Observer Sliding Mode Control Design for lower Exoskeleton system: Rehabilitation Case

Sliding mode (SM) has been selected as the controlling technique, and the state observer (SO) design is used as a component of active disturbance rejection control (ADRC) to reduce the knee position trajectory for therapeutic purposes. The suggested controller will improve the needed position performances for the Exoskeleton system when compared to the proportional-derivative controller (PD) and SMC as feed-forward in the ADRC approach, as shown theoretically and through computer simulations. Simulink tool is used in this comparison to analyze the nominal case and several disruption cases. The results of mathematical modeling and simulation studies demonstrated that SMC with a disturbance observer strategy performs better than the PD control system and SMC in feed-forward with a greater capacity to reject disturbances and significantly better than these controllers. Performance indices are used for numerical comparison to demonstrate the superiority of these controllers

Anti-Disturbance Compensation-Based Nonlinear Control for a Class of MIMO Uncertain Nonlinear Systems

Multi-Inputs-Multi-Outputs (MIMO) systems are recognized mainly in industrial applications with both input and state couplings, and uncertainties. The essential principle to deal with such difficulties is to eliminate the input couplings, then estimate the remaining issues in real-time, followed by an elimination process from the input channels. These difficulties are resolved in this research paper, where a decentralized control scheme is suggested using an Improved Active Disturbance Rejection Control (IADRC) configuration. A theoretical analysis using a state-space eigenvalue test followed by numerical simulations on a general uncertain nonlinear highly coupled MIMO system validated the effectiveness of the proposed control scheme in controlling such MIMO systems. Time-domain comparisons with the Conventional Active Disturbance Rejection Control (CADRC)-based decentralizing control scheme are also included

A Nonlinear Optimal Control Approach for a Lower-Limb Robotic Exoskeleton

The use of robotic limb exoskeletons is growing fast either for rehabilitation purposes or in an aim to enhance human ability for lifting heavy objects or for walking for long distances without fatigue. The paper proposes a nonlinear optimal control approach for a lower-limb robotic exoskeleton. The method has been successfully tested so far on the control problem of several types of robotic manipulators and this paper shows that it can also provide an optimal solution to the control problem of limb robotic exoskeletons. To implement this control scheme, the state-space model of the lower-limb robotic exoskeleton undergoes first approximate linearization around a temporary operating point, through first-order Taylor series expansion and through the computation of the associated Jacobian matrices. To select the feedback gains of the H-infinity controller an algebraic Riccati equation is solved at each time-step of the control method. The global stability properties of the control loop are proven through Lyapunov analysis. Finally, to implement state estimation-based feedback control, the H-infinity Kalman Filter is used as a robust state estimator

Application of wearable sensors in actuation and control of powered ankle exoskeletons: a Comprehensive Review

Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human–machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user’s locomotion intention and requirement, whereas machine–machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment—machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided

Control Methods for Compensation and Inhibition of Muscle Fatigue in Neuroprosthetic Devices

For individuals that suffer from paraplegia activities of daily life are greatly inhibited. With over 5,000 new cases of paraplegia each year in the United States alone there is a clear need to develop technologies to restore lower extremity function to these individuals. One method that has shown promise for restoring functional movement to paralyzed limbs is the use of functional electrical stimulation (FES), which is the application of electrical stimulation to produce a muscle contraction and create a functional movement. This technique has been shown to be able to restore numerous motor functions in persons with disability; however, the application of the electrical stimulation can cause rapid muscle fatigue, limiting the duration that these devices may be used. As an alternative some research has developed fully actuated orthoses to restore motor function via electric motors. These devices have been shown to be capable of achieving greater walking durations than FES systems; however, these systems can be significantly larger and heavier. To develop smaller and more efficient systems some research has explored hybrid neuroprostheses that use both FES and electric motors. However, these hybrid systems present new research challenges. In this dissertation novel control methods to compensate/inhibit muscle fatigue in neuroprosthetic and hybrid neuroprosthetic devices are developed. Some of these methods seek to compensate for the effects of fatigue by using fatigue dynamics in the control development or by minimizing the amount of stimulation used to produce a desired movement. Other control methods presented here seek to inhibit the effects of muscle fatigue by adding an electric motor as additional actuation. These control methods use either switching or cooperative control of FES and an electric motor to achieve longer durations of use than systems that strictly use FES. Finally, the necessity for the continued study of hybrid gait restoration systems is facilitated through simulations of walking with a hybrid neuroprosthesis. The results of these simulations demonstrate the potential for hybrid neuroprosthesis gait restoration devices to be more efficient and achieve greater walking durations than systems that use strictly FES or strictly electric motors

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017–2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed

Prescribed Performance Function Based Sliding Mode Control of Opposing Pneumatic Artificial Muscles to Enhance Safety

The field of rehabilitation robotics has seen a significant increase in the utilization of Pneumatic Artificial Muscle (PAM)-based systems in recent years. These systems have demonstrated great potential in assisting and enhancing human movements and motor functions. However, as with any system that involves human interaction, safety is of the utmost importance. It is essential to ensure that the tracking error is kept within a safe range to prevent harm to people and equipment. This research proposes a control strategy that combines the exponential reaching law with a prescribed performance function to enhance safety in PAM-based rehabilitation robots. The prescribed performance function is designed to regulate the tracking error within predetermined limits during short and long-term operations, thereby mitigating large oscillations that may damage mechanical structures and patients. The experimental results indicate that the proposed controller demonstrated superior tracking accuracy and safety performance compared to traditional control methods. It is hoped that the findings of this study will contribute to developing safe and effective rehabilitation systems for patients in need

Adaptive control for wearable robots in human-centered rehabilitation tasks

Robotic rehabilitation therapies have been improving by providing the needed assistance to the patient, in a human-centered environment, and also helping the therapist to choose the necessary procedure. This thesis presents an adaptive "Assistance-as-needed" strategy which adheres to the specific needs of the patient and with the inputs from the therapist, whenever needed. The exertion of assistive and responsive behavior of the lower limb wearable robot is dedicated for the rehabilitation of incomplete spinal cord injury (SCI) patients. The main objective is to propose and evaluate an adaptive control model on a wearable robot, assisting the user and adhering to their needs, with no or less combination of external devices. The adaptation must be more interactive to understand the user needs and their volitional orders. Similarly, by using the existing muscular strength, in incomplete SCI patients, as a motivation to pursue the movement and assist them, only when needed. The adaptive behavior of the wearable robot is proposed by monitoring the interaction and movement of the user. This adaptation is achieved by modulating the stiffness of the exoskeleton in function of joint parameters, such as positions and interaction torques. These joint parameters are measured from the user independently and then used to update the new stiffness value. The adaptive algorithm performs with no need of external sensors, making it simple in terms of usage. In terms of rehabilitation, it is also desirable to be compatible with combination of assistive devices such as muscle stimulation, neural activity (BMI) and body balance (Wii), to deliver a user friendly and effective therapy. Combination of two control approaches has been employed, to improve the efficiency of the adaptive control model and was evaluated using a wearable lower limb exoskeleton device, H1. The control approaches, Hierarchical and Task based approach have been used to assist the patient as needed and simultaneously motivate the patient to pursue the therapy. Hierarchical approach facilitates combination of multiple devices to deliver an effective therapy by categorizing the control architecture in two layers, Low level and High level control. Task-based approaches engage in each task individually and allow the possibility to combine them at any point of time. It is also necessary to provide an interaction based approach to ensure the complete involvement of the user and for an effective therapy. By means of this dissertation, a task based adaptive control is proposed, in function of human-orthosis interaction, which is applied on a hierarchical control scheme. This control scheme is employed in a wearable robot, with the intention to be applied or accommodated to different pathologies, with its adaptive capabilities. The adaptive control model for gait assistance provides a comprehensive solution through a single implementation: Adaptation inside a gait cycle, continuous support through gait training and in real time. The performance of this control model has been evaluated with healthy subjects, as a preliminary study, and with paraplegic patients. Results of the healthy subjects showed a significant change in the pattern of the interaction torques, elucidating a change in the effort and adaptation to the user movement. In case of patients, the adaptation showed a significant improvement in the joint performance (flexion/extension range) and change in interaction torques. The change in interaction torques (positive to negative) reflects the active participation of the patient, which also explained the adaptive performance. The patients also reported that the movement of the exoskeleton is flexible and the walking patterns were similar to their own distinct patterns. The presented work is performed as part of the project HYPER, funded by Ministerio de Ciencia y Innovación, Spain. (CSD2009 - 00067 CONSOLIDER INGENIOLas terapias de rehabilitación robóticas han sido mejoradas gracias a la inclusión de la asistencia bajo demanda, adaptada a las variaciones de las necesidades del paciente, así como a la inclusión de la ayuda al terapeuta en la elección del procedimiento necesario. Esta tesis presenta una estrategia adaptativa de asistencia bajo demanda, la cual se ajusta a las necesidades específicas del paciente junto a las aportaciones del terapeuta siempre que sea necesario. El esfuerzo del comportamiento asistencial y receptivo del robot personal portátil para extremidades inferiores está dedicado a la rehabilitación de pacientes con lesión de la médula espinal (LME) incompleta. El objetivo principal es proponer y evaluar un modelo de control adaptativo en un robot portátil, ayudando al usuario y cumpliendo con sus necesidades, en ausencia o con reducción de dispositivos externos. La adaptación debe ser más interactiva para entender las necesidades del usuario y sus intenciones u órdenes volitivas. De modo similar, usando la fuerza muscular existente (en pacientes con LME incompleta) como motivación para lograr el movimiento y asistirles solo cuando sea necesario. El comportamiento adaptativo del robot portátil se propone mediante la monitorización de la interacción y movimiento del usuario. Esta adaptación conjunta se consigue modulando la rigidez en función de los parámetros de la articulación, tales como posiciones y pares de torsión. Dichos parámetros se miden del usuario de forma independiente y posteriormente se usan para actualizar el nuevo valor de la rigidez. El desempeño del algoritmo adaptativo no requiere de sensores externos, lo que favorece la simplicidad de su uso. Para una adecuada rehabilitación, efectiva y accesible para el usuario, es necesaria la compatibilidad con diversos mecanismos de asistencia tales como estimulación muscular, actividad neuronal y equilibrio corporal. Para mejorar la eficiencia del modelo de control adaptativo se ha empleado una combinación de dos enfoques de control, y para su evaluación se ha utilizado un exoesqueleto robótico H1. Los enfoques de control Jerárquico y de Tarea se han utilizado para ayudar al usuario según sea necesario, y al mismo tiempo motivarle para continuar el tratamiento. Enfoque jerárquico facilita la combinación de múltiples dispositivos para ofrecer un tratamiento eficaz mediante la categorización de la arquitectura de control en dos niveles : el control de bajo nivel y de alto nivel. Los enfoques basados en tareas involucran a la persona en cada tarea individual, y ofrecen la posibilidad de combinarlas en cualquier momento. También es necesario proporcionar un enfoque basado en la interacción con el usuario, para asegurar su participación y lograr así una terapia eficaz. Mediante esta tesis, proponemos un control adaptativo basado en tareas y en función de la interacción persona-ortesis, que se aplica en un esquema de control jerárquico. Este esquema de control se emplea en un robot portátil, con la intención de ser aplicado o acomodado a diferentes patologías, con sus capacidades de adaptación. El modelo de control adaptativo propuesto proporciona una solución integral a través de una única aplicación: adaptación dentro de la marcha y apoyo continúo a través de ejercicios de movilidad en tiempo real. El rendimiento del modelo se ha evaluado en sujetos sanos según un estudio preliminar, y posteriormente también en pacientes parapléjicos. Los resultados en sujetos sanos mostraron un cambio significativo en el patrón de los pares de interacción, elucidando un cambio en la energía y la adaptación al movimiento del usuario. En el caso de los pacientes, la adaptación mostró una mejora significativa en la actuación conjunta (rango de flexión / extensión) y el cambio en pares de interacción. El cambio activo en pares de interacción (positivo a negativo) refleja la participación activa del paciente, lo que también explica el comportamiento adaptativo

Trajectory Planning and Subject-Specific Control of a Stroke Rehabilitation Robot using Deep Reinforcement Learning

There are approximately 13 million annual new stroke cases worldwide. Research has shown that robotics can provide practical and efficient solutions for expediting post-stroke patient recovery. Assistive robots provide automatic limb training, which saves a great deal of time and energy. In addition, they facilitate the use of data acquisition devices. The data is beneficial in terms of quantitative evaluation of the patient progress. This research focused on the trajectory planning and subject-specific control of an upper-extremity post-stroke rehabilitation robot. To find the optimal rehabilitation practice, the manipulation trajectory was designed by an optimization-based planner. A linear quadratic regulator (LQR) controller was then applied to stabilize the trajectory. The integrated planner-controller framework was tested in simulation. To validate the simulation results, hardware implementation was conducted, which provided good agreement with simulation. One of the challenges of rehabilitation robotics is the choice of the low-level controller. To find the best candidate for our specific setup, five controllers were evaluated in simulation for circular trajectory tracking. In particular, we compared the performance of LQR, sliding mode control (SMC), and nonlinear model predictive control (NMPC) to conventional proportional integral derivative (PID) and computed-torque PID controllers. The real-time assessment of the mentioned controllers was done by implementing them on the physical hardware for point stabilization and circular trajectory tracking scenarios. Our comparative study confirmed the need for advanced low-level controllers for better performance. Due to complex online optimization of the NMPC and the incorporated delay in the method of implementation, performance degradation was observed with NMPC compared to other advanced controllers. The evaluation showed that SMC and LQR were the two best candidates for the robot. To remove the need for extensive manual controller tuning, a deep reinforcement learning (DRL) tuner framework was designed in MATLAB to provide the optimal weights for the controllers; it permitted the online tuning of the weights, which enabled the subject-specific controller weight adjustment. This tuner was tested in simulation by adding a random noise to the input at each iteration, to simulate the subject. Compared to fixed manually tuned weights, the DRL-tuned controller presented lower position-error. In addition, an easy to implement high-level force controller algorithm was designed by incorporating the subject force data. The resulting hybrid position/force controller was tested with a healthy subject in the loop. The controller was able to provide assist as needed when the subject increased the position-error. Future research might consider model reduction methods for expediting the NMPC optimization, application of the DRL on other controllers and for optimization parameter adjustment, testing other high-level controllers like admittance control, and testing the final controllers with post-stroke patients

Simulation And Control At the Boundaries Between Humans And Assistive Robots

Human-machine interaction has become an important area of research as progress is made in the fields of rehabilitation robotics, powered prostheses, and advanced exercise machines. Adding to the advances in this area, a novel controller for a powered transfemoral prosthesis is introduced that requires limited tuning and explicitly considers energy regeneration. Results from a trial conducted with an individual with an amputation show self-powering operation for the prosthesis while concurrently attaining basic gait fidelity across varied walking speeds. Experience in prosthesis development revealed that, though every effort is made to ensure the safety of the human subject, limited testing of such devices prior to human trials can be completed in the current research environment. Two complementary alternatives are developed to fill that gap. First, the feasibility of implementing impulse-momentum sliding mode control on a robot that can physically replace a human with a transfemoral amputation to emulate weight-bearing for initial prototype walking tests is established. Second, a more general human simulation approach is proposed that can be used in any of the aforementioned human-machine interaction fields. Seeking this general human simulation method, a unique pair of solutions for simulating a Hill muscle-actuated linkage system is formulated. These include using the Lyapunov-based backstepping control method to generate a closed-loop tracking simulation and, motivated by limitations observed in backstepping, an optimal control solver based on differential flatness and sum of squares polynomials in support of receding horizon controlled (e.g. model predictive control) or open-loop simulations. v The backstepping framework provides insight into muscle redundancy resolution. The optimal control framework uses this insight to produce a computationally efficient approach to musculoskeletal system modeling. A simulation of a human arm is evaluated in both structures. Strong tracking performance is achieved in the backstepping case. An exercise optimization application using the optimal control solver showcases the computational benefits of the solver and reveals the feasibility of finding trajectories for human-exercise machine interaction that can isolate a muscle of interest for strengthening