Position Control Method Research Articles

Three-Dimensional Dynamic Positioning Using a Novel Lyapunov-Based Model Predictive Control for Small Autonomous Surface/Underwater Vehicles

Small Autonomous Surface/Underwater Vehicles (S-ASUVs) are gradually attracting attention from related fields due to their small size, low energy consumption, and flexible motion. Existing dynamic positioning (DP) control approaches suffer from chronic restrictions that hinder adaptability to varying practical conditions, rendering performance poor. A new three-dimensional (3D) dynamic positioning control method for S-ASUVs is proposed to tackle this issue. Firstly, a dynamic model for the DP control problem considering thrust allocation was established deriving from dynamic models of S-ASUVs. A novel Lyapunov-based model predictive control (LBMPC) method was then designed. Unlike the conventional Lyapunov-based model predictive control (LMPC), this study used multi-variable proportional–integral–derivative (PID) control as the secondary control law, improving the accuracy and rapidity of the control performance significantly. Both the feasibility and stability were rigorously proved. A series of digital experiments using the S-ASUV model under diverse conditions demonstrate the proposed method’s advantages over existing controllers, affirming satisfactory performances for 3D dynamic positioning in complex environments.

Multi-Observer Fusion Based Minimal-Sensor Adaptive Control for Ship Dynamic Positioning Systems

This paper proposes an adaptive dynamic positioning (DP) control method based on a multi-observer fusion architecture with minimal sensor requirements. A sliding mode observer is designed based on a high- and low-frequency superposition model to filter high-frequency state variables, while a finite-time convergence disturbance observer estimates unknown time-varying low-frequency disturbances online. For efficient handling of model uncertainties, a single-parameter learning neural network is implemented that requires only one parameter to be estimated online. The control system employs auxiliary dynamic systems to handle input saturation constraints and considers thruster system dynamics. Theoretical analysis demonstrates the stability of the observer-fusion control strategy, while simulation results based on the SimuNPS platform validate its effectiveness in state estimation and disturbance rejection compared to traditional sensor-dependent methods.

Predefined-Time Enhanced Antidisturbance Attitude Control for Rigid–Liquid Coupled Launch Vehicles

High-precision and fast-response attitude control of launch vehicles plays an increasingly important role in numerous space launch missions, ensuring the efficiency of space transportation. However, disturbances caused by liquid sloshing and external environments degrade the control performances seriously. Because of the complicated coupling with control inputs and angular acceleration, the dynamics of liquid sloshing disturbance are highly uncertain. Therefore, it is urgent to develop novel disturbance estimation methods for launch vehicles by thoroughly analyzing disturbance dynamics. In this paper, an observer-based predefined-time attitude control method aimed at achieving precise and rapid estimation of liquid sloshing disturbance and other external disturbances is proposed. More specifically, a prescribed-time reduced-order state observer is proposed for estimating the liquid sloshing disturbance and the unmeasurable system state, leveraging the specific characteristics and inherent coupling relations of liquid sloshing disturbance. Additionally, a prescribed-time extended state observer is introduced for estimating external disturbances and nonlinear terms. Subsequently, by employing a predefined-time output feedback controller, a composite control law is proposed to enhance the antidisturbance capabilities of rigid–liquid coupled launch vehicles. Finally, simulation examples are presented to illustrate the effectiveness of the proposed method.

Research on Assembly Robots Based on Flexible Control Technology

Industrial robots play a vital role in modern production automation, especially in improving production efficiency and reducing labor costs. However, traditional robot position control methods often have difficulty coping with complex assembly tasks, especially in changing environments. To solve this problem, flexible control technology has emerged, which can dynamically adjust the state of the robot according to the force applied by the external environment, thereby effectively avoiding rigid collisions and improving the accuracy and safety of the robot. This paper summarizes recent research on force-aware control in scenarios of shaft-hole assembly, including improvements to traditional force-position hybrid control and the introduction of intelligent control principles. This technique has significantly improved the efficiency and success rate of the alignment and loading phases. However, the high cost of force-aware control and data transmission limitations remain challenges. Future research should focus on improving system flexibility, expanding application scope and optimizing the underlying hardware to drive robotics towards intelligence and refinement

Research on Omnidirectional Gait Switching and Attitude Control in Hexapod Robots.

To tackle the challenges of poor stability during real-time random gait switching and precise trajectory control for hexapod robots under limited stride and steering conditions, a novel real-time replanning gait switching control strategy based on an omnidirectional gait and fuzzy inference is proposed, along with an attitude control method based on the single-neuron adaptive proportional-integral-derivative (PID). To start, a kinematic model of a hexapod robot was developed through the Denavit-Hartenberg (D-H) kinematics analysis, linking joint movement parameters to the end foot's endpoint pose, which formed the foundation for designing various gaits, including omnidirectional and compound gaits. Incorporating an omnidirectional gait could effectively resolve the challenge of precise trajectory control for the hexapod robot under limited stride and steering conditions. Next, a real-time replanning gait switching strategy based on an omnidirectional gait and fuzzy inference was introduced to tackle the issue of significant impacts and low stability encountered during gait transitions. Finally, in view of further enhancing the stability of the hexapod robot, an attitude adjustment algorithm based on the single-neuron adaptive PID was presented. Extensive experiments confirmed the effectiveness of this approach. The results show that our approach enabled the robot to switch gaits seamlessly in real time, effectively addressing the challenge of precise trajectory control under limited stride and steering conditions; moreover, it significantly improved the hexapod robot's dynamic stability during its motion, enabling it to adapt to complex and changing environments.

Asymmetric full-state constrained attitude control for a flexible agile satellite with multiple disturbances and uncertainties

In this paper, an asymmetric full-state constrained attitude control method for a flexible agile satellite with multiple disturbances and uncertainties is proposed. Both the attitudes and angular velocities of the flexible agile satellite are guaranteed in the asymmetric/symmetric bounds by utilizing the time-varying integral barrier Lyapunov function (TVIBLF) and constant integral barrier Lyapunov function (IBLF) respectively. Compared with the existing error-based barrier Lyapunov functions, the asymmetric TVIBLF does not need error transformation, relaxes the conservation of the initial requirements, and has more compatibility. Furthermore, the proposed asymmetric TVIBLF can deal with the asymmetric time-varying constraints without loss of generality. In addition, the high-frequency flexible vibrations, inertial uncertainties, and unknown external disturbance torques affected on the agile satellite are considered respectively. On one hand, a modal observer is utilized to suppress the high-frequency flexible vibration effects on the attitudes. On the other hand, the inertial uncertainties and unknown external disturbance torques are estimated with a multi-variable nonlinear disturbance observer. Therefore, both the asymmetric full-state constrained performances and robustness of the attitude control system for the flexible agile satellite are achieved. The stability of the closed-loop system is proved and numerical simulations have validated the effectiveness of the proposed method.

Adaptive neural network based fixed-time attitude tracking control of spacecraft considering input saturation

Aiming at the issues of actuator saturation, inertia uncertainties, and external unknown disturbances in the attitude tracking control process of spacecraft, an adaptive fixed-time attitude control method is proposed, which is based on a radial basis function neural network (RBFNN). Firstly, a spacecraft attitude kinematics and dynamics model is established based on the quaternion method and a Gaussian error function is introduced to constrain the controller amplitude. Secondly, the external unknown disturbances are addressed by a fixed-time disturbance observer, and the controller is designed utilizing the backstepping method. To eliminate the adverse effects caused by actuator saturation, we design an enhanced auxiliary system to improve the stability of the system. Aiming at inertia uncertainties, RBFNN is used to approximate it, and an innovative fixed-time convergence adaptive law with RBFNN weights is devised. Subsequently, based on Lyapunov theory, the fixed time stability of the closed loop system is proven, and an expression for the settling time is given. Finally, simulation analysis validates the effectiveness of the designed controller.

The Design and Adaptive Control of a Parallel Chambered Pneumatic Muscle-Driven Soft Hand Robot for Grasping Rehabilitation.

The widespread application of exoskeletons driven by soft actuators in motion assistance and medical rehabilitation has proven effective for patients who struggle with precise object grasping and suffer from insufficient hand strength due to strokes or other conditions. Repetitive passive flexion/extension exercises and active grasp training are known to aid in the restoration of motor nerve function. However, conventional pneumatic artificial muscles (PAMs) used for hand rehabilitation typically allow for bending in only one direction, thereby limiting multi-degree-of-freedom movements. Moreover, establishing precise models for PAMs is challenging, making accurate control difficult to achieve. To address these challenges, we explored the design and fabrication of a bidirectionally bending PAM. The design parameters were optimized based on actual rehabilitation needs and a finite element analysis. Additionally, a dynamic model for the PAM was established using elastic strain energy and the Lagrange equation. Building on this, an adaptive position control method employing a radial basis function neural network, optimized for parameters and hidden layer nodes, was developed to enhance the accuracy of these soft PAMs in assisting patients with hand grasping. Finally, a wearable soft hand rehabilitation exoskeleton was designed, offering two modes, passive training and active grasp, aimed at helping patients regain their grasp ability.

Dynamic event-triggered adaptive positioning control of unmanned surface vehicles with system uncertainties

Abstract To solve the problems of unmanned surface vehicles (USVs), such as limited network bandwidth and computing resources, a finite-time command filtering backstepping (FTCFB) robust adaptive dynamic positioning control method with dynamic event-triggered mechanisms (DETM) for unmanned surface vehicles (USVs) is proposed. It addresses thruster dynamics, system uncertainties, and unknown external disturbances, ensuring finite-time convergence while mitigating issues like communication congestion and actuator wear. Adaptive neural networks are utilized to estimate the system uncertainties, and a DETM-based FTCFB control law, validated with Lyapunov stability theory, guarantees finite-time convergence of tracking and parameter estimation errors. Numerical simulations confirm the method’s effectiveness.

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.