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

Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton

A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user’s intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems

On the dynamics of human locomotion and co-design of lower limb assistive devices

Recent developments in lower extremities wearable robotic devices for the assistance and rehabilitation of humans suffering from an impairment have led to several successes in the assistance of people who as a result regained a certain form of locomotive capability. Such devices are conventionally designed to be anthropomorphic. They follow the morphology of the human lower limbs. It has been shown previously that non-anthropomorphic designs can lead to increased comfort and better dynamical properties due to the fact that there is more morphological freedom in the design parameters of such a device. At the same time, exploitation of this freedom is not always intuitive and can be difficult to incorporate. In this work we strive towards a methodology aiding in the design of possible non-anthropomorphic structures for the task of human locomotion assistance by means of simulation and optimization. The simulation of such systems requires state of the art rigid body dynamics, contact dynamics and, importantly, closed loop dynamics. Through the course of our work, we first develop a novel, open and freely available, state of the art framework for the modeling and simulation of general coupled dynamical systems and show how such a framework enables the modeling of systems in a novel way. The resultant simulation environment is suitable for the evaluation of structural designs, with a specific focus on locomotion and wearable robots. To enable open-ended co-design of morphology and control, we employ population-based optimization methods to develop a novel Particle Swarm Optimization derivative specifically designed for the simultaneous optimization of solution structures (such as mechanical designs) as well as their continuous parameters. The optimizations that we aim to perform require large numbers of simulations to accommodate them and we develop another open and general framework to aid in large scale, population based optimizations in multi-user environments. Using the developed tools, we first explore the occurrence and underlying principles of natural human gait and apply our findings to the optimization of a bipedal gait of a humanoid robotic platform. Finally, we apply our developed methods to the co-design of a non-anthropomorphic, lower extremities, wearable robot in simulation, leading to an iterative co-design methodology aiding in the exploration of otherwise hard to realize morphological design

Control of robot-assisted gait trainer using hybrid proportional integral derivative and iterative learning control

An inexpensive exoskeleton of the lower limb was designed and developed in this study. It can be used as a gait trainer for persons with lower limb problems. It plays an essential role in lower limb rehabilitation and aid for patients, and it can help them improve their physical condition. This paper proposes a hybrid controller for regulating the lower limb exoskeleton of a robot-assisted gait trainer that uses a proportional integral and derivative (PID) controller combined with an iterative learning controller (ILC). The direct current motors at the hip and knee joints are controlled by a microcontroller that uses a preset pattern for the trajectories. It can learn how to monitor a trajectory. If the trajectory or load is changed, it will be able to follow the change. The experiment showed that the PID controller had the smallest overshoot, and settling time, and was responsible for system stability. Even if there are occasional interruptions, the tracking performance improves with the ILC

Projeto do sistema de controle de um exoesqueleto do membro inferior direito

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2017.Nesta pesquisa o modelo de um exoesqueleto do membro inferior direita para melhorar a mobilidade do usuário e seu sistema de controle foram desenvolvidos. O projeto físico do modelo do exoesqueleto consiste em três partes principais: um quadril e a parte superior e inferior da perna conectados um com o outro por juntas revolutas. Cada uma das juntas é atuado por um motor Brushless DC (BLDC) com caixa de redução para aumentar torque. Os motores a serem usados na construção possuem sensores de velocidade e de posição para fornecer os dados necessários para o sistema de controle. Solidworks Computer Aided Design (CAD) software é usado para desenvolver o modelo do exoesqueleto, que é salvo em formato extensible markup language (XML) para depois ser importado em Simmechanics, permitindo a integração de modelos de corpos físicos com componentes de Simulink. A cinemática inversa do exoesqueleto é desenvolvido e projetado em Very high speed integrated circuit Hardware Description Language (VHDL) usando aritmética em ponto flutuante para ser executado a partir de um dispositivo Field Programmable Gate Array (FPGA). Quatro representações diferentes do projeto de hardware do modelo cinematico do exoesqueleto foram desenvolvidos fazendo análise de erro com Mean Square Error (MSE) e Average Relative Error (ARE). Análise de trade-off de desempenho e área em FPGA é feito. A estratégia de controle Proportional-Integrative-Derivative (PID) é escolhido para desenvolver o sistema de controle do exoesqueleto por ser relativamente simples e eficiente para desenvolver e por ser amplamente usado em muitas áreas de aplicação. Duas estratégias de sistemas de controle combinado de posiçaõ e velocidade são desenvolvidos e comparados um com o outro. Cada sistema de controle consiste em dois controladores de velocidade e dois de posição. Os parâmetros PID são calculados usando os métodos de sintonização Ziegler-Nichols e Particle Swarm Optimization (PSO). PSO é um método de sintonização relativamente simples porém eficiente que é aplicado em muitos problemas de otimização. PSO é baseado no comportamento supostamente inteligente de cardumes de peixes e bandos de aves em procura de alimento. O algoritmo, junto com o método Ziegler-Nichols, é usado para achar parâmetros PID apropriados para os blocos de controle nas duas estratégias te controle desenvolvidos. A resposta do sistema de controle é avaliada, analisando a resposta a um step input. Simulação da marcha humana é também feito nos dois modelos de sistema de controle do exoesqueleto fornecendo dados de marcha humana ao modelo e analisando visualmente os movimentos do exoesqueleto em Simulink. Os dados para simulação da marcha humana são extraídos de uma base de dados existente e adaptados para fazer simulações nos modelos de sistema de controle do exoesqueleto.In this research a model of an exoskeleton of the right lower limb for user mobility enhancement and its control system are designed. The exoskeleton design consists of three major parts: a hip, an upper leg and a lower leg part, connected to one another with revolute joints. The joints will each be actuated by Brushless DC (BLDC) Motors equipped with gearboxes to increase torque. The motors are also equipped with velocity and position sensors which provide the necessary data for the designed control systems. Solidworks Computer Aided Design (CAD) software is used to develop a model of the exoskeleton which is then exported in extensible markup language (XML) format to be imported in Simmechanics, enabling the integration of physical body components with Simulink components. The inverse kinematics of the exoskeleton model is calculated and designed in Very high speed integrated circuit Hardware Description Language (VHDL) using floating-point numbers, to be executed from a Field Programmable Gate Array (FPGA) Device. Four different bit width representations of the hardware design of the kinematics model of the exoskeleton are developed, performing error analysis with the Mean Square Error (MSE) and the Average Relative Error (ARE) approaches. Trade-off analysis is then performed against performance and area on FPGA. The Proportional-Integrative-Derivative (PID) control strategy is chosen to develop the control system for the exoskeleton for its relatively simple design and proven efficient implementation in a very broad range of real life application areas. Two control system strategies are developed and compared to one another. Each control system design is comprised of two velocity- and two position controllers. PID parameters are calculated using the Ziegler-Nichols method and Particle Swarm Optimization (PSO). PSO is a relatively simple yet powerful optimization method that is applied in many optimization problem areas. It is based on the seemingly intelligent behaviour of fish schools and bird flocks in search of food. The algorithm, alongside the Ziegler-Nichols method, is used to find suitable PID parameters for control system blocks in the two designs. The system response of the control systems is evaluated analyzing step response. Human gait simulation is also performed on the developed exoskeleton control systems by observing the exoskeleton model movements in Simulink. The gait simulation data is extracted from a human gait database and adapted to be fed as input to the exoskeleton control system models

Autonomous motion and control of lower limb exoskeleton rehabilitation robot

Introduction: The lower limb exoskeleton rehabilitation robot should perform gait planning based on the patient’s motor intention and training status and provide multimodal and robust control schemes in the control strategy to enhance patient participation.Methods: This paper proposes an adaptive particle swarm optimization admittance control algorithm (APSOAC), which adaptively optimizes the weights and learning factors of the PSO algorithm to avoid the problem of particle swarm falling into local optimal points. The proposed improved adaptive particle swarm algorithm adjusts the stiffness and damping parameters of the admittance control online to reduce the interaction force between the patient and the robot and adaptively plans the patient’s desired gait profile. In addition, this study proposes a dual RBF neural network adaptive sliding mode controller (DRNNASMC) to track the gait profile, compensate for frictional forces and external perturbations generated in the human-robot interaction using the RBF network, calculate the required moments for each joint motor based on the lower limb exoskeleton dynamics model, and perform stability analysis based on the Lyapunov theory.Results and discussion: Finally, the efficiency of the APSOAC and DRNNASMC algorithms is demonstrated by active and passive walking experiments with three healthy subjects, respectively

MyoSim:Fast and physiologically realistic MuJoCo models for musculoskeletal and exoskeletal studies

Owing to the restrictions of live experimentation, musculoskeletal simulation models play a key role in biological motor control studies and investigations. Successful results of which are then tried on live subjects to develop treatments as well as robot aided rehabilitation procedures for addressing neuromusculoskeletal anomalies ranging from limb loss, to tendinitis, from sarcopenia to brain and spinal injuries. Despite its significance, current musculoskeletal models are computationally expensive, and provide limited support for contact-rich interactions which are essential for studying motor behaviors in activities of daily living, during rehabilitation treatments, or in assistive robotic devices. To bridge this gap, this work proposes an automatic pipeline to generate physiologically accurate musculoskeletal, as well as hybrid musculoskeletal-exoskeletal models. Leveraging this pipeline we present MyoSim - a set of computationally efficient (over 2 orders of magnitude faster than state of the art) musculoskeletal models that support fully interactive contact rich simulation. We further extend MyoSim to support additional features that help simulate various real-life changes/diseases, such as muscle fatigue, and sarcopenia. To demonstrate the potential applications, several use cases, including interactive rehabilitation movements, tendon-reaffirmation, and the cosimulation with an exoskeleton, were developed and investigated for physiological correctness. Web-page: https://sites.google.com/view/myosuit