Motion Control Of Manipulators Research Articles

Development of a cable suspension and balance system and its novel calibration methods for effective wind tunnel tests

Based on the advantages of the cable-driven parallel robot (CDPR), we developed a cable suspension and balance system (CSBS) as a multi-purpose device for wind tunnel tests with motion control and load measurement capability. The CSBS was designed to serve as both a wind tunnel model mount and balance.This paper suggests two novel methodologies to improve the CSBS performance, one for more accurate motion control and the other for improved load measurement. The motion control correction was implemented by applying a multi-variable polynomial function that adjusts motion commands to generate the required response utilizing the measured motion data. For more accurate load measurement, a multi-layer perceptron (MLP) is proposed as a correction function. This MLP was trained to provide corrected three forces and three moments according to the reference balance data.Several motion tests, loading tests, and statistical analyses were carried out before and after correction to investigate the correction effectiveness. Differences between the motion command and response were dramatically reduced by the polynomial correction, and the MLP learning methodology effectively enhanced the load measurement accuracy of the CSBS.Using both of the suggested corrections, the CSBS was equipped with enhanced motion control and load measurement performance. Several wind tunnel tests were conducted to examine the CSBS reliability as a model mount and load measurement system, and test results showed reasonably good agreement with reference data for a cylinder model and a NACA0015 airfoil model.

Tek Mıknatıs aracığılı ile bir Manyetotaktik Bakterinin Adaptif Manevra Kontrolü için Bağımsız Eklem Kontrol Simulasyonları

The use of micro-robotic systems in non-invasive medicine has been heavily promoted in the literature for the last decade. The studies usually focus on artificial or biohybrid microswimmers of various origins subject to the effect of an external electromagnetic field controlled by a computer. Although there exist several motion control studies shared to date, control of a bio-hybrid microswimmer has rarely been demonstrated employing an open kinematic chain, in detail. In this work, motion control of an isolated magnetotactic bacterium cell (Magnetospirillum Gryphiswaldens) is presented via a magnetic field actively positioned by an open kinematic chain. The cell is modeled with its complete environment to make it as realistic as possible along with the magnetic torque, which is induced by a single magnet attached at the end effector of a robotic arm, exerted on it for maneuvering control. The control is based on a proportional – integral – derivative (PID) gain scheme with adaptive integral gain to focus on a particular steady-state error with discontinuous reference signals. The control signal is transformed into pulse width modulation (PWM) signals to drive the motors articulating the joints of the open kinematic chain, the inverse kinematics of which is designed to be simple enough to achieve independent joint control. A numerical analysis of the coupled system is carried out in the time domain. The performance of the said motion control approach is investigated for each degree of freedom for the planar motion of the microswimmer. Simulations demonstrate a planar open kinematic chain is capable of control the gait of the microswimmer while following its trajectory near a planar boundary via independent joint control. Furthermore, simulations demonstrate that the effective magnetic inertia and the shear stress results in a small but certain lag in the motion control performance of the overall system.

Phase Current Sensing Method Using Three Shunt Resistors to Eliminate Immeasurable Area in Voltage Vector Plane

This paper proposes a current sensing method to eliminate the immeasurable areas in three-phase inverters using the shunt resistors. Conventional shunt resistor-based current sensing methods have immeasurable areas in the voltage vector plane. Since motor control performance is degraded by the immeasurable area, various current reconstruction techniques have been proposed to expand the measurement range. Nevertheless, the software algorithm is quite complex, and the reconstruction ability is still unsatisfactory. In the proposed method, shunt resistors are located in series with the inverter output lines to obtain the motor phase currents. Both ends of the shunt resistors are at the high-potential as the dc-link voltage. High-potential voltages are lowered to the common-mode voltage level of the analog amplifier using voltage divider, and voltage drops across shunt resistors are detected by instrumentation amplifiers. Immeasurable areas are completely eliminated because phase currents can be detected directly through line shunt resistors. Simulations and experiments are carried out to verify the effectiveness of the proposed current sensing method.

Extended Kalman filter based estimations for improving speed‐sensored control performance of induction motors

In this study, an extended Kalman filter (EKF)-based estimation algorithm is presented to improve the speed-sensored control performance of induction motors (IMs). The proposed EKF-based estimation algorithm is to simultaneously estimate the stator stationary axis components of stator currents and rotor fluxes, rotor angular speed, load torque including viscous friction term, rotor resistance and magnetising inductance in a single EKF algorithm without requiring any switching operation or a hybrid structure. In order to improve the speed-sensored control performance, the measurement/output matrix of IM model is extended by the measured rotor speed in addition to stationary axis components of the measured stator currents. Therefore, the proposed EKF algorithm uses the speed and stator current errors between the measured and priori estimation values in order to calculate the posterior estimation ones. For performance evaluation, the eighth order (proposed) EKF algorithm is tested by simulations and real-time experiments under challenging scenarios and compared with the developed sixth order EKF in real time. The obtained real-time results also show that the eighth order (proposed) EKF algorithm provides additional and improved estimations with the increased but feasible execution time in terms of the sixth order EKF designed in this paper.

Expansion of Operating Speed Range of High-Speed BLDC Motor Using Hybrid PWM Switching Method Considering Dead Time

In vehicle electrical systems with limited battery power, the output torque and speed of high-speed brushless DC (BLDC) motors can decrease due to unstable and reduced supply voltage or manufacturing errors in the motor back electromotive force (EMF). This paper presents a method that can guarantee the output performance of an inverter through a control algorithm without a separate power supply system and DC-link voltage increase. The proposed control algorithm can increase the output torque and speed of a high-speed BLDC motor by using appropriate selection and change of the inverter’s pulse width modulation (PWM) control method. In this paper, the operation and electrical characteristics of various PWM methods of BLDC motors are analyzed, and the optimal PWM method for improving the control performance of high-speed BLDC motors is presented. In addition, the relationship between the switching frequency, dead time, and voltage utilization was mathematically analyzed. Based on the results of this analysis, the proposed control algorithm automatically changes the PWM switching mode at the point where the output torque and speed need to be extended. The effectiveness and feasibility of the control method proposed in this paper is verified through the experimental results on the designed and manufactured high-speed BLDC motor system for vehicles.

Evaluation of Three Improved Space-Vector-Modulation Strategies for the High-Speed Permanent Magnet Motor Fed by a SiC/Si Hybrid Inverter

The large current ripple caused by the limited switching frequency significantly degrades the control performance of the high-speed permanent magnet motor drive system. To mitigate the current ripple, the switching frequency of the inverter should be high enough. The purpose of this article is to reduce the current ripple of the HSPMSM drive system. First, a Silicon-Carbide/Silicon(SiC/Si) hybrid inverter is designed. The designed hybrid inverter consists of two SiC-MOSFETs and six Si-IGBTs, and the circuit modals of the SiC/Si hybrid inverter are analyzed. Second, based on the SiC/Si hybrid inverter, three improved space-vector-modulation (SVM) strategies are proposed. The core principle of the proposed SVM strategies is that most of the switching actions are transferred from the Si-IGBTs to the SiC-MOSFETs. The benefit from the low switching loss of the SiC device is that the total power loss can be significantly reduced. Besides, the SiC-MOSFETs can create the zero-voltage-switch condition for the Si-IGBTs, and then the switching loss of the Si-IGBTs can be almost eliminated. Finally, to verify the effectiveness of the proposed methods, sufficient simulations and experiments are implemented. The stator current ripple, total-harmonic-distortion, and the conversion efficiency of the inverter in the full power range are compared under the same condition based on the full-Si-IGBT inverter, the full-SiC-MOSFET inverter, and the SiC/Si hybrid inverter. The evaluated results sufficiently verify the superiority of the proposed SiC/Si hybrid inverter and the improved SVM strategies.

Collision risk reduction of N-trailer agricultural machinery by off-track minimization

In this work we study the effect of changing the vehicle guidance point position over the collision risk and motion control performance of an N-trailer vehicle. Based on a Monte Carlo method, we are able to find the correlation among motion control performance, trajectory curvature variation and guidance point position, to strategically select the latter to enhance the motion control performance. The performance metrics consider controller effort and manoeuvrability space penalties associated with off-track errors and unsafe manoeuvres of the entire vehicles’ chain. To validate scalability of the guidance point selection strategy, several N-trailer vehicles were tested in simulation to track a trajectory with abrupt curvature changes, similar to the trajectories executed by machinery in agricultural operations. The performance results are compared to ones obtained using traditional strategy to show the reduction of dangerous manoeuvres as internal turning from 24.5% up to 42.6% as well as a reduction of the resultant off-track errors from 17.3% up to 26.84% in the cases from 4 to 10 trailers respectively.

Proximity Operations and Three Degree-of-Freedom Maneuvers Using the Smartphone Video Guidance Sensor

This paper presents the first demonstration of NASA’s Smartphone Video Guidance Sensor (SVGS) as real-time position and attitude estimator for proximity and formation maneuvers. An optimal linear quadratic Gaussian controller was used, combining a linear quadratic regulator and a Kalman filter. The system was demonstrated controlling the 3-degree of freedom planar motion of the RINGS ground units (Resonant Inductive Near-field Generation Systems). A state-space model of the system’s 3-DOF motion dynamics was derived, and model parameters extracted using a system identification technique. The system’s motion control performance is experimentally demonstrated in both tracking and formation maneuvers. The results highlight the capabilities and performance of the Smartphone Video Guidance Sensor (SVGS) as a vision-based real-time position and attitude sensor for motion control, formation flight and proximity operations. A leader-follower formation maneuver approach is demonstrated, as well as position hold and path following.

Tribo-dynamic analysis and motion control of a rotating manipulator based on the load and temperature dependent friction model

Joint friction has a significant influence on the dynamics and motion control of manipulators. However, the friction effect is often omitted or simplified in previous studies. In this paper, we establish the dynamics model of a single joint in a rotating industrial manipulator taking detailed friction effects into consideration and propose a new control algorithm for the friction compensation purpose. Firstly, the manipulator dynamics modeling is carried out employing a recently-proposed extended static friction model, which depicts load and temperature influence on Coulomb, Stribeck and viscous terms. Moreover, based on the established dynamics model, the paper presents a new adaptive fast nonsingular terminal sliding mode (AFNTSM) controller. The proposed approach has the advantages of continuous control inputs, fast convergence rate, no singularity and great robustness against disturbances. Furthermore, its adaptive property does not require any prior knowledge of the upper bound of the uncertainties. Finally, the proposed controller is applied to the manipulator joint trajectory tracking problem with varying friction subject to load and temperature changes. The numerical simulation verifies the effectiveness of our proposed method and its advantages over other controllers.

Identification of mechanical parameters for position-controlled servo systems using sinusoidal commands

This paper proposes an identification method for mechanical parameters based on position control. To improve motion control performance, the moment of inertia and friction components must be considered. Based on mechanical equations, the proposed method estimates the moment of inertia, viscous friction coefficient, and Coulomb friction in the off-line state. Mechanical parameters are obtained from the integral values for the products of the torque, speed, and position using the 90° phase relationship between acceleration and velocity. Simulation and experimental results demonstrate the validity and accuracy of the proposed method. Since its implementation is simple, this method can be applied easily to industry.

An Overall System Delay Compensation Method for IPMSM Sensorless Drives in Rail Transit Applications

In rail transit application, the multimode pulsewidth modulation (PWM) is used to take full advantage of the dc-bus voltage in wide speed range. To improve the sensorless control performance of interior permanent magnet synchronous motor (IPMSM) drives in this condition, an overall system delay compensation method is proposed for a hybrid position observer that combines a simple square-wave voltage injection with a nonsingular terminal sliding mode observer. Due to the delay effect, the currents and position estimation performance are deteriorated, such as more severe dq-axis currents coupling and fluctuation of estimated position error. In order to solve these problems, the analysis of delay characteristics in the IPMSM sensorless drives is first presented. For carrier-based modulation, the compensation time is obtained through a self-tuning PI regulator based on predictive q-axis voltage error. Then, the three-phase reference currents are utilized to judge the direction near zero-crossing point and the parameters robustness is also analyzed. For optimal PWM modulation, the voltage vector angle and modulation depth are predicted in a simple way, and the delay effect can be eliminated. Furthermore, the harmonics distribution in sensorless control is described by power spectral density. Finally, the effectiveness of proposed strategy is verified by experimental results with a 3.7-kW IPMSM sensorless drive platform.

Acceleration Noise Suppression for Geared In-Wheel-Motor Vehicles Using Double Encoder

In-wheel-motor type electric vehicles (IWM-EVs), which have independent motors inside each of the four wheels, are expected to be the next power-trains because they have quite good motion-control performance. From the viewpoint of commercial use, a reduction gear must be inserted between the motor and the wheels to increase the driving torque of the wheels, but doing so drastically declines the control performance because of gear elasticity and backlash. Moreover, the reduction gear not only obstructs the smooth transmission of the driving torque but also leads to unpleasant noise or ride-comfort deterioration. Here, we propose the gear-collision-free backlash-compensation and the joint-torque-control method for geared IWM-EVs by using a double encoder. Currently, the concepts of the double-encoder system are prevailing as an effective control method for the two-inertia systems in the motion-control societies. We have introduced these schemes skillfully and constructed a simple and practical control strategy. The suitable mechanical system of IWM and angular sensor resolution are also discussed, and its effectiveness is demonstrated via some simulations and experiments. We anticipate this research to solve the critical problem of the gear noise and facilitate the practical realization of IWM-EVs.

Fault Diagnosis of Underwater Vehicle Propulsion System Based on Deep Learning

Wang, Y. and Li, Y.-R., 2020. Fault diagnosis of underwater vehicle propulsion system based on deep learning. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 65-68. Coconut Creek (Florida), ISSN 0749-0208.The 21st century is the marine era. Marine economy has played an important role in promoting global economic development, which requires us to continuously develop marine resources. The underwater vehicle is an important tool for human to understand the ocean and explore the marine resources, which has a good application prospect in the exploration of marine environment, resource development and military applications. With the development of ocean strategy, the safety and stability of underwater robot becomes the most important problem, which requires us to improve the performance of the work. Propulsion system is the core of the underwater robot, which requires us to do a good job in motion control and fault diagnosis. However, in the complex marine environment, the underwater robot will be affected by many aspects, which will cause the diversity of propulsion system fault. How to effectively identify and diagnose fault types has become the most important problem, which will help us to improve the motion control performance and survivability of underwater robots.

Data-Driven Multiobjective Controller Optimization for a Magnetically Levitated Nanopositioning System

The performance achieved with traditional model-based control system design approaches typically relies heavily on accurate modeling of the motion dynamics. However, modeling the true dynamics of present-day increasingly complex systems can be an extremely challenging task; and the usually necessary practical approximations often renders the automation system to operate in a nonoptimal condition. This problem can be greatly aggravated in the case of a multiaxis magnetically levitated (maglev) nanopositioning system where the fully floating behavior and multiaxis coupling make extremely accurate identification of the motion dynamics largely impossible. On the other hand, in many related industrial automation applications, e.g., the scanning process with the maglev system, repetitive motions are involved which could generate a large amount of motion data under nonoptimal conditions. These motion data essentially contain rich information; therefore, the possibility exists to develop an intelligent automation system to learn from these motion data, and to drive the system to operate toward optimality in a data-driven manner. Along this line then, this article proposes a data-driven model-free controller optimization approach that learns from the past nonoptimal motion data to iteratively improve the motion control performance. Specifically, a novel data-driven multiobjective optimization approach is proposed that is able to automatically estimate the gradient and Hessian purely based on the measured motion data; the multiobjective cost function is suitably designed to take into account both smooth and accurate trajectory tracking. In this article, experiments are then conducted on the maglev nanopositioning system to demonstrate the effectiveness of the proposed method, and the results show rather clearly the practical appeal of our methodology for related complex robotic systems with no accurate model available.

Target-centric angle variables in pursuit tracking

A method for constructing a dynamic local reference frame centered at the location of a pursuit target is proposed. It uses original fixed reference frame coordinates for three positions: the location of a moving target, the location of a pursuing entity, and a third reference point that defines the instantaneous target trajectory direction. The method involves rigid body geometric operations and creates a consistent local reference frame configuration having its origin at the target location. The local frame also has the target trajectory pointing in the positive x axis direction, and a defined azimuth plane descriptive of the current pursuer location. This plane is determined by a concluding local frame rotation around the set x axis, which specifies a particular y axis direction. If the pursuer position is not on the local frame’s azimuth plane, its perpendicular projection on to that plane serves as an additional position for use in defining angle variables. Three angle variables relevant to the pursuit dynamics (not all mutually independent) are obtained from the three coordinate values of the pursuer position in the local frame: an elevation angle, an azimuth angle, and a bearing angle that basically describes how far off course the pursuer position is from being directly behind the target’s current trajectory path position. To illustrate the method of obtaining these angles and to demonstrate their potential usefulness in characterizing pursuit behavior, example experimental data from a motor control and coordination pursuit task performance are presented and analyzed.

Motion control of a space manipulator using fuzzy sliding mode control with reinforcement learning

The free-flying space manipulators present challenges in controlling the motions of both the spacecraft bus and the manipulator, because of the highly-coupling system dynamics and the unknown space environment disturbances. Although the sliding mode controllers are robust to the unknown disturbances and system uncertainties, the chattering effect could affect the pointing accuracy and the lifetime of the actuators. This paper first introduces the dynamics of a CuBot, which is a 3-rigid-link manipulator based on the CubeSat platform. To maintain the robustness while decreasing the chattering effect, an innovative reinforcement learning based fuzzy adaptive sliding mode controller is proposed. To maintain the robustness while reducing the chattering effect, an innovative reinforcement learning based fuzzy adaptive sliding mode controller is proposed. The switching gain is updated to estimate the lumped upper bound of the system uncertainties and the unknown disturbances, and then a new fuzzy logic adaptive law is applied on the switching gain to decrease the chattering effects. On top of that, the fuzzy logic rules are tuned by an innovative modified reinforcement learning mechanism to achieve the better tracking performance. The uniformly ultimately bounded tracking errors are guaranteed by the proposed control scheme, and the effectiveness is validated by the simulation results.

Recursive sliding mode control with adaptive disturbance observer for a linear motor positioner

The control performance of linear motor (LM) is deteriorated by payload variations, friction, and external disturbances. In this paper, a robust recursive sliding mode controller combined with an adaptive disturbance observer (RSM-ADO) is proposed for the high-speed and high-precision control of an LM positioner. The benefits of the proposed ADO lie in that it can be designed without the need for the upper bound information of the disturbance and its derivative. Hence, the ADO is ideally capable of rejecting any time-varying disturbances. Furthermore, a recursive integral sliding surface is constructed for the RSM controller such that the reaching phase is eliminated. Benefiting from the proposed recursive structure, the tracking error can converge to zero in finite time. Besides, system chattering is eliminated in the reaching control input due to the integral element. Lyapunov analysis is investigated to prove the finite time convergence of the tracking error under the proposed RSM-ADO control scheme. Experiments demonstrate the superior property of stronger robustness and fewer chattering effects of the proposed method compared to existing disturbance observers and adaptive recursive terminal sliding mode (ARTSM) controller.

A Fault-Tolerant Method for Motion Planning of Industrial Redundant Manipulator

Nowadays, industrial redundant manipulators have been playing important roles in manufacturing fields, such as welding and assembling, by performing repetitive and dull work. Such long-term industrial operations usually require redundant manipulators to keep good working conditions and maintain steadiness of joint actuation. However, some joints of redundant manipulators may fall into fault status after enduring long-period heavy manipulations, causing that the desired industrial tasks cannot be accomplished accurately. In this article, we propose a novel fault-tolerant method with simultaneous fault-diagnose function for motion planning and control of industrial redundant manipulators. The proposed approach is able to adaptively localize which joints run away from the normal state to be fault, and it can guarantee to finish the desired path tracking control even when these fault joints lose their velocity to actuate. Simulation and experiment results on a Kuka LBR iiwa manipulator demonstrate the efficiency of the proposed fault-tolerant method for motion control of the redundant manipulator.

Comprehensive modeling and identification of nonlinear joint dynamics for collaborative industrial robot manipulators

For collaborative robots, the ability to accurately predict the actuator torques required to realize the desired task is highly important. This will improve and guarantee the safety, motion and force control performance, and smooth lead-through programming experience. Thus, this paper presents the investigation towards comprehensive modeling and identification of nonlinear joint dynamics for collaborative robots. The proposed joint dynamics model and identification describes the most dominant dynamic characteristics of robot joints that comprise strain-wave transmissions, such as nonlinear friction, nonlinear stiffness, hysteresis, and kinematic error. Position-dependent backlash characteristics is observed and quantified using our proposed identification method and the Generalized Maxwell-Slip friction model is extended to describe the observed phenomena. The developed dynamic modeling and identification procedures provides insightful guidance for the design and model-based control of collaborative robots.

A spiking network classifies human sEMG signals and triggers finger reflexes on a robotic hand

The interaction between robots and humans is of great relevance for the field of neurorobotics as it can provide insights on how humans perform motor control and sensor processing and on how it can be applied to robotics. We propose a spiking neural network (SNN) to trigger finger motion reflexes on a robotic hand based on human surface Electromyography (sEMG) data. The first part of the network takes sEMG signals to measure muscle activity, then classify the data to detect which finger is being flexed in the human hand. The second part triggers single finger reflexes on the robot using the classification output. The finger reflexes are modeled with motion primitives activated with an oscillator and mapped to the robot kinematic. We evaluated the SNN by having users wear a non-invasive sEMG sensor, record a training dataset, and then flex different fingers, one at a time. The muscle activity was recorded using a Myo sensor with eight different channels. The sEMG signals were successfully encoded into spikes as input for the SNN. The classification could detect the active finger and trigger the motion generation of finger reflexes. The SNN was able to control a real Schunk SVH 5-finger robotic hand online. Being able to map myo-electric activity to functions of motor control for a task, can provide an interesting interface for robotic applications, and a platform to study brain functioning. SNN provide a challenging but interesting framework to interact with human data. In future work the approach will be extended to control also a robot arm at the same time.