developed robot

Toxicity and neurophysiological impacts of three plant-derived essential oils against the vineyard mealybug Planococcus ficus

Many natural products are able to control pests and can be used as alternatives for chemical treatments. Plant essential oils (EOs) have been found to exhibit some biological activity against many insects including mealybugs. This study aimed at studying the insecticidal activity and behavioral and neurophysiological impacts of three plant essential oils against the vine mealybug Planococcus ficus. The topical and fumigant toxicity of Cymbopogon citratus, Mentha piperita, and Pelargonium graveolens essential oils was evaluated against P. ficus adults. The chemical composition analysis of EOs by gas chromatographic-mass spectrometry (GC-MS) revealed citronellal (31.69 %), menthol (73.78 %), and geraniol (39.6%), as major components, respectively. Bioassays of EOs against vine mealybug adults through fumigation toxicity method revealed lethal concentrations LC50 values of 17.01, 26.27 and 24.52 µL·L-1 air for C. citratus, M. piperita, and P. graveolens, respectively. In both topical and fumigant bioassays, essential oil from C. citratus was the most active altering the behavioral response of treated mealybugs which becomes hyperactive and disoriented. EOs induced general stress in P. ficus adults, as evidenced by oxidative stress biomarker analyses. Biochemical analyses showed that the EOs exposure reduced the activity of acetylcholinesterase and significantly induced the glutathione S-transferases and Malondialdehydes accumulation in the vine mealybug tissues. Mortality caused by lemongrass EO positively correlated with the significant decrease in the AChE activity indicating lethal neurological effects. These toxicity bioassays and neurological impact findings provide new informations for formulating effective essential oil based-insecticides to control P. ficus in the framework of integrated pest management programs

Development of an End-Effector Type Therapeutic Robot with Sliding Mode Control for Upper-Limb Rehabilitation

Geriatric disorders, strokes, spinal cord injuries, trauma, and workplace injuries are all prominent causes of upper limb disability. A two-degrees-of-freedom (DoFs) end-effector type robot, iTbot (intelligent therapeutic robot) was designed to provide upper limb rehabilitation therapy. The non-linear control of iTbot utilizing modified sliding mode control (SMC) is presented in this paper. The chattering produced by a conventional SMC is undesirable for this type of robotic application because it damages the mechanical structure and causes discomfort to the robot user. In contrast to conventional SMC, our proposed method reduces chattering and provides excellent dynamic tracking performance, allowing rapid convergence of the system trajectory to its equilibrium point. The performance of the developed robot and controller was evaluated by tracking trajectories corresponding to conventional passive arm movement exercises, including several joints. According to the results of experiment, the iTbot demonstrated the ability to follow the desired trajectories effectively

Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks

Throughout the last decade, many assistive robots for people with disabilities have been developed; however, researchers have not fully utilized these robotic technologies to entirely create independent living conditions for people with disabilities, particularly in relation to activities of daily living (ADLs). An assistive system can help satisfy the demands of regular ADLs for people with disabilities. With an increasing shortage of caregivers and a growing number of individuals with impairments and the elderly, assistive robots can help meet future healthcare demands. One of the critical aspects of designing these assistive devices is to improve functional independence while providing an excellent human–machine interface. People with limited upper limb function due to stroke, spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis, and other conditions find the controls of assistive devices such as power wheelchairs difficult to use. Thus, the objective of this research was to design a multimodal control method for robotic self-assistance that could assist individuals with disabilities in performing self-care tasks on a daily basis. In this research, a control framework for two interchangeable operating modes with a finger joystick and a chin joystick is developed where joysticks seamlessly control a wheelchair and a wheelchair-mounted robotic arm. Custom circuitry was developed to complete the control architecture. A user study was conducted to test the robotic system. Ten healthy individuals agreed to perform three tasks using both (chin and finger) joysticks for a total of six tasks with 10 repetitions each. The control method has been tested rigorously, maneuvering the robot at different velocities and under varying payload (1–3.5 lb) conditions. The absolute position accuracy was experimentally found to be approximately 5 mm. The round-trip delay we observed between the commands while controlling the xArm was 4 ms. Tests performed showed that the proposed control system allowed individuals to perform some ADLs such as picking up and placing items with a completion time of less than 1 min for each task and 100% success

Impedance learning adaptive super‐twisting control of a robotic exoskeleton for physical human‐robot interaction

Abstract This study addresses two issues about the interaction of the upper limb rehabilitation robot with individuals who have disabilities. The first step is to estimate the human's target position (also known as TPH). The second step is to develop a robust adaptive impedance control mechanism. A novel Non‐singular Terminal Sliding Mode Control combined with an adaptive super‐twisting controller is being developed to achieve this goal. This combination's purpose is to provide high reliability, continuous performance tracking of the system's trajectories. The proposed adaptive control strategy reduces matched dynamic uncertainty while also lowering chattering, which is the sliding mode's most glaring issue. The proposed TPH is coupled with adaptive impedance control with the use of a Radial Basis Function Neural Network, which allows a robotic exoskeleton to simply track the desired impedance model. To validate the approach in real‐time, an exoskeleton robot was deployed in controlled experimental circumstances. A comparison study has been set up to show how the adaptive impedance approach proposed is better than other traditional controllers