Motion Control Experiments Research Articles

Research on underwater motion modeling and closed-loop control of bionic undulating fin robot

Aiming at the problems of unclear underwater motion characteristics and poor robustness of bionic undulating fin robots, the underwater motion characteristics of the robot are studied by force analysis and numerical simulation, and the dynamic model is established. Based on the dynamic model, the proportional-integral-derivative controller (PID) and neural network PID simulation model are established for numerical simulation, and the robot's underwater closed-loop motion control experiment is carried out. The accuracy of the dynamic model and the consistency between the numerical simulation and the experimental results can be verified. The results indicate that under the function of the neural network PID controller, the robot can be closer to the target depth, and the fluctuation range could be smaller. As the above characteristics improve, the control accuracy and stability of the robot could also be enhanced.

Microsecond-level Real-time Ethernet Deterministic Bus (REDBUS): Architecture and motor control experiments

Microsecond-level Real-time Ethernet Deterministic Bus (REDBUS): Architecture and motor control experiments

Static-to-kinematic modeling and experimental validation of tendon-driven quasi continuum manipulators with nonconstant subsegment stiffness

Continuum robots with high flexibility and compliance have the capability to operate in confined and cluttered environments. To enhance the load capacity while maintaining robot dexterity, we propose a novel non-constant subsegment stiffness structure for tendon-driven quasi continuum robots (TDQCRs) comprising rigid-flexible coupling subsegments. Aiming at real-time control applications, we present a novel static-to-kinematic modeling approach to gain a comprehensive understanding of the TDQCR model. The analytical subsegment-based kinematics for the multisection manipulator is derived based on screw theory and product of exponentials formula, and the static model considering gravity loading, actuation loading, and robot constitutive laws is established. Additionally, the effect of tension attenuation caused by routing channel friction is considered in the robot statics, resulting in improved model accuracy. The root-mean-square error between the outputs of the static model and the experimental system is less than 1.63% of the arm length (0.5 m). By employing the proposed static model, a mapping of bending angles between the configuration space and the subsegment space is established. Furthermore, motion control experiments are conducted on our TDQCR system, and the results demonstrate the effectiveness of the static-to-kinematic model.

Modelling of Lake Waves to Simulate Environmental Disturbance to a Scale Ship Model

Abstract In the development of ship motion control systems, software simulations or scale model experiments in pools or open water are very often carried out in the verification and testing stages. This paper describes the process of building a software wave simulator based on data gathered on the Silm Lake near Iława, Poland, where scale ship models are used for research and training. The basis of the simulator structure is a set of shaping filters fed with Gaussian white noise. These filters are built in the form of transfer functions generating irregular wave signals for different input wind forces. To enable simulation of a wide range of wind speeds, nonlinear interpolation is used. The lake wave simulation method presented in this paper fills a gap in current research, and enables accurate modelling of characteristic environmental disturbances on a small lake for motion control experiments of scale model ships.

Design and Experiments of Angular Motion Control for a Novel Unmanned Water-Powered Aerial Vehicle

Design and Experiments of Angular Motion Control for a Novel Unmanned Water-Powered Aerial Vehicle

FPGA-based Control Design and Implementation of DELTA robots Using Horizontal-Axial Pneumatic Actuators

This study researched, analyzed, and applied a field-programmable gate array (FPGA) to develop a control system for the DELTA robot with three pneumatic actuation subsystems. To achieve positioning accuracy, a dual-feedback control framework is first applied to control the position of a rodless pneumatic actuator. The inner pressure difference between the actuator and cylinder was used as feedback signals. Then, the derived dynamic model is included in the control strategy to conduct decoupling and system linearization on the nonlinear parallel manipulator using inverse dynamic control. Additionally, to realize the operation of the integrated control system under the FPGA environment, a multilayered neural network framework is designed to learn the inverse dynamic control behaviors. The motion control experiments involving single- and three-axis translational parallel manipulators revealed that the established FPGA-based control system exhibits high precision over the reachable workspace regarding the DELTA robot's three-dimensional trajectory tracking control.

Partial Lagrangian for Efficient Extension and Reconstruction of Multi-DoF Systems and Efficient Analysis Using Automatic Differentiation

In the fields of control engineering and robotics, either the Lagrange or Newton–Euler method is generally used to analyze and design systems using equations of motion. Although the Lagrange method can obtain analytical solutions, it is difficult to handle in multi-degree-of-freedom systems because the computational complexity increases explosively as the number of degrees of freedom increases. Conversely, the Newton–Euler method requires less computation even for multi-degree-of-freedom systems, but it cannot obtain an analytical solution. Therefore, we propose a partial Lagrange method that can handle the Lagrange equation efficiently even for multi-degree-of-freedom systems by using a divide-and-conquer approach. The proposed method can easily handle system extensions and system reconstructions, such as changes to intermediate links, for multi-degree-of-freedom serial link manipulators. In addition, the proposed method facilitates the derivation of the equations of motion-by-hand calculations, and when combined with an analysis algorithm using automatic differentiation, it can easily realize motion analysis and control the simulation of multi-degree-of-freedom models. Using multiple pendulums as examples, we confirm the effectiveness of system expansion and system reconstruction with the partial Lagrangians. The derivation of their equations of motion and the results of motion analysis by simulation and motion control experiments are presented. The system extensions and reconstructions proposed herein can be used simultaneously with conventional analytical methods, allowing manual derivations of equations of motion and numerical computer simulations to be performed more efficiently.

A general motion control architecture for an autonomous underwater vehicle with actuator faults and unknown disturbances through deep reinforcement learning

This paper studies the application of deep reinforcement learning (RL) in the motion control tasks of an underactuated AUV with actuator faults and unknown disturbances. Firstly, a general state space and action space that can be applied to a variety of motion control tasks are customized. Furthermore, a universal reward function is designed to minimize the energy consumption of the AUV under the condition that the learned optimal control policy can achieve fast and high-precision control. Then, a simulation method with random reference values and simulated random disturbances is given to train the deep RL algorithm. In order to deploy the optimal control policy obtained from the simulation training directly to an actual AUV, we design five extended state observers (ESOs) to estimate the unknown disturbances in different directions, and take the estimated values as the disturbance states required to obtain the optimal control action. Combined with these ideas, a general AUV motion control architecture with simulation training and deployment process is developed. Finally, four different AUV motion control experiments are carried out, and the results confirm the generality and effectiveness of our architecture.

System and Experiments of Model-Driven Motion Planning and Control for Autonomous Vehicles

This article presents a model-based motion planning and control system for autonomous vehicles and its experimental validation. The system consists of four modules: 1) global routing; 2) behavior planner; 3) local trajectory generation; and 4) trajectory tracking. The algorithm and software of each module are detailed, including a behavior planner with unified models to handle typical scenarios in both highway and urban driving, a deterministic sampling algorithm for robust responsive trajectory generation, and a dynamics-and-delay-aware preview algorithm to achieve accurate trajectory tracking. The developed system is implemented and tested at the Mcity test facility with a full-size automated car and a dozen of challenging traffic scenarios.

Evaluation of a Portable fMRI Compatible Robotic Wrist Interface.

This paper presents evaluation of a portable fMRI compatible haptic interface to study the brain correlates of sensorimotor control during wrist motion. The interface is actuated by a shielded DC motor located more than 2 m away from the 3T MR scanner's bore. The achievable wrist torque of the interface is up to 2 Nm, and the interface provides sufficient bandwidth for human motor control experiments. Ergonomic and fMRI compatibility testing with a 3T MR scanner showed that the interface is MR safe, compatible with a strong static magnetic field and radio frequency emission, and its operation does not affect the quality of the acquired images. Clinical Relevance- We present and evaluate an fMRI compatible robotic interface to study human wrist joint motor function.

Control Strategy of Pipeline Robot

The detection of pipeline in service is very important, but the existing manual detection has blind spots, long cycle, high risk, low precision and high cost. Therefore, it is very important to develop a mobile detection robot to replace manual inspection. This paper mainly introduces the control strategy of pipeline robot. In this paper, the overall control structure of the pipeline robot system is built, including the design of hardware framework and the design of software implementation process. Hardware design includes the selection design of microcontroller ARM, sensor, motor driver and so on. The software design is mainly the process design of action realization and the frame design of man-machine interactive operation panel structure. After the system construction is completed, the motion control experiment of the pipeline robot control system is carried out to test the walking performance of the pipeline robot prototype in the vertical variable diameter pipeline, and the motion control experiment is carried out to verify the rationality of the mechanical structure of the pipeline robot and the practicability of the control system.

A small-scale robotic manipulandum for motor control study with rodents

Computational models of motor learning have been examined experimentally with human participants using a robotic manipulandum that produces force perturbations on their reach trajectories. Here, we developed a small-scale robotic manipulandum for rodent studies. This device enabled us to conduct the same motor control experiments typically used for human study on rodents in order to examine the neural basis of the computational process of motor control and learning. We present the design of the manipulandum and controller system in addition to kinematic analysis of the link structures. We also confirmed the feasibility of the developed system for rodent studies.

Optimal controller applied to robotic systems using covariant control equations

ABSTRACT We elaborate an algorithm to optimally control the actuators of robot manipulators. By regarding the robot kinetic energy as a metric, the dynamic model is formulated using tensors in a Riemannian geometry manifold. The control criterion is selected to be invariant, leading to covariant control equations that take advantage of the system dynamics. Motion equations are associated with conjugate control equations resulting in a system of second order ordinary differential equations. The suggested control method consists in solving this system of equations to obtain optimal joint forces off-line and use them as inputs in a control scheme to track optimal trajectories. Position-velocity feedback ensures proper behaviour. Motion control simulations show that the optimal trajectory can be tracked with good accuracy, even with imposed perturbations. A stability analysis for switched systems precedes real-time motion control experiments conducted on a serial robot manipulator with revolute joints to validate our method.

Research on Motion Control for a Mobile Robot Using Learning Control Method

Abstract Precise motion control is a challenging and important goal in the application of mobile robots. The mechanical structure of a novel mobile robot is presented. Using the support vector machine learning control method in statistical theory, the human control strategy is represented by the parametric model without knowledge of the actual robot mathematical model. Moreover, using the learning controller, the position motion control experiments of the robot are carried out. The results of the experiments show that this learning control method is feasible and valid for the precise position control of the mobile robot, and the maxim error is less than 32 cm in a 10 m linear movement.

Design and control of a novel magnetic resonance imaging-compatible breast intervention robot

Breast cancer is one of the most frequent cancers and a major cause of cancer death in women. In this article, the design and control of a novel magnetic resonance imaging-compatible breast intervention robot are proposed. The dimensions and tolerance of the robot system are considered, and a novel pitching mechanism is designed to achieve a dexterous operation in the limited space. The magnetic resonance imaging compatibility of the robot materials is tested. The nonmagnetic structure and compact Cartesian mechanism of the robot allow it to operate safely in a magnetic resonance imaging scanner. According to the robot’s structure, a kinematics analysis based on a coupled motions model is established. The workspace simulation analysis of the robot proves that it is suitable for the whole breast surgery. To control the needle insertion tasks, the overall control system in the form of “personal computer (PC) + single-chip micyoco (SCM)” is designed. Finally, the motion control experiment is carried out, and the robot positioning error is 0.37 mm, which proves that the breast intervention robot and its control system designed in this article can meet the requirements of breast intervention.

Experiments of Motion Control of a Wall Climbing Robot

Experiments of Motion Control of a Wall Climbing Robot

Experimental and numerical study of autopilot using Extended Kalman Filter trained neural networks for surface vessels

Due to the nonlinearity and environmental uncertainties, the design of the ship's steering controller is a long-term challenge. The purpose of this study is to design an intelligent autopilot based on Extended Kalman Filter (EKF) trained Radial Basis Function Neural Network (RBFNN) control algorithm. The newly developed free running model scaled surface vessel was employed to execute the motion control experiments. After describing the design of the EKF trained RBFNN autopilot, the performances of the proposed control system were investigated by conducting experiments using the physical model on lake and simulations using the corresponding mathematical model. The results demonstrate that the developed control system is feasible to be used for the ship's motion control in the presences of environmental disturbances. Moreover, in comparison with the Back-Propagation (BP) neural networks and Proportional-Derivative (PD) based control methods, the EKF RBFNN based control method shows better performance regarding course keeping and trajectory tracking.

Design and Analysis of a Drive System for a Series Manipulator Based on Orthogonal-Fuzzy PID Control

Because the proportional–integral–derivative (PID) parameters selected by experience are random, the control effect of fuzzy PID cannot be optimized. In order to improve the accuracy and stability of robot motion control, an orthogonal-fuzzy PID intelligent control method is proposed. In this paper, the electric steering gear is used as the actuator, and the mathematical model of the servo motor joint drive system is established. The simulation analysis of the original control, PID control, fuzzy PID control, and orthogonal-fuzzy PID control of the manipulator joints in the Simulink software simulation environment and the motion control experiment of the manipulator show that using the orthogonal test method to adjust the PID parameters can quickly determine the appropriate PID parameters and greatly reduce the number of trials. The rise time, adjustment time, and overshoot of the system are significantly reduced by using fuzzy PID control, which can improve the adaptability of the system. By comparing and analyzing fuzzy PID and orthogonal-fuzzy PID control methods, it can be found that the system of orthogonal-fuzzy PID for optimal factor level combination (Kp = 0.1, Ki = 30 and Kd = 0.02) is the optimal system. The experiment results show that the orthogonal-fuzzy PID can further improve the accuracy of the system and reduce the oscillation process of the system near the steady state and make the motion more stable.

A workbench for motion control experiments using programmable automation controllers in industrial automation laboratory at Kongu Engineering College

AbstractMechatronics Engineering has become an internationally recognized discipline with degree programmes offered in majority of the universities all around the world. Integrating mechanical, instrumentation, and programmable automation controller (PAC) for developing technical skills and expertise in system level integration to attain manufacturing and controller programming superiority has become the basic requirement for Mechatronics Laboratory. Divulging the graduates to servo control concept down the line to the programmable automation controller helps to gain the practical knowledge in the field of I/O interfacing, closed loop control, remote monitoring, and PLC motion control programming. Hence in this paper, a 3‐axis servo control system with HMI work bench case study has been presented to enable the graduates to learn multi‐disciplinary concepts in the field of material handling and precise position synchronous control of multi‐axis rotating system. With this work bench, applications of servo control such as electronic gearing (positioning), pick and place (material handling), and profiling (welding, machining) can be exposed to mechatronics graduates. Graduates with fundamental programming skills and apparently no‐practical experience with mechatronics systems can quickly learn how to incorporate control architectures with this work bench and prepare them to compete in Industry 4.0 environment.

Design and Experimental Testing of a Free-Running Ship Motion Control Platform

In recent years, ship motion control has been used in unmanned merchant ships. It is a key issue in the sailing performance and navigation cost of unmanned vessels. The merchant ship trajectory and attitude are always generated from the response mechanism for ship motion, which uses one rudder and one propeller as the actuator. Therefore, it is a typical underactuated ship. However, there is not a specialized control test platform for unmanned merchant ships, in particular container ships. A precise merchant ship motion control platform is crucial; its computing power and motion attitude monitoring are difficult to solve by ship-borne computers. To solve this problem, a new free-running ship motion control platform is proposed, which realizes ship attitude monitoring and various motion control experiments. The proposed platform is novel as it embeds ship motion parameters and control parameters in the ship motion model, which successfully solves the precise control of the ship motion control experiment with linear or nonlinear dynamics. In this paper, the design scheme of the ship motion control platform is first presented. Then, the operating principle of the platform is shown, and the adaptive model of the ship plane motion is selected. A series of free-running model experiments and simulations is conducted to test performance of the new platform. To this end, the proposed platform can meet the requirements.