Position Control Method Research Articles

Table tennis ball landing control in a robotic system by cameras

Abstract Controlling the landing position of a spinning ball is difficult when using a table tennis robot. A complete physical model requires the factoring in of aerodynamic elements and object collisions, and inaccurate environmental coefficients would increase the landing position error. This study proposed a landing position control method based on a cascade neural network (CNN) that consists of forward and recurrent neural networks (RNNs). The forward NNs are used to estimate the velocity of the outgoing ball according to the velocity and acceleration of the incoming ball captured by cameras and the desired velocity of the outgoing ball. The RNN is employed to reverse-predict ball displacement based on the state of the incoming ball, desired landing point, and ball flight duration. The experiments verified that the method proposed in this study achieved control of differently spinning balls more effectively than the locally weighted regression (LWR)-based model did. The success rate of the CNN at two of six desired landing points was 25.9% and 32.9% higher, respectively, compared with use of the LWR-based model.

Vehicle posture wheelbase preview control of in-wheel motors drive intelligent vehicle on potholed road

The in-wheel motors drive intelligent vehicle (IMDIV) has a strong ability of dynamic control, which makes it be a major development path of intelligent vehicles in the future. However, its stability is easily deteriorated by the impact of the road when driving on potholed road. To tackle this issue, a vehicle posture control method based on road feedback and elevation recognition is proposed. Firstly, an elevation recognition algorithm of adaptive Kalman filter (AKF) based on vehicle dynamic response is proposed after considering the flooded road and the system noise uncertainty. Secondly, a vehicle posture controller of fuzzy active disturbance rejection control (FADRC) is designed, with the function of dynamically adjusting the active disturbance rejection control (ADRC) parameters. It is designed to implement front suspension feedback control and rear suspension wheelbase preview control. At last, the road elevation recognition algorithm and vehicle posture control method are proved by Simulink simulation and off-line hardware in the loop test. The results revealed that the AKF can effectively recognize both white noise pavement and potholed road, and the recognition accuracy is greater than 90%. The FADRC controller realizes accurate front suspension feedback control and rear suspension wheelbase preview control by adjusting the real-time online parameters of ADRC. The proposed vehicle posture control method can effectively control the pitch and roll motion, which significantly improves the stability of IMDIV on potholed road. This study can serve as a reference for the posture stability control of IMDIV on potholed road.

A Model-Free Adaptive Positioning Control Method for Underactuated Unmanned Surface Vessels in Unknown Ocean Currents

Aiming to address the problem of underactuated unmanned surface vehicles (USVs) performing fixed-point operations at sea without dynamic positioning control systems, this paper introduces an original approach to positioning control: the virtual anchor control method. This method is applicable in environments with currents that change slowly and does not require prior knowledge of current information or vessel motion model parameters, thus offering convenient usability. This method comprises four steps. First, a concise linear motion model with unknown disturbances is proposed. Then, a motion planning law is designed by imitating underlying principles of ship anchoring. Next, an adaptive disturbance observer is proposed to estimate uncertainties in the motion model. In the last step, based on the observer, a sliding-mode method is used to design a heading control law, and a thrust control law is also designed by applying the Lyapunov method. Numerical simulation experiments with significant disturbances and tidal current variations are conducted, which demonstrate that the proposed method has a good control effect and is robust.

Robust H-Infinity Dual Cascade MPC-Based Attitude Control Study of a Quadcopter UAV

Aimed at the stability problem of quadrotor Unmanned Aerial Vehicle (UAV) flight attitudes under random airflow disturbance conditions, a robust H-infinity-based dual cascade Model Predictive Control (MPC) attitude control method is proposed. Model Predictive Control itself has the capability to minimize the deviation between the prediction error and the control target by optimizing the control algorithm. The robust H-infinity controller can maintain stability in the face of system model uncertainty and external disturbances. The controller designed in this paper divides the attitude control loop into the following two parts: the angle loop and the angular velocity loop. The angle loop, serving as the main control loop of the attitude control, employs the robust H-infinity controller to process the angle of the quadrotor UAV and then transmits the processed value to the MPC controller. This approach reduces the computational load of the MPC controller. Simultaneously, by optimizing the algorithm, MPC minimizes the prediction error and the deviation from the control target. Simulation experiments confirm that the proposed algorithm improves the stability of the UAV attitude under random airflow disturbance conditions, while also achieving accurate tracking of the UAV’s position.

Predefined-time sliding mode position and attitude coupling control for in-cabin robots on space stations

As space science advances, the significance of space robotics research escalates, particularly in control methodologies. Addressing the work efficiency requirements during the mission of the in-cabin robot, it is necessary to develop a fast and robust position and attitude coupling controller for in-cabin robots. This paper presents a predefined-time sliding mode position and attitude coupling control method, employing the SE(3) frame description. The first, the dynamic model of the in-cabin robot is developed within the SE(3) framework. Then, the predefined-time position and attitude coupling sliding mode controller is designed with a sliding surface based on the dynamic error model, and a hyperbolic tangent function is used to suppress system chattering. In addition, the stability of the proposed control method is rigorously confirmed using the Lyapunov theorem, ensuring that the convergence time is accurately predefined. Finally, simulation results are provided to demonstrate the effectiveness of this novel control strategy. A comparative analysis with fixed-time sliding mode control highlights its superior performance.

A composite motion control method for quadruped robots in trot gait

This work aims to address the issues of instability in trot gait walking and poor terrain adaptability in rugged environments under conventional control strategies for quadruped robots by proposing a novel composite control strategy. During the support phase, a separated force-position mixing control method is employed, utilizing gravity-compensated PD (Proportional Derivative) control at the lateral swing and knee joints while employing position control at the hip joint. During the swing phase, VMC (Virtual Model Control) is used to construct three sets of virtual spring-damper components, guiding the foot to move along a predetermined trajectory and converting virtual forces into joint torques for the swinging leg. At the body, IMU (Inertial Measurement Unit) feedback data combined with PI (Proportional Integral) control is used to adjust leg length and maintain the robot’s posture stability. Finally, the proposed separated force-position hybrid control method is theoretically validated using the Lyapunov stability criterion, and simulation tests on flat and rugged terrains under the quadruped robot’s composite control method are conducted using MATLAB/Simulink, comparing it with the VMC and position control methods. The findings indicate that the composite control strategy leads to smaller changes in the attitude angles and reduced impact forces at the feet during the quadruped robot’s walk on flat ground while also demonstrating strong adaptability to rugged terrains.

Large-Scale Solar-Powered UAV Attitude Control Using Deep Reinforcement Learning in Hardware-in-Loop Verification

Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash.

Auditory perception based milling posture detection and depth control enhancement for orthopedic robots

The use of robots for bone milling is thought to improve surgical efficiency and reduce surgeon fatigue. Surgeons can use their hearing to perceive the milling state and adjust the position of surgical tools. Inspired by this, this paper proposes an automatic tool position control method based on acoustic signal feedback. Firstly, the mapping model between milling depth and sound energy was calibrated through experiments, and the influence of different angles on depth estimation accuracy was tested. Then, a network of CNN-GRU with an attention mechanism is designed to realize milling angle recognition using acoustic features as inputs. Finally, a milling depth automatic control framework with angle optimization was designed and validated through relevant experiments. The results of the experiment showed that the depth estimation error was larger when the milling angle of ball end milling tools (BMT) was within 70-90°. After adding the angle adjustment function, the milling depth accuracy in angle interval 1 (80-90°) and angle interval 2 (70°-80°) increased by about 30% and 10%, respectively.

Attitude collaborative control strategy for space gravitational wave detection

This paper proposes a high-precision attitude cooperative control strategy for three spacecraft in space gravitational wave detection. Firstly, based on the layout of optical components on the spacecraft and factors such as external interference and model uncertainty, a relative attitude model based on the Euler angle is constructed for the characteristics of small angle motion in differential wavefront sensing mode. Secondly, aiming at the cooperative control problem of multi drag-free attitude control system after the laser link capture between two spacecraft is completed, an attitude control method based on consensus mode cooperative control strategy is proposed. It combines local neighbourhood error from star sensing and differential wavefront sensing with an adaptive controller for equilateral triangle configuration control. Additionally, a cooperative control strategy based on laser measurement information is established to improve control accuracy by avoiding star sensors with low accuracy. Simulation results indicate that the control accuracy of the consistency mode and its sub-mode laser measurement mode is superior to the master-slave and cyclic modes, achieving better than 5×10−6 rad. The laser measurement mode demonstrates the highest accuracy, with relative attitude control accuracy reaching 1.5×10−7 rad, meeting the requirements for space gravitational wave detection.

Natural mechanism of superexcellent vibration isolation of the chicken neck

No matter how the chicken's body shakes, its head can always remain relatively motionless, leading to the intuitive impression that the S-configuration of the chicken neck possesses an unparalleled vibration isolation capability. However, this viewpoint has not been rigorously substantiated. To unravel the vibration isolation mechanism inherent in the chicken neck, this study introduces a multi-modular S-configurational model inspired by its biological structure. Utilizing Newton's law and Lagrange's equations, both the static and dynamic models for the S-configurational model are formulated. Furthermore, we incorporate actuators into the model and develop a bionic attitude control method. Surprisingly, numerical simulations reveal that the S-configuration of the chicken neck does not exhibit any passive vibration isolation function, challenging the intuitive impression. Instead, the motionless of a chicken head results from active control implemented by the corresponding chicken neck. This study may contribute to a better understanding of the vibration attenuation function of the neck of birds.

Position control of a soft pneumatic actuator based on the pressure parameter feedback model (PPFM)

Abstract. Soft pneumatic actuators have been one of the cores of soft robotics research and play a key role in driving the development of soft robots. Due to its high degree of internal nonlinearity and unpredictable deformation caused by environmental influences, the control model established for soft robots is still a difficult problem in terms of improving accuracy. This paper proposes a new positional control method for soft pneumatic actuators that are suitable for independent 3D deformation at any position and are the core units of continuous robots. The pressure parameter feedback model (PPFM) of the airbag is obtained by adjusting the pressure input through a proportional valve, collecting the air pressure inside the airbag and obtaining the airbag expansion height. The pressure input signal is changed according to the PPFM of the airbag to control the position of the soft pneumatic actuator. A modular experimental platform is built to validate the PPFM-based control strategy, which is able to adjust the position of the end center point of the soft pneumatic actuator in space with the discussed characteristics. It is demonstrated that the theoretical model can significantly improve the stability and accuracy of the soft pneumatic actuator motion.

Research on Attitude Analysis of Hydraulic Support Based on Column Length

The existing hydraulic support attitude monitoring and control methods integrate multiple sensing technologies, but it is difficult to apply and promote due to unclear monitoring mechanisms, complicated types, and a large number of sensors. Aiming at the problem of attitude monitoring and control of hydraulic support, firstly, the kinematics mathematical model of hydraulic support is established by the key marker point method and the two-dimensional bar system model, and the forward and inverse solution algorithm of hydraulic support attitude based on the double-drive length of front and rear columns is proposed. Secondly, the rigid body dynamics simulation model of hydraulic support is constructed to verify the theoretical analytical model and prove the correctness of the algorithm. The rigid body simulation model can be used as the attitude monitoring system of hydraulic support. Thirdly, considering the influence of material elastic deformation, a rigid-flexible coupling dynamic simulation model of hydraulic support is established to explore the influence of elastic deformation of components on the attitude of hydraulic support and its causes. On this basis, the rigid-flexible coupling dynamic simulation model of the hydraulic support with clearance is established by replacing the revolute pair in the simulation model with the solid axis pin connection unit. The influence of the axis pin connection clearance on the attitude of the hydraulic support and its causes are explored, and the actual attitude and change characteristics of the hydraulic support under simulated real conditions are obtained. Finally, considering the force compression of the hydraulic cylinder of the column, the rigid-flexible and machine-liquid coupling dynamic simulation model of the hydraulic support with clearance is constructed.

Robust generalised predictive position control for chain‐type rotary shell magazine with disturbance observer

AbstractThe realisation of fast position tracking control and strong robust control of the chain‐type rotary shell magazine in the complex systems such as the large calibre howitzer has been the focus and challenge of research. The predictive control strategies can achieve a fast dynamic response, but it relies on the system model. By integrating the generalised predictive control method with sliding mode theory, a novel robust generalised predictive position control method is proposed. Firstly, a non‐cascade position tracking controller is designed based on the continuous‐time model of the systems; then, a sliding mode compensation structure is introduced to address the degradation of control performance due to load variations and external disturbances. The scheme utilises the sliding mode switching term to overcome the effects caused by the disturbances while preserving the fast dynamic response characteristics of the original predictive control. Moreover, the disturbance observer is designed to further enhance the robustness by producing corresponding compensation according to the perturbation quantity. The proposed controller has been validated in a shell magazine test bench, indicating its superior position control performance of the shell magazine under different load conditions.

Fully Autonomous Brick Pick and Place in Fields by Articulated Aerial Robot: Results in Various Outdoor Environments

Picking and placing of objects by aerial robots in fields is an important and challenging task for a range of outdoor activities, including industry and rescue operations. General strategies depend on the magnetic force to the pick object; however, this approach lacks both generality and robustness. Therefore, we focus on an articulated structure to grasp bricking. Another issue in performing pick-and-place tasks in fields is autonomous recognition using onboard sensors. In this article, we present a fully autonomous pick-and-place scheme in outdoor environments using articulated aerial robots. First, an articulated robot model with an actively tiltable sensor is developed to guarantee robustness in both state estimation and object detection. Second, object detection methods are designed according to the distance between the robot and target object. Third, a comprehensive motion strategy is developed to perform an autonomous object searching, picking, and placing sequence. In particular, a visual servoing method for robot position control is also proposed in this motion strategy to improve the robustness while approaching the target. Finally, we present the experimental results of autonomous brick picking and placing by our proposed methods in various outdoor environments, including a real robotics challenge, the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020). To the best of our knowledge, our study represents the first successful attempt at fully autonomous object grasping by an articulated aerial robot in a field.

Image moment-based visual positioning and robust tracking control of ultra-redundant manipulator

Image moment features can describe more general target patterns and have good decoupling properties. However, the image moment features that control the camera’s rotation motion around the x-axis and y-axis mainly depend on the target image itself. In this paper, the ultra-redundant manipulator visual positioning and robust tracking control method based on the image moments are advocated.First, six image moment features used to control camera motion around the x-axis and around the y-axis are proposed. And then, a novel method is proposed to use to select image features. For tracking a moving target, a kalman filter combined with adaptive fuzzy sliding mode control method is proposed to achieve tracking control of moving targets, which can estimate changes in image features caused by the target’s motion on-line and compensate for estimation errors. Finally, the experimental system based on Labview-RealTime system and ultra-redundant manipulator is used to verify the real-time performance and practicability of the algorithm. Experimental results are presented to illustrate the validity of the image features and tracking method.

Integrated attitude—orbit control of solar sail with single-axis gimbal mechanism

A new attitude control method for solar sails is proposed using a single-axis gimbal mechanism and three-axis reaction wheels. The gimbal angle is varied to change the geometrical relationship between the force due to solar radiation pressure (SRP) and the center of mass of the spacecraft, such that the disturbance torque is minimized during attitude maintenance for orbit control. Attitude maneuver and maintenance are performed by the reaction wheels based on the quaternion feedback control method. Even if angular momentum accumulates on the reaction wheels due to modelling error, it can also be unloaded by using the gimbal to produce suitable torque due to SRP. In this study, we analyzed the attitude motion under the reaction wheel control by linearizing the equations of motion around the equilibrium point. Further, we newly derived the propellent-free unloading method based on the analytical formulation. Finally, we constructed the integrated attitude-orbit control method, and its validity was verified in integrated attitude-orbit control simulations.

Adaptive sliding mode and RBF neural network based fault tolerant attitude control for spacecraft with unknown uncertainties and disturbances

Taking the external disturbance, model uncertainty, actuator failure, and actuator saturation into consideration, this paper investigates the nonlinear fault-tolerant attitude control problem for the spacecraft. First, to deal with the disturbance and uncertainty with unknown boundaries, an adaptive sliding mode control method is proposed to stabilize the state variables of the spacecraft attitude control system. Then the actuator failure and saturation are further considered, and the second spacecraft fault-tolerant attitude controller is derived based on adaptive sliding mode control and radial basis function (RBF) neural networks. The adaptive control gains reduction technique is applied in the design process, which can weaken the chattering phenomenon of the controller to some extent, and the stability of the attitude control system is analyzed through Lyapunov theory. In the proposed controller, model information and external disturbance are not required and only the system states are needed. Furthermore, the boundaries of the model uncertainty, external disturbance, actuator fault and saturation are assumed to be existing but unknown by the proposed controllers. These features make the proposed attitude controllers need few modeling information of the spacecraft, or maintain strong robustness even if the model or external environment occurs significant changes. Finally, numerical simulations demonstrate the great performance of the proposed fault-tolerant attitude control method.

Research on rapid batch test technology of small satellite attitude and orbit control system

The attitude and orbit control sub-system of small satellites is an important sub-system to ensure the stable operation of satellites in orbit, and its testing in the satellite factory development stage runs through the whole testing process. Most of the traditional attitude and orbit control sub-system tests require the cooperation of many people to complete the performance tests of various components and sensitive devices, which also leads to the complicated and time-consuming testing process of the sub-system. At present, the mass production of small satellites is developing rapidly. In order to meet the urgent needs of small satellite mass production, the traditional attitude control subsystem test equipment and methods can’t meet the requirements of satellite mass production mode. In order to meet the needs of small satellite mass development, this paper studies the rapid batch test technology of small satellite attitude and orbit control systems. A fast pipeline test system which can support the performance and dynamics simulation of various components of a multi-satellite attitude and orbit control subsystem is studied. Based on the architecture of real-time communication between the upper computer and lower computer, the system can realize the integration and rapid testing of component performance and dynamic simulation. A continuous dynamic simulation test of 2140 seconds was carried out for the cruise mode and attitude maneuver mode of a certain type of satellite in orbit. During the test, various components and sensors of the attitude and orbit control subsystem operate correctly and can support the stable operation of the required attitude in various modes of the satellite, which can verify the correctness of the interface and performance of the satellite attitude and orbit control subsystem. The research results greatly reduce the input of manpower and material resources, and greatly improve the test efficiency. It lays a foundation for the rapid batch test of the small satellite attitude control subsystem.

Research on UAV flight attitude control system based on simulink and genetic algorithm

With the evolution of networking and information technology, unmanned aerial vehicles (UAVs) have emerged as integral components in numerous fields, executing diverse and vital tasks. The accuracy and efficiency of UAV operations are greatly influenced by their attitude control systems, which dictate flight stability, speed, and functionality. However, traditional control methods for UAV flight stability and accuracy have become outdated, unable to meet the demands of modern applications. To address these challenges, this study introduces a novel attitude control approach based on genetic algorithms. This method utilizes the principles of natural selection and genetic traits to optimize the parameters of the attitude control system. The objective is to enhance the current control effectiveness, improve the UAV's maneuverability, and enable its versatile applications across multiple fields. Simulink tool simulations are conducted to evaluate the stability of the optimized results. This simulation tool allows for the replication of real-world flight conditions and the assessment of the attitude control system's performance under various scenarios. The results of these simulations provide valuable insights into the effectiveness of the genetic algorithm-based attitude control method. This study contributes to the advancement of UAV technology by improving the accuracy and stability of attitude control systems. It paves the way for more efficient and reliable UAV operations in various fields, from environmental monitoring to search-and-rescue missions, thus enhancing their operational capabilities and expanding their potential applications.

Intelligent robotic arm for human pose recognition based on teleoperation system

With the rapid development of skeleton recognition, machine learning and other technologies, we will find that there are great drawbacks in the control of manipulators. Robot teleoperation refers to the inclusion of human operation in the control loop of robot control. When robots deal with complex perception and a large number of tasks, teleoperation is far superior to intelligent programming when making decisions quickly and dealing with extreme situations. The goal of this paper is to build a robotic arm teleoperation system for human motion capture, so as to solve the problems that the control accuracy of the end of the robotic arm is not high and the motion of the robotic arm is greatly affected by the difference between the human arm in the current related research, the master-slave human motion mapping algorithm is designed and extended with machine learning algorithms. We use inertial motion capture to realize teleoperation, so as to avoid the use of the terminal position and orientation control method of the hand controller to form the control command of the remote robot after tedious calculation, and it is convenient for the operator to complete the attitude tracking task in real time. The obtained attitude information has a larger range, higher sensitivity and better dynamic performance.