Motion Control Tasks Research Articles

Introduction, mathematical modelling and motion control of the novel pneumatic textile actuator

AbstractThis paper introduces a novel type of pneumatic actuator, namely the pneumatic textile actuator (PTA). Although the operating principle is similar to pneumatic artificial muscles, design, fabrication and properties of PTAs show significant differences. PTAs consist of double-layered textiles, fabricated in one piece using the Jacquard weaving technology. By filling the chamber between the two layers with pressurised air, one obtains a low-weight, high-power pneumatic actuator at very low cost. The paper first describes the design, fabrication and properties of PTAs in general. Then, the characteristics of a specific PTA are determined experimentally. Moreover, we derive a mathematical model of the dynamic behaviour of the PTA. The model forms the basis for a motion control algorithm, combining flatness-based feedforward and linear feedback control. Finally, the performance of the controller is evaluated experimentally. The results indicate that PTAs are well suited for motion control tasks requiri...

Prototyping Force-Controlled 3-DOF Hydraulic Arms for Humanoid Robots

[abstFig src='/00280001/11.jpg' width=""230"" text='Hydraulic dual arm robot prototype' ]This paper reports on a hydraulic dual arm robot developed as a rapid prototype for our hydraulic humanoid robot. The lightweight arms (4 kg each) have three joints driven by hydraulic linear servo actuators that can achieve higher torque and speed than human arms. A double four-bar linkage provides a wide range of motion (210°) to the shoulder joint. Each joint has torque controllability that is fully utilized for compliant whole-body motion control tasks. Based on singular perturbation analysis, we discuss how damping on the joints is actively modulated by hydraulic force feedback control, which is then utilized in our passivity-based task-space force control scheme. The effectiveness of the proposed system is experimentally evaluated through zero-force tracking gravity compensation with a 10 kg payload and object manipulation tasks.

Guaranteeing Isochronous Control of Networked Motion Control Systems Using Phase Offset Adjustment.

Guaranteeing isochronous transfer of control commands is an essential function for networked motion control systems. The adoption of real-time Ethernet (RTE) technologies may be profitable in guaranteeing deterministic transfer of control messages. However, unpredictable behavior of software in the motion controller often results in unexpectedly large deviation in control message transmission intervals, and thus leads to imprecise motion. This paper presents a simple and efficient heuristic to guarantee the end-to-end isochronous control with very small jitter. The key idea of our approach is to adjust the phase offset of control message transmission time in the motion controller by investigating the behavior of motion control task. In realizing the idea, we performed a pre-runtime analysis to determine a safe and reliable phase offset and applied the phase offset to the runtime code of motion controller by customizing an open-source based integrated development environment (IDE). We also constructed an EtherCAT-based motion control system testbed and performed extensive experiments on the testbed to verify the effectiveness of our approach. The experimental results show that our heuristic is highly effective even for low-end embedded controller implemented in open-source software components under various configurations of control period and the number of motor drives.

Influence Vectors Control for Robots Using Cellular-Like Binary Actuators

Robots using cellular-like redundant binary actuators could outmatch electric-gearmotor robotic systems in terms of reliability, force-to-weight ratio, and cost. This paper presents a robust fault-tolerant control scheme that is designed to meet the control challenges that are encountered by such robots, i.e., discrete actuator inputs, complex system modeling, and cross-coupling between actuators. In the proposed scheme, a desired vectorial system output, such as a position or a force, is commanded by recruiting actuators based on their influence vectors on the output. No analytical model of the system is needed; influence vectors are identified experimentally by sequentially activating each actuator. For position control tasks, the controller uses a probabilistic approach and a genetic algorithm to determine an optimal combination of actuators to recruit. For motion control tasks, the controller uses a sliding mode approach and independent recruiting decision for each actuator. Experimental results on a four degrees of freedom binary manipulator with 20 actuators confirm the method’s effectiveness and its ability to tolerate massive perturbations and numerous actuator failures.

Accuracy enhancement of the spherical actuator with a two-level geometric calibration method

This paper presents a two-level geometric calibration method for the permanent magnet (PM) spherical actuator to improve its motion control accuracy. The proposed actuator is composed of a stator with circumferential coils and a rotor with multiple PM poles. Due to the assembly and fabrication errors, the real geometric parameters of the actuator will deviate from their design values. Hence, the identification of such errors is critical for the motion control tasks. A two-level geometric calibration approach is proposed to identify such errors. In the first level, the calibration model is formulated based on the differential form of the kinematic equation, which is to identify the geometric errors in the spherical joint. In the second level, the calibration model is formulated based on the differential form of torque formula, which is to calibrate the geometric parameters of the magnetization axes of PM poles and coils axes. To demonstrate the robustness and availability of the calibration algorithm, simulations are conducted. The results have shown that the proposed two-level calibration method can effectively compensate the geometric parameter errors and improve the positioning accuracy of the spherical actuator.

Lining-Up Control Strategies for N-trailer Vehicles

Maneuvers performed with tractor-trailers vehicles (N-trailers) belong to the most demanding motion control tasks in the transportation practice. Very frequent maneuvers concern the lining-up proce...

Contribution of coherent motion to the perception of biological motion among persons with Schizophrenia.

People with schizophrenia (SCZ) are impaired in several domains of visual processing, including the discrimination and detection of biological motion. However, the mechanisms underlying SCZ-related biological motion processing deficits are unknown. Moreover, whether these impairments are specific to biological motion or represent a more widespread visual motion processing deficit is unclear. In the current study, three experiments were conducted to investigate the contribution of global coherent motion processing to biological motion perception among patients with SCZ. In Experiments 1 and 2, participants with SCZ (n = 33) and healthy controls (n = 33) were asked to discriminate the direction of motion from upright and inverted point-light walkers in the presence and absence of a noise mask. Additionally, participants discriminated the direction of non-biological global coherent motion. In Experiment 3, participants discriminated the direction of motion from upright scrambled walkers (which contained only local motion information) and upright random position walkers (which contained only global form information). Consistent with previous research, results from Experiment 1 and 2 showed that people with SCZ exhibited deficits in the direction discrimination of point-light walkers; however, this impairment was accounted for by decreased performance in the coherent motion control task. Furthermore, results from Experiment 3 demonstrated similar performance in the discrimination of scrambled and random position point-light walkers.

An Approach for Teaching Automatic Control in a Laboratory of Mechatronics

In this paper we propose an approach to effectively teach the automatic control topic in a Laboratory of Mechatronics course. In particular, we propose to use a motion system with an elastic transmission in order to implement a motion control task. The use of this experimental setup, which includes both a mechanical and an electrical/electronic part allows the student to verify the importance of many concepts learned in previous courses such as PID control, cascade control, anti-windup, observer design, digital control.

Modeling, control, and analysis of a robotic assist device

This paper presents a model-based approach to control design for an existing lower-limb robotic assist device, the portable powered ankle–foot orthosis (PPAFO). This approach seeks to address two key limitations of the PPAFO caused by the use of solenoid valves: slow system response and inefficient actuation during assistance. System limitations were addressed using a proportional valve coupled with a modified control approach. The two different system configurations were compared in simulation and on an experimental test fixture during motion and torque control tasks. Root mean square (RMS) trajectory tracking error was used to evaluate system performance, while system efficiency was assessed by measuring pneumatic fuel consumed during each task. The proportional valve system reduced RMS tracking error by as much as 91%, and increased efficiency by as much as 95% over the solenoid valve system.

Adaptive range‐measurement‐based target pursuit

SUMMARYThis paper presents an adaptive scheme for localization of a target from distance measurements and motion control of a mobile agent to pursue this target. The localization and motion control task of interest is approached within a parameter‐identifier‐based adaptive control framework, where the localization is formulated as a parameter identification problem and the motion control is achieved using an adaptive controller based on the produced location estimates of the target. First, a robust adaptive law is designed to generate location estimate of the target using distance measurements. Then, following the standard certainty equivalence approach, a motion control law is developed considering substitution of the estimate generated by the localization algorithm for the unknown location of the target. Noting that there is some incompatibility between the persistence of excitation requirements of the localization algorithm and the target pursuit goal of the motion control law, the base motion control law is (re)designed to eliminate the effects of this incompatibility. The novelty of this paper is in this motion control design eliminating the persistence of excitation incompatibility. Stability and convergence analysis for the overall adaptive control scheme is presented. The results are valid in both two and three dimensions of motion space. The applications of the adaptive scheme include rescue localization, surveillance of signal sources, and formation acquisition of autonomous multi‐robot/vehicle systems. Copyright © 2012 John Wiley & Sons, Ltd.

Advanced DC Motor Drive for Haptic Devices

Haptics covers many different forms of mechanical interaction with human senses by engaging, touch, vibrations and forces/torques, established for the purpose of augmenting the feedback structure during human-machine interaction. A haptic device has mechanical part, moved by actuators from one and human hand or fingers from the other, actuators, drives, sensing elements, as well as algorithms designed to control the interaction between human and machine in positioning and motion control tasks. With such a system, motors can be controlled in a way to simulate various environments, defined by their material and dynamics, for example pushing soft ball uphill. Haptic devices are becoming more popular in medical applications after introduction of modern medical robots with many different extensions for minimally invasive surgery or diagnostics based on palpation. This paper discusses one DC motor driver custom designed for the purpose of designing a haptic device for medical applications.

CPG Control for Biped Hopping Robot in Unpredictable Environment

A CPG control mechanism is proposed for hopping motion control of biped robot in unpredictable environment. Based on analysis of robot motion and biological observation of animal's control mechanism, the motion control task is divided into two simple parts: motion sequence control and output force control. Inspired by a two-level CPG model, a two-level CPG control mechanism is constructed to coordinate the drivers of robot joint, while various feedback information are introduced into the control mechanism. Interneurons within the control mechanism are modeled to generate motion rhythm and pattern promptly for motion sequence control; motoneurons are modeled to control output forces of joint drivers in real time according to feedbacks. The control system can perceive changes caused by unknown perturbations and environment changes according to feedback information, and adapt to unpredictable environment by adjusting outputs of neurons. The control mechanism is applied to a biped hopping robot in unpredictable environment on simulation platform, and stable adaptive motions are obtained.

Motion Control System Design of a Humanoid Robot Based on Stereo Vision

Vision is an important means of the humanoid robot to get external environmental information; vision system is an important part of humanoid robot. The system of a humanoid robot with the functions of visual perception and object manipulation is very complex because the body of the humanoid robot possesses many joint units and sensors. Two computers linked by Memolink communication unit is adopted to meet the needs of real time motion control and visual information processing tasks. The motion control system included coordination control computer, the distributed DSP joint controllers, DC motor drivers and sensors. Linux and real-time RT-Linux OS are used as the operating system to achieve the real-time control capability.

Measuring Neuromuscular Control Dynamics During Car Following With Continuous Haptic Feedback

In previous research, a driver support system that uses continuous haptic feedback on the gas pedal to inform drivers of the separation to the lead vehicle was developed. Although haptic feedback has been previously shown to be beneficial, the influence of the underlying biomechanical properties of the driver on the effectiveness of haptic feedback is largely unknown. The goal of this paper is to experimentally determine the biomechanical properties of the ankle-foot complex (i.e., the admittance) while performing a car-following task, thereby separating driver responses to visual feedback from those to designed haptic feedback. An experiment was conducted in a simplified fixed-base driving simulator, where ten participants were instructed to follow a lead vehicle, with and without the support of haptic feedback. During the experiment, the lead vehicle velocity was perturbed, and small stochastic torque perturbations were applied to the pedal. Both perturbations were separated in the frequency domain to allow the simultaneous estimation of frequency response functions of both the car-following control behavior and the biomechanical admittance. For comparison to previous experiments, the admittance was also estimated during three classical motion control tasks (resist forces, relax, and give way to forces). The main experimental hypotheses were that, first, the haptic feedback would encourage drivers to adopt a "give way to force task," resulting in larger admittance compared with other tasks and, second, drivers needed less control effort to realize the same car-following performance. Time- and frequency-domain analyses provided evidence for both hypotheses. The developed methodology allows quantification of the range of admittances that a limb can adopt during vehicle control or while performing a variety of motion control tasks. It thereby allows detailed computational driver modeling and provides valuable information on how to design and evaluate continuous haptic feedback systems.

Multitask Constrained Motion Control Using a Mass-Weighted Orthogonal Decomposition

This paper presents an approach to formulating task-level motion-control for holonomically constrained multibody systems based on a mass-weighted orthogonal decomposition. The basis for this approach involves the formation of a recursive null space for constraints and motion-control tasks onto which subsequent motion-control tasks are projected. The recursive null space arises out of the process of orthogonalizing individual task Jacobian matrices. This orthogonalization process is analogous to the Gram–Schmidt process used for orthogonalizing a vector basis. Based on this mass-weighted orthogonal decomposition, recursive algorithms are developed for formulating the overall motion-control equations. The natural symmetry between task-level dynamics and the dynamics of constrained systems is exploited in this approach. An example is presented to illustrate the practical application of this methodology.

Immersion and Invariance Based Adaptive Control of Three Dimensional Autonomous Vehicle Maneuvers

In this paper, we focus on the three dimensional motion control of autonomous vehicles controlled by body frame fixed control signals, to track specified desired trajectories. Since the trajectories are specified in the inertial frame and the control signals to be generated are in the body frame, such a motion control task depends on measurement of the Euler angles defining the relation between body and inertial frames using the navigation equipment mounted on the vehicles. The measurements provided by this equipment carry noises, which bring parametric uncertainty to the corresponding motion kinematics. We design an adaptive controller to perform the motion control task mentioned above, paying attention to the parametric uncertainties caused by measurement noises, employing a recently developed immersion and invariance based scheme. We provide simulation results demonstrating the performance of the adaptive controller and discuss application in formation control of autonomous multi-agent systems.

A rigorous model of reflex function indicates that position and force feedback are flexibly tuned to position and force tasks.

This study aims to quantify the separate contributions of muscle force feedback, muscle spindle activity and co-contraction to the performance of voluntary tasks (“reduce the influence of perturbations on maintained force or position”). Most human motion control studies either isolate only one contributor, or assume that relevant reflexive feedback pathways during voluntary disturbance rejection tasks originate mainly from the muscle spindle. Human ankle-control experiments were performed, using three task instructions and three perturbation characteristics to evoke a wide range of responses to force perturbations. During position tasks, subjects (n = 10) resisted the perturbations, becoming more stiff than when being relaxed (i.e., the relax task). During force tasks, subjects were instructed to minimize force changes and actively gave way to imposed forces, thus becoming more compliant than during relax tasks. Subsequently, linear physiological models were fitted to the experimental data. Inhibitory, as well as excitatory force feedback, was needed to account for the full range of measured experimental behaviors. In conclusion, force feedback plays an important role in the studied motion control tasks (excitatory during position tasks and inhibitory during force tasks), implying that spindle-mediated feedback is not the only significant adaptive system that contributes to the maintenance of posture or force.

Self-Calibration of a Biologically Inspired 7 DOF Cable-Driven Robotic Arm

This paper presents the self-calibration of a novel biologically inspired 7 DOF cable-driven robotic arm. Similar to the human arm, the proposed robotic arm consists of three sequentially connected modules, i.e., a 3 DOF shoulder module, a 1 DOF elbow module, and a 3 DOF wrist module. Due to factors like manufacturing defects, assembly misalignments, compliance, and wear of connecting mechanisms, errors in the geometric model parameters always exist. Hence, the identification of such errors is critical for path planning and motion control tasks. Self-calibration models of the various modules in the robotic arm are formulated based on the differential change in the cable end-point distances. Due to the linear nature of these self-calibration models, an iterative least-squares algorithm is employed to identify the errors in the geometric model parameters. The calibration does not require any external pose measurement devices, because it utilizes the cable length data obtained from the redundant actuation scheme of the cable-driven arm. Computer simulations and experimental studies were carried out on both the 3 DOF and 1 DOF modules, to verify the robustness and effectiveness of the proposed self-calibration algorithm. From the experimental studies, errors in the geometric model parameters were precisely recovered after a minimum number of pose measurements.

Cooperative Path-Following of Underactuated Autonomous Marine Vehicles with Logic-based Communication

This paper addresses the problem of steering a group of underactuated marine vehicles along given spatial paths, while holding a desired inter-vehicle formation pattern (cooperative path-following). The CPF problem is divided into a motion control task of making each vehicle track a virtual target moving along the desired path and a dynamic assignment task of adjusting the speeds of the virtual targets so as to achieve vehicle coordination. At the path-following level, the controller derived exhibits a inner-outer-loop structure and includes an observer to estimate the ocean currents and the sway velocity. At the coordination level, the decentralized control algorithm addresses explicitly the case where the communications among the vehicles occur with non-homogeneous, possibly varying delays. Convergence and stability of the overall system are proved formally. Simulations results are presented and discussed.

Reinforcement learning of a continuous motor sequence with hidden states

Reinforcement learning is the scheme for unsupervised learning in which robots are expected to acquire behavior skills through self-explorations based on reward signals. There are some difficulties, however, in applying conventional reinforcement learning algorithms to motion control tasks of a robot because most algorithms are concerned with discrete state space and based on the assumption of complete observability of the state. Real-world environments often have partial observablility; therefore, robots have to estimate the unobservable hidden states. This paper proposes a method to solve these two problems by combining the reinforcement learning algorithm and a learning algorithm for a continuous time recurrent neural network (CTRNN). The CTRNN can learn spatio-temporal structures in a continuous time and space domain, and can preserve the contextual flow by a self-organizing appropriate internal memory structure. This enables the robot to deal with the hidden state problem. We carried out an experiment on the pendulum swing-up task without rotational speed information. As a result, this task is accomplished in several hundred trials using the proposed algorithm. In addition, it is shown that the information about the rotational speed of the pendulum, which is considered as a hidden state, is estimated and encoded on the activation of a context neuron.