scholarly journals Combined Feedback Feedforward Control of a 3-Link Musculoskeletal System Based on the Iterative Training Method

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Amin Valizadeh ◽  
Ali Akbar Akbari
Keyword(s):  
Feedforward Control ◽  
Humanoid Robots ◽  
Task Space ◽  
Forgetting Factor ◽  
Human Arm ◽  
Wide Range ◽  
Controller Performance ◽  
Human Motor ◽  
Musculoskeletal Systems ◽  
Investigation And Study

The investigation and study of the limbs, especially the human arm, have inspired a wide range of humanoid robots, such as movement and muscle redundancy, as a human motor system. One of the main issues related to musculoskeletal systems is the joint redundancy that causes no unique answer for each angle in return for an arm’s end effector’s arbitrary trajectory. As a result, there are many architectures like the torques applied to the joints. In this study, an iterative learning controller was applied to control the 3-link musculoskeletal system’s motion with 6 muscles. In this controller, the robot’s task space was assumed as the feedforward of the controller and muscle space as the controller feedback. In both task and muscle spaces, some noises cause the system to be unstable, so a forgetting factor was used to a convergence task space output in the neighborhood of the desired trajectories. The results show that the controller performance has improved gradually by iterating the learning steps, and the error rate has decreased so that the trajectory passed by the end effector has practically matched the desired trajectory after 1000 iterations.

Robust stabilization control of a spatial inverted pendulum using integral sliding mode controller

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ishan Chawla ◽  
Vikram Chopra ◽  
Ashish Singla
Keyword(s):  
Inverted Pendulum ◽  
Sliding Mode ◽  
Robust Stabilization ◽  
Linear Quadratic Regulator ◽  
Humanoid Robots ◽  
Sliding Mode Controller ◽  
Linear Quadratic ◽  
Integral Sliding Mode ◽  
Wide Range ◽  
Lqr Controller

AbstractFrom the last few decades, inverted pendulums have become a benchmark problem in dynamics and control theory. Due to their inherit nature of nonlinearity, instability and underactuation, these are widely used to verify and implement emerging control techniques. Moreover, the dynamics of inverted pendulum systems resemble many real-world systems such as segways, humanoid robots etc. In the literature, a wide range of controllers had been tested on this problem, out of which, the most robust being the sliding mode controller while the most optimal being the linear quadratic regulator (LQR) controller. The former has a problem of non-robust reachability phase while the later lacks the property of robustness. To address these issues in both the controllers, this paper presents the novel implementation of integral sliding mode controller (ISMC) for stabilization of a spatial inverted pendulum (SIP), also known as an x-y-z inverted pendulum. The structure has three control inputs and five controlled outputs. Mathematical modeling of the system is done using Euler Lagrange approach. ISMC has an advantage of eliminating non-robust reachability phase along with enhancing the robustness of the nominal controller (LQR Controller). To validate the robustness of ISMC to matched uncertainties, an input disturbance is added to the nonlinear model of the system. Simulation results on two different case studies demonstrate that the proposed controller is more robust as compared to conventional LQR controller. Furthermore, the problem of chattering in the controller is dealt by smoothening the controller inputs to the system with insignificant loss in robustness.

scholarly journals A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199858
Author(s):  
Gianpaolo Gulletta ◽  
Eliana Costa e Silva ◽  
Wolfram Erlhagen ◽  
Ruud Meulenbroek ◽  
Maria Fernanda Pires Costa ◽  
...  
Keyword(s):  
Motor Control ◽  
Upper Limb ◽  
Planning Process ◽  
Computational Cost ◽  
Humanoid Robots ◽  
Efficient Manner ◽  
Daily Lives ◽  
Human Motor Control ◽  
Planning Algorithm ◽  
Human Motor

As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and motion are often considered key features for intuitive human–robot interactions because they allow human peers to easily predict the final intention of a robotic movement. Here, we present a novel motion planning algorithm, the Human-like Upper-limb Motion Planner, for the upper limb of anthropomorphic robots, that generates collision-free trajectories with human-like characteristics. Mainly inspired from established theories of human motor control, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions. For experimental validation, we generate arm-hand trajectories in a series of tasks including simple point-to-point reaching movements and sequential object-manipulation paradigms. Being a major contribution to the current literature, specific focus is on the kinematics of naturalistic arm movements during the avoidance of obstacles. To evaluate human-likeness, we observe kinematic regularities and adopt smoothness measures that are applied in human motor control studies to distinguish between well-coordinated and impaired movements. The results of this study show that the proposed algorithm is capable of planning arm-hand movements with human-like kinematic features at a computational cost that allows fluent and efficient human–robot interactions.

scholarly journals Motor learning in reaching tasks leads to homogenization of task space error distribution

2021 ◽  
Author(s):  
Puneet Singh ◽  
Oishee Ghosal ◽  
Aditya Murthy ◽  
Ashitava Ghodal
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Error Distribution ◽  
Task Space ◽  
Field Perturbation ◽  
Directional Dependence ◽  
Reaching Tasks ◽  
Space Error ◽  
Human Arm ◽  
Point To Point

A human arm, up to the wrist, is often modelled as a redundant 7 degree-of-freedom serial robot. Despite its inherent nonlinearity, we can perform point-to-point reaching tasks reasonably fast and with reasonable accuracy in the presence of external disturbances and noise. In this work, we take a closer look at the task space error during point-to-point reaching tasks and learning during an external force-field perturbation. From experiments and quantitative data, we confirm a directional dependence of the peak task space error with certain directions showing larger errors than others at the start of a force-field perturbation, and the larger errors are reduced with repeated trials implying learning. The analysis of the experimental data further shows that a) the distribution of the peak error is made more uniform across directions with trials and the error magnitude and distribution approaches the value when no perturbation is applied, b) the redundancy present in the human arm is used more in the direction of the larger error, and c) homogenization of the error distribution is not seen when the reaching task is performed with the non-dominant hand. The results support the hypothesis that not only magnitude of task space error, but the directional dependence is reduced during motor learning and the workspace is homogenized possibly to increase the control efficiency and accuracy in point-to-point reaching tasks. The results also imply that redundancy in the arm is used to homogenize the workspace, and additionally since the bio-mechanically similar dominant and non-dominant arms show different behaviours, the homogenizing is actively done in the central nervous system.

Humanoids can take Advantage of Crab-Walking Gaits

2015 ◽  
Vol 12 (01) ◽  
pp. 1550004
Author(s):  
Jonathan Spitz ◽  
Eric Sidorov ◽  
Miriam Zacksenhouse
Keyword(s):  
Humanoid Robots ◽  
Small Range ◽  
Rough Terrain ◽  
Recent Advances ◽  
Wide Range ◽  
Locomotion Pattern

Recent advances in control of humanoid robots have resulted in bipedal gaits that are dynamically stable on moderately rough terrain but are still limited to a small range of slopes. Humanoid robots, like humans, can take advantage of quadruped gaits to greatly extend this range. Cleverly designed gaits can provide robustness to rough terrain without requiring extensive feedback. In this paper, we present a robust crab-walking framework that includes forward and backward crawling patterns, rotation patterns, and sit-down and recovery sequences. The latter are activated autonomously once the robot detects that it tipped over. The performance and robustness of each locomotion pattern are investigated over a wide range of slopes. Crab-walking is shown to be especially adept at crawling forward on steep downward slopes (up to -54°) and crawling backward on steep upward slopes (up to 18°). Finally, we demonstrate the framework's autonomous capabilities by crossing the rough terrain in DARPA's virtual robotics challenge.

Recursive field estimation and tracking for autonomous manipulation

Robotica ◽  
2011 ◽  
Vol 30 (5) ◽  
pp. 743-753 ◽  
Author(s):  
Soo Jeon
Keyword(s):  
Radial Basis Function Network ◽  
Least Square Method ◽  
Least Square ◽  
Source Tracking ◽  
Control Law ◽  
Task Space ◽  
Recursive Least Square ◽  
Wide Range ◽  
Field Estimation ◽  
Task Space Control

SUMMARYAutonomous operation of mechanical systems often requires the ability to detect and locate a particular phenomenon occurring in the surrounding environment. Being implemented to articulated manipulation, such a capability may realize a wide range of applications in autonomous maintenance and repair. This paper presents the sensor-driven task space control of an end-effector that combines the field estimation and the target tracking in an unknown spatial field of interest. The radial basis function network is adopted to model spatial distribution of an environmental phenomenon as a scalar field. Their weight parameters are estimated by a recursive least square method using collective measurements from the on-board sensors mounted to the manipulator. Then the asymptotic source tracking has been achieved by the control law based on the gradient of the estimated field. A new singularity tolerant scheme has been suggested to command the task space control law despite singular configurations. Simulation results using the three-link planar robot and the 6-revolute elbow manipulator are presented to validate the main ideas.

Efficient Actor-Critic Reinforcement Learning With Embodiment of Muscle Tone for Posture Stabilization of the Human Arm

2021 ◽  
Vol 33 (1) ◽  
pp. 129-156
Author(s):  
Masami Iwamoto ◽  
Daichi Kato
Keyword(s):  
Reinforcement Learning ◽  
Muscle Tone ◽  
Muscle Tension ◽  
Experimental Studies ◽  
Control Policy ◽  
Humanoid Robots ◽  
Pedunculopontine Tegmental Nucleus ◽  
Learning Efficiency ◽  
Human Arm ◽  
Posture Stabilization

This letter proposes a new idea to improve learning efficiency in reinforcement learning (RL) with the actor-critic method used as a muscle controller for posture stabilization of the human arm. Actor-critic RL (ACRL) is used for simulations to realize posture controls in humans or robots using muscle tension control. However, it requires very high computational costs to acquire a better muscle control policy for desirable postures. For efficient ACRL, we focused on embodiment that is supposed to potentially achieve efficient controls in research fields of artificial intelligence or robotics. According to the neurophysiology of motion control obtained from experimental studies using animals or humans, the pedunculopontine tegmental nucleus (PPTn) induces muscle tone suppression, and the midbrain locomotor region (MLR) induces muscle tone promotion. PPTn and MLR modulate the activation levels of mutually antagonizing muscles such as flexors and extensors in a process through which control signals are translated from the substantia nigra reticulata to the brain stem. Therefore, we hypothesized that the PPTn and MLR could control muscle tone, that is, the maximum values of activation levels of mutually antagonizing muscles using different sigmoidal functions for each muscle; then we introduced antagonism function models (AFMs) of PPTn and MLR for individual muscles, incorporating the hypothesis into the process to determine the activation level of each muscle based on the output of the actor in ACRL. ACRL with AFMs representing the embodiment of muscle tone successfully achieved posture stabilization in five joint motions of the right arm of a human adult male under gravity in predetermined target angles at an earlier period of learning than the learning methods without AFMs. The results obtained from this study suggest that the introduction of embodiment of muscle tone can enhance learning efficiency in posture stabilization disorders of humans or humanoid robots.

Do Arm Postures Vary With the Speed of Reaching?

1999 ◽  
Vol 81 (5) ◽  
pp. 2582-2586 ◽  
Author(s):  
Kiisa C. Nishikawa ◽  
Sara T. Murray ◽  
Martha Flanders
Keyword(s):  
Degrees Of Freedom ◽  
Three Dimensional ◽  
Optimal Solution ◽  
Reaching Movements ◽  
Total Force ◽  
Joint Torques ◽  
Dynamic Factors ◽  
Human Arm ◽  
Wide Range ◽  
Dynamic Forces

Do arm postures vary with the speed of reaching? For reaching movements in one plane, the hand has been observed to follow a similar path regardless of speed. Recent work on the control of more complex reaching movements raises the question of whether a similar “speed invariance” also holds for the additional degrees of freedom. Therefore we examined human arm movements involving initial and final hand locations distributed throughout the three-dimensional (3D) workspace of the arm. Despite this added complexity, arm kinematics (summarized by the spatial orientation of the “plane of the arm” and the 3D curvature of the hand path) changed very little for movements performed over a wide range of speeds. If the total force (dynamic + quasistatic) had been optimized by the control system (e.g., as in a minimization of the change in joint torques or the change in muscular forces), the optimal solution would change with speed; slow movements would reflect the minimal antigravity torques, whereas fast movements would be more strongly influenced by dynamic factors. The speed-invariant postures observed in this study are instead consistent with a hypothesized optimization of only the dynamic forces.

scholarly journals Soft and ion-conducting hydrogel artificial tongue for astringency perception

2020 ◽  
Vol 6 (23) ◽  
pp. eaba5785 ◽  
Author(s):  
Jeonghee Yeom ◽  
Ayoung Choe ◽  
Seongdong Lim ◽  
Youngsu Lee ◽  
Sangyun Na ◽  
...  
Keyword(s):  
Flexible Substrate ◽  
High Sensitivity ◽  
Humanoid Robots ◽  
Fast Response ◽  
Nanoporous Structure ◽  
Proof Of Concept ◽  
Human Tongue ◽  
Wide Range ◽  
Ion Conducting ◽  
Monitoring Devices

Artificial tongues have been receiving increasing attention for the perception of five basic tastes. However, it is still challenging to fully mimic human tongue–like performance for tastes such as astringency. Mimicking the mechanism of astringency perception on the human tongue, we use a saliva-like chemiresistive ionic hydrogel anchored to a flexible substrate as a soft artificial tongue. When exposed to astringent compounds, hydrophobic aggregates form inside the microporous network and transform it into a micro/nanoporous structure with enhanced ionic conductivity. This unique human tongue–like performance enables tannic acid to be detected over a wide range (0.0005 to 1 wt %) with high sensitivity (0.292 wt %−1) and fast response time (~10 s). As a proof of concept, our sensor can detect the degree of astringency in beverages and fruits using a simple wipe-and-detection method, making a powerful platform for future applications involving humanoid robots and taste monitoring devices.

Application of SCES in Improving Dynamic Response of DC Microgrid Bus Voltage

2014 ◽  
Vol 521 ◽  
pp. 431-434
Author(s):  
Yuan Sheng Xiong ◽  
Jian Ming Xu
Keyword(s):  
Dynamic Response ◽  
Feedforward Control ◽  
Dc Microgrid ◽  
Wide Range ◽  
Supercapacitor Energy Storage ◽  
Response Performance ◽  
Dc Bus ◽  
Bus Voltage ◽  
The Stability ◽  
The Impact

To improve the stability of DC bus voltage in DC microgrid, and reduce the impact on microgrid equipments by the DC bus voltage fluctuations, a supercapacitor energy storage (SCES) is designed to connect to the DC bus by the bi-directional converter. The controller is designed by the feedforward control and proportional method with the deadband. The great load disturbance is simulated in PSIM software when the DC microgrid operates in the grid-connected rectification mode. The simulation results show that SCES under the proposed control strategy can reduce the fluctuation range of the DC bus voltage in a wide range of load disturbances, and the dynamic response performance of DC bus voltage is improved.

Task-Space Control Design and Performance Evaluation for a 6DOF Stewart Nanoscale Platform

2008 ◽  
Author(s):  
Chun-Chung Li ◽  
Yung Ting ◽  
Yi-Hung Liu ◽  
Yi-Da Lee ◽  
Chun-Wei Chiu
Keyword(s):  
Feedback Control ◽  
Hybrid Control ◽  
Feedforward Control ◽  
Feedback Controller ◽  
Test Method ◽  
Computation Method ◽  
Task Space ◽  
Force Output ◽  
End Effector ◽  
Control Scheme

A 6DOF Stewart platform using piezoelectric actuators for nanoscale positioning objective is designed. A measurement method that can directly measure the pose (position and orientation) of the end-effector is developed so that task-space on-line control is practicable. The design of a sensor holder for sensor employment, a cuboid with referenced measure points, and the computation method for obtaining the end-effector parameters is introduced. A control scheme combining feedforward and feedback is proposed. The inverse model of a hysteresis model derived by using a dynamic Preisach method is used for the feedforward control. Hybrid control to maintain both the positioning and force output for nano-cutting and nano-assembly applications is designed for the feedback controller. The optimal gain of the feedback controller is searched by using relay feedback test method and genetic algorithm. In experiment, conditions with/without external load employed with feedforward, feedback, and feedforward with feedback control schemes respectively are carried out. Performance of each control scheme verifies the capability of achieving nanoscale precision. The combined feedforward and feedback control scheme is superior to the others for gaining better precision.

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