Reflex Controller Research Articles

Robustifying a reinforcement learning agent-based bionic reflex controller through an adaptive sliding mode control

Robustifying a reinforcement learning agent-based bionic reflex controller through an adaptive sliding mode control

Evaluation of controllers for augmentative hip exoskeletons and their effects on metabolic cost of walking: explicit versus implicit synchronization.

Background: Efficient gait assistance by augmentative exoskeletons depends on reliable control strategies. While numerous control methods and their effects on the metabolic cost of walking have been explored in the literature, the use of different exoskeletons and dissimilar protocols limit direct comparisons. In this article, we present and compare two controllers for hip exoskeletons with different synchronization paradigms. Methods: The implicit-synchronization-based approach, termed the Simple Reflex Controller (SRC), determines the assistance as a function of the relative loading of the feet, resulting in an emerging torque profile continuously assisting extension during stance and flexion during swing. On the other hand, the Hip-Phase-based Torque profile controller (HPT) uses explicit synchronization and estimates the gait cycle percentage based on the hip angle, applying a predefined torque profile consisting of two shorter bursts of assistance during stance and swing. We tested the controllers with 23 naïve healthy participants walking on a treadmill at 4km ⋅ h-1, without any substantial familiarization. Results: Both controllers significantly reduced the metabolic rate compared to walking with the exoskeleton in passive mode, by 18.0% (SRC, p < 0.001) and 11.6% (HPT, p < 0.001). However, only the SRC led to a significant reduction compared to walking without the exoskeleton (8.8%, p = 0.004). The SRC also provided more mechanical power and led to bigger changes in the hip joint kinematics and walking cadence. Our analysis of mechanical powers based on a whole-body analysis suggested a reduce in ankle push-off under this controller. There was a strong correlation (Pearson's r = 0.778, p < 0.001) between the metabolic savings achieved by each participant with the two controllers. Conclusion: The extended assistance duration provided by the implicitly synchronized SRC enabled greater metabolic reductions compared to the more targeted assistance of the explicitly synchronized HPT. Despite the different assistance profiles and metabolic outcomes, the correlation between the metabolic reductions with the two controllers suggests a difference in individual responsiveness to assistance, prompting more investigations to explore the person-specific factors affecting assistance receptivity.

An adaptive reflexive control strategy for walking assistance system based on functional electrical stimulation.

Functional electrical stimulation (FES) neuroprostheses have been regarded as an effective approach for gait rehabilitation and assisting patients with stroke or spinal cord injuries. A multiple-channel FES system was developed to improve the assistance and restoration of lower limbs. However, most neuroprostheses need to be manually adjusted and cannot adapt to individual needs. This study aimed to integrate the purely reflexive FES controller with an iterative learning algorithm while a multiple-channel FES walking assistance system based on an adaptive reflexive control strategy has been established. A real-time gait phase detection system was developed for accurate gait phase detection and stimulation feedback. The reflexive controller generated stimulation sequences induced by the gait events. These stimulation sequences were updated for the next gait cycle through the difference between the current and previous five gait cycles. Ten healthy young adults were enrolled to validate the multiple-channel FES system by comparing participants' gait performance to those with no FES controller and purely reflexive controller. The results showed that the proposed adaptive FES controller enabled the adaption to generate fitted stimulation sequences for each participant during various treadmill walking speeds. The maximum, minimum, and range of motion (ROM) of the hip, knee, and ankle joints were furtherly improved for most participants, especially for the hip and knee flexion and ankle dorsiflexion compared with the purely reflexive FES control strategy. The presented system has the potential to enhance motor relearning and promote neural plasticity.

Choosing appropriate prosthetic ankle work to reduce the metabolic cost of individuals with transtibial amputation

Powered ankle prostheses have been designed to reduce the energetic burden that individuals with transtibial amputation experience during ambulation. There is an open question regarding how much power the prosthesis should provide, and whether approximating biological ankle kinetics is optimal to reduce the metabolic cost of users. We tested 10 individuals with transtibial amputation walking on a treadmill wearing the BiOM powered ankle prosthesis programmed with 6 different power settings (0–100%), including a prosthetist-chosen setting, chosen to approximate biological ankle kinetics. We measured subjects’ metabolic cost of transport (COT) and the BiOM’s net ankle work during each condition. Across participants, power settings greater than 50% resulted in lower COT than 0% or 25%. The relationship between power setting, COT, and net ankle work varied considerably between subjects, possibly due to individual adaptation and exploitation of the BiOM’s reflexive controller. For all subjects, the best tested power setting was higher than the prosthetist-chosen setting, resulting in a statistically significant and meaningful difference in COT between the best tested and prosthetist-chosen power settings. The results of this study demonstrate that individuals with transtibial amputation may benefit from prescribed prosthetic ankle push-off work that exceeds biological norms.

Human-Inspired Reflex to Autonomously Prevent Slip of Grasped Objects Rotated with a Prosthetic Hand.

Autonomously preventing grasped objects from slipping out of prosthetic hands is an important feature for limb-absent people since they cannot directly feel the grip force applied to grasped objects. Oftentimes, a satisfactory grip force in one situation will be inadequate in different situations, such as when the object is rotated or transported. Over time, people develop a grip reflex to prevent slip of grasped objects when they are rotated with respect to gravity by their natural hands. However, this reflexive trait is absent in commercially available prosthetic hands. This paper explores a human-inspired grasp reflex controller for prosthetic hands to prevent slip of objects when they are rotated. This novel human-inspired grasped object slip prevention controller is evaluated with 6 different objects in benchtop tests and by 12 able-bodied subjects during human experiments replicating realistic tasks of daily life. An analysis of variance showed highly significant improvement in the number of successfully completed cycles for both the benchtop and human tests when the slip prevention reflex was active. An object sorting task, which was designed to serve as a cognitive distraction for the human subjects while controlling the prosthetic hand, had a significant impact on many of the performance metrics. However, assistance from the novel slip prevention reflex mitigated the effects of the distraction, offering an effective method for reducing both object slip and the required cognitive load from the prosthetic hand user.

Simple Reflex Controller for Decentralized Motor Coordination Based on Resonant Oscillation

This article describes an extremely simple controller as a minimal example of decentralized motor coordination and gait generation. The control strategy is based on the stretch reflex in animals and requires no mutual communication or detailed body models. Despite such simplicity, each controller can sync itself and generate various resonant oscillation by only physical interaction through whole body dynamics. To evaluate this controller, we conduct some simulations with a linear spring–mass–damper system and a nonlinear legged robot model with multiple controllers. The former shows an adaptability to change in vibration frequency and the body parameter. In the latter, first we show a limitation of the proposed method due to the nonlinearity, and an alternative method is proposed. Finally, the simple controllers generate versatile gaits just by choosing a control parameter of “speeding up or down,” and the gait generation can be explained by the controllers–body integration based on resonant oscillation.

Instability detection of the tire-road contact and torque control for wheel-individual driven vehicles

Abstract Traction control for vehicles with wheel individual drives is a challenging task, since the wheel slips are unknown when each wheel transfers a torque. This contribution presents an approach which deals with this problem especially for electrical vehicles, where the drive torques can be changed very fast in comparison to vehicles with combustion engines. The algorithm is based on two filters. The first filter is an adaptive Kalman filter which is used to detect fast changes in the stability conditions of the wheels. The filter also delivers a valuation for the torque to which the drive torque has to be reduced to get the wheel stable again. The second filter estimates the wheel slip. It is based on a simplified expression of the slip dynamics in combination with a special reset strategy for low slip situations. This filter allows detection of slow changes in the stability conditions. All filters are wheel individual and use only standard sensor information. No tire model is needed for the filters to work. A simple reflexive traction controller, which is based on a state machine, computes the drive torques in dependence on the stability criteria based on the filter outputs.

Wavelet Transformation-Based Fuzzy Reflex Control for Prosthetic Hands to Prevent Slip

A wavelet transformation-based fuzzy reflex control method is proposed to prevent slip and maintain stable grasping posture for prosthetic hands. A key component of the control method is that the response time from slip detection to slip prevention is very short. The proposed control method mainly includes slip detection, reflex grasping force estimation, and use of a reflex controller. In the slip detection, initial slip information can be rapidly obtained via wavelet transformation as soon as disturbances cause the grasped object to slip. This shortens the response time of the reflex control. The initial slip information can then trigger the reflex control. In the grasping estimation, a reflex force estimation model is presented to estimate a force increment that is used to adjust to the desired force. In the reflex controller, a fuzzy logic controller is adopted to track the desired force. Finally, the proposed control method is applied to control a prosthetic hand with a single degree of freedom. The experimental results show that the proposed control method can adjust the grasping force and effectively prevent slip under disturbances. The time of slip prevention is less than 160 ms.

A sensory-driven controller for quadruped locomotion

Locomotion of quadruped robots has not yet achieved the harmony, flexibility, efficiency and robustness of its biological counterparts. Biological research showed that spinal reflexes are crucial for a successful locomotion in the most varied terrains. In this context, the development of bio-inspired controllers seems to be a good way to move toward an efficient and robust robotic locomotion, by mimicking their biological counterparts. This contribution presents a sensory-driven controller designed for the simulated Oncilla quadruped robot. In the proposed reflex controller, movement is generated through the robot's interactions with the environment, and therefore, the controller is solely dependent on sensory information. The results show that the reflex controller is capable of producing stable quadruped locomotion with a regular stepping pattern. Furthermore, it is capable of dealing with slopes without changing the parameters and with small obstacles, overcoming them successfully. Finally, system robustness was verified by adding noise to sensors and actuators and also delays.

A Reflexive Neural Network for Dynamic Biped Walking Control

Biped walking remains a difficult problem, and robot models can greatly facilitate our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network governed mainly by local sensor signals. Building on earlier work (Taga, 1995; Cruse, Kindermann, Schumm, Dean, & Schmitz, 1998), this study shows that human-like gaits emerge without specific position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (anterior extreme angle and ground contact), which operate at the interjoint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e.g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motor neurons in our reflexive controller are directly driving the motors of the joints rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuromechanical system, and this study emphasizes that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using a real robot as well as by a Poincaré map analysis applied on a model of the robot in order to assess its stability.

A Reflexive Neural Network for Dynamic Biped Walking Control

Biped walking remains a difficult problem, and robot models can greatly facilitate our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network governed mainly by local sensor signals. Building on earlier work (Taga, 1995; Cruse, Kindermann, Schumm, Dean, &amp; Schmitz, 1998), this study shows that human-like gaits emerge without specific position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (anterior extreme angle and ground contact), which operate at the interjoint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e.g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motor neurons in our reflexive controller are directly driving the motors of the joints rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuromechanical system, and this study emphasizes that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using a real robot as well as by a Poincaré map analysis applied on a model of the robot in order to assess its stability.

Are the respiratory responses to changes in ventilatory assist optimized?

The input-output relationship of the CO2 respiratory controller can be described in two different ways. The first method is to describe the output as a function of the inputs; this type of controller model may be called a reflex controller model. The other method is to describe the relationship by an operating principle. The operating principle is often the minimization of a certain criterion or parameter; in this case, the controller is called an optimal controller. Several investigators have proposed that the respiratory controller responds to various stimuli in order to minimize certain criteria, which represent the energetic cost of breathing (3,4,9,13). In the concept proposed by Poon (9), the maintenance of arterial blood gas tensions and mechanical work are competing priorities for the respiratory controller, and the controller functions to minimize the net operating cost of both work and deviations of the blood gas tensions from given set points.