Feedback-error learning for gait rehabilitation using a powered knee orthosis: first advances

Powered assistive devices have been playing a major role in gait rehabilitation. Hereby, the development of time-effective control strategies to manage such devices is a key concern to rehabilitation engineering. This paper presents a real-time Feedback-Error Learning control strategy, by means of an Artificial Neural Network as a feedforward controller to acquire the inverse model of the plant, and a Proportional-Integral-Derivative feedback controller to guarantee stability and handle with disturbances. A Powered Knee Orthosis was used as the assistive device and a trajectory generator assistive strategy, previously acquired through an inertial system, was applied. A validation with one subject walking in a treadmill at 1 km/h with the Powered Knee Orthosis controlled by the Feedback-Error Learning control was performed. Evidences on the control behavior presented good performances, with the Artificial Neural Network taking 90 seconds to learn the inverse model, which enabled a decrease in the angular position error by 75% and eliminated the phase delay, when compared to solo Proportional-Integral-Derivative feedback controller. Robust reactions to external disturbances were also achieved. The implemented Feedback-Error Learning strategy proves to be a time-effective asset to control assistive powered devices.This work has been supported in part by the Fundacao para a Ciencia e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015, and part by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs -Controlo Inteligente de um Sistema Ortotico Ativo e Autonomo-under Grant NORTE-01-0145-FEDER-030386, and by the FEDER Funds through the COMPETE 2020-Programa Operacional Competitividade e Internacionalizacao (POCI)-with the Reference Project under Grant POCI-01-0145-FEDER-006941 and supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Feedback-error learning control for powered assistive devices

Active orthoses (AOs) are becoming relevant for user-oriented training in gait rehabilitation. This implies efficient responses of AO's low-level controllers with short time modeling for medical applications. This thesis investigates, in an innovative way, the performance of Feedback-Error Learning (FEL) control to time-effectively adapt the AOs' responses to user-oriented trajectories and changes in the dynamics due to the interaction with the user. FEL control comprises a feedback PID controller and a neural network feedforward controller to promptly learn the inverse dynamics of two AOs. It was carried out experiments with able-bodied subjects walking on a treadmill and considering external disturbances to user-AO interaction. Results showed that the FEL control effectively tracked the user-oriented trajectory with position errors between 5% to 7%, and with a mean delay lower than 25 ms. Compared to a single PID control, the FEL control decreased by 16.5% and 90.7% the position error and delay, respectively. Moreover, the feedforward controller was able to learn the inverse dynamics of the two AOs and adapt to variations in the user-oriented trajectories, such as speed and angular range, while the feedback controller compensated for random disturbances. FEL demonstrated to be an efficient low-level controller for controlling AOs during gait rehabilitation.This work has been supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015, and part by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs - Controlo Inteligente de um Sistema Ortótico Ativo e Autónomo - under Grant NORTE-01-0145-FEDER-030386, and by the FEDER Funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

Optimized PID-Like Neural Network Controller for Single-Objective Systems

The utilization of intelligent controllers becomes more prevalent as the hype of Industry 4.0 arises. Artificial neural network (ANN) exhibits the mapping ability and can estimate the output by means of either interpolation or extrapolation. These properties are sought to supersede the classical controllers. In this study, the ANN establishment was initiated by collecting dataset from the input and output of a well-known PID controller. The dataset was trained using a set of control factor combinations, including the number of neurons, the number of hidden layers, activation functions, and learning rates. Two kinds of ANN controllers were investigated, including one-input and three-input ANN. The testing was conducted under normal and uncertain conditions. These uncertainties include external disturbances, plant variations, and setpoint variations. The integral absolute error (IAE) was selected as the single objective to assess. The simulation results show that the response of three-input ANN controllers could yield smaller IAE at their best combinations under most kinds of conditions. Besides, the three-input ANN outperforms the one-input ANN both qualitatively and quantitatively. These facts might lead to a broader utilization of ANN as controllers

Design and implementation of adaptive NeuralPID for non linear dynamics in mobile robots

In this work, it will be reported original results concerning the application of PID Adaptive Neural controller in mobile robot in trajectory tracking control. In this control strategy the exact dynamical model of the robot will not need to be known and identified. To implement this strategy, two controllers are implemented separately: a kinematic controller and an adaptive neural PID controller. The uncertainty and dynamics variations in the robot dynamic are compensated by an adaptive neural PID controller. The resulting adaptive neural PID controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance. The stability of the proposed technique (based on Lyapunov’s theory) was demonstrated. Finally, experiments on a mobile robot have been developed to show the performance of the proposed technique, including the comparison with other controllers.Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina. Universidad Nacional de San Juan; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina. Universidad Nacional de San Juan; Argentin

Fair bandwidth allocation algorithm for PONS based on network utility maximization

Network utility maximization (NUM) models have been successfully applied to address multiple resource- allocation problems in communication networks. This paper explores, for the first time to our knowledge, their application to modeling the bandwidth-allocation problem in passive optical networks (PONs) and long-reach PONs. Using the NUM model, we propose the FEx-DBA (fair excess-dynamic bandwidth allocation) algorithm, a new DBA scheme to allow a fair and efficient allocation of the upstream channel capacity. The NUM framework provides the mathematical support to formally define the fairness concept in the resource allocation and the guidelines to devise FEx-DBA. A simulation study is conducted, whereby FEx-DBA is compared to a state-of-the-art proposal. We show that FEx-DBA (i) provides bandwidth guarantees to the users according to the service level agreement (SLA) contracted and fairly distributes the excess bandwidths among them; (ii) has a stable response and fast convergence when traffic or SLAs change, avoiding the oscillations appearing in other proposals; (iii) improves average delay and jitter measures; and (iv) only depends on a reduced set of parameters, which can be easily tuned.This work has been funded by Spanish Ministry of Science and Innovation (TEC2014-53071-C3-2-P and TEC2015-71932-REDT)

PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks

Producción CientíficaIn this paper, a proportional-integral-derivative (PID) controller integrated with a neural network (NN) is proposed to ensure quality of service (QoS) bandwidth requirements in passive optical networks (PONs). To the best of our knowledge, this is the first time an approach that implements a NN to tune a PID to deal with QoS in PONs is used. In contrast to other tuning techniques such as Ziegler-Nichols or genetic algorithms (GA), our proposal allows a real-time adjustment of the tuning parameters according to the network conditions. Thus, the new algorithm provides an online control of the tuning process unlike the ZN and GA techniques, whose tuning parameters are calculated offline. The algorithm, called neural network service level PID (NNSPID), guarantees minimum bandwidth levels to users depending on their service level agreement, and it is compared with a tuning technique based on genetic algorithms (GASPID). The simulation study demonstrates that NN-SPID continuously adapts the tuning parameters, achieving lower fluctuations than GA-SPID in the allocation process. As a consequence, it provides a more stable response than GA-SPID since it needs to launch the GA to obtain new tuning values. Furthermore, NN-SPID guarantees the minimum bandwidth levels faster than GA-SPID. Finally, NN-SPID is more robust than GA-SPID under real-time changes of the guaranteed bandwidth levels, as GA-SPID shows high fluctuations in the allocated bandwidth, especially just after any change is made.Ministerio de Ciencia e Innovación (Projects TEC2014-53071-C3-2-P and TEC2015-71932-REDT