Attitude Error Research Articles

Influence factors of the accuracy of monocular vision pose estimation for spacecraft based on neural network

Accurate acquisition of spacecraft pose information is a key step in tasks such as space debris removal, rendezvous and docking, and on-orbit maintenance. The application of neural network has deepened for aerospace measurement and control. Studying the parameters’ influence and degree of imaging and neural network on the accuracy of spacecraft pose estimation is necessary and urgent, but there is still little research in related fields. Approximate Bayesian inference in the depth Gaussian process is utilized in this paper to explore above problems, and systematic verification experiments are carried based on the general neural network and spacecraft pose estimation data set. The verification results depict that the estimation errors of position and attitude can be both effectively reduced by selecting a camera whose radial distortion error is less than 240%. Selecting a camera with focal ratio error less than 2.0% can effectively reduce the position estimation error. Selecting a camera whose principal point error is less than 1.0% can effectively improve the accuracy of attitude estimation. The suppression of tangential distortion error does not significantly improve the estimation accuracy of both position and attitude. Parameters about weighted attenuation factor, the number of network layers and characteristic channels of neural network are all sensitive to the accuracy of pose estimation of spacecraft within a specific range. An excellent balance can be achieved between the estimation accuracy and computational overhead when the weight attenuation factor is set between 0.01 and 0.1, and the number of network layers and characteristic channels are set separately between 20 ∼ 40 and 70 ∼ 90. It can reach the accuracy of the traditional pose estimation algorithm, and has better processing time stability in real ground test system by using the network architecture of ResNet34 with weight attenuation factor of 0.01, characteristic channels of 90.

Data-Driven Immersion and Invariance Adaptive Attitude Control for Rigid Bodies With Double-Level State Constraints

This article investigates the attitude control problem of a rigid body subject to attitude and angular rate constraints (double-level state constraints), and inertia uncertainties. A data-driven immersion and invariance (I&I) adaptive control scheme is proposed to tackle this technically challenging problem. As a stepping stone, a novel dynamically scaled I&I adaptive controller is designed to bypass the realizability condition that may not hold in the Lyapunov sense when considering angular rate constraints. Lyapunov stability analysis shows that this controller can enable the attitude errors and angular rates to converge asymptotically to zero for most initial conditions in the accessible space, while strictly obeying double-level state constraints with the help of two judiciously constructed potential functions. After that, to further relax the dependence of parameter convergence on the persistent excitation (PE) condition, the I&I adaptive law is extended to a data-driven counterpart through adding a learning term that is acquired by adopting the regressor filtering in conjunction with the dynamic regressor extension and mixing (DREM) procedure. The extended adaptive controller can not only preserve all the results obtained by the earlier proposed I&I adaptive controller, but also ensure asymptotic parameter convergence under a finite excitation condition much weaker than PE. In addition, benefiting from the DREM method and some special designs, the parameter convergence rates across all the parameter vector components can be flexibly tuned in an explicit way, and moreover, they are independent of the excitation level. Finally, simulation results are given to show the effectiveness of the proposed method.

Event-Triggered Finite-Time Attitude Cooperative Control for Multiple Unmanned Aerial Vehicles.

The finite-time attitude cooperative control problem for a group of multiple unmanned aerial vehicle systems with external disturbances and uncertain parameters is discussed in this paper. The dynamics of the systems is described by quaternion avoiding the singularity. Based on the attitude error and angular velocity error, a novel nonsingular terminal sliding mode surface is proposed for the controller with event-triggered scheme. The lumped disturbances are estimated by neural networks with adaptive law. The communication frequency is decreased by the proposed distributed event-triggered based sliding mode controller. Lyapunov theory is utilized to analyze the stability of the systems, and the Zeno behavior is avoided by rigorous proof. Finally, simulation examples are presented to illustrate the efficiency of the proposed control algorithm.

Calibration method for misalignment angles of a fiber optic gyroscope in single-axis rotational inertial navigation systems.

In a fiber optic gyroscope rotational inertial navigation system (RINS), attitude errors may change after vibration due to the change of misalignment angles. There are two kinds of misalignment angles which can cause the same attitude errors: the one is misalignment angles of gyroscopes, and the other is misalignment angles between input axis of gyroscope and rotating gimbal axis. Thus, it is difficult to calibrate any kind of misalignment angles by attitude errors alone. Self-calibration methods can separate and calibrate the two kinds of misalignment angles. But single-axis RINSs rely on a turntable to realize the rotation scheme. And misalignment angles may change during repeated removal. Therefore, it is necessary to study an efficient and convenient method to analyze which kind of misalignment angles leads to the change of attitude errors and calibrate these misalignment angles. According to the different influences of two kinds of misalignment angles on navigation errors and fine alignment errors, this paper proposes a calibration method based on fine alignment algorithm to calibrate the gyroscopes' misalignment angles. Its accuracy is proven by simulations and experiments. From experimental results, position errors have decreased at least 21.4% with the proposed method.

A New Space-to-Ground Microwave-Based Two-Way Time Synchronization Method for Next-Generation Space Atomic Clocks

The accuracy of time synchronization can be significantly increased by enhancing the performance of atomic clocks. Future-generation time-frequency loads will be equipped with the latest ultrahigh-precision atomic clocks (with a day stability better than 10−17) and will leverage advantages of the space environment such as microgravity and low interference to operate a new generation of high-performance time-frequency payloads on low-orbit spacecraft. Moreover, using the high-precision time-frequency system of ground stations, low-time-delay high-performance time-frequency transmission networks, which have the potential to achieve ultrahigh-precision time synchronization, will be constructed. By considering full link error terms above the picosecond level, this paper proposes a new space-to-ground microwave two-way time synchronization method for scenarios involving low-orbit spacecraft and ground stations. Using the theoretical principles and practical application scenarios related to this method, a theoretical and simulation verification platform was developed to research the impact of the attitude, phase center calibration, and orbit determination errors on the single-frequency two-way time synchronization method. The effectiveness of this new method was verified. The results showed that when the attitude error is less than 72 arc seconds (0.02°), the phase center calibration error is less than 1 mm, and the precision orbit determination (POD) error is less than 10 cm (three-axis). After disregarding nonlink error terms such as equipment noise, this method can attain a space-to-ground time synchronization accuracy of better than 1.5 ps, and the time deviation (TDEV) of the transfer link is better than 0.7 ps @ 100 s, which results in ultrahigh-precision space-to-ground time synchronization.

STEREO VISION INFORMATION SYSTEM USING MEDIAN THEOREM AND ATTITUDE COMPENSATION WITH NONLINEAR DIFFERENTIAL EQUATIONS

Using computer vision technology to obtain and analyze biomechanical information is an important research direction in recent years. However, the linear model in the computer vision system cannot accurately describe the geometric relationship of the camera imaging, so it is difficult to realize human posture recognition in high-precision mechanics information. Therefore, how to improve the recognition accuracy is very important. In this paper, we apply nonlinear differential equations to stereo computer vision (SCV) information systems. And based on the median theorem, a nonlinear posture recognition and error compensation algorithm based on BP neural network is proposed to reduce the recognition error. The test set uses the Leeds Motion Pose (LSP) dataset to verify the performance of the algorithm. Experimental results show that the compensated median filter of BP neural network can eliminate glitches in attitude data. Superimposing the output attitude error compensation value with the attitude estimation value can greatly reduce the root-mean-square error of the attitude angle. The result of gesture recognition is closer to reality. Compared with traditional algorithms, the cyclomatic complexity of the proposed BP neural network algorithm has a much lower growth rate in high-order calculations, which indicates that the proposed BP neural network algorithm is more concise and scalable.

Agile flight for a quadrotor via robust geometry control: Theory and experimental verification

AbstractThis article investigates the agile flight control design for a quadrotor unnamed aerial vehicle in the 3D environment. Due to the need for the high speed flight and aggressive manoeuver in practical scenarios, the kinematic and dynamic models of the unmanned quadrotor are described in SO(3) and the controller is designed in the rotation format, which can avoid the singularity in the Euler angle format and the unwinding issue in the quaternion format. Moreover, comparing with the other existing nonlinear control strategies for the unmanned quadrotor, the proposed controller has the advantage of easy implementation and good robustness to the modeling uncertainties and unknown external disturbances by using the robust‐integral‐signum‐error method. The stability of the closed loop system is proved via the Lyapunov based stability analysis, and the proposed nonlinear robust controller can guarantee the asymptotic convergence of the quadrotor's attitude and position tracking errors. The effectiveness of the proposed agile flight control design is validated via real‐time experiments in comparison with the classical cascaded PID controller. And the experimental results show that the proposed controller achieves much higher flight control accuracy in agile flights.

Crucial errors identification of worm grinding process of face gears based on tooth flank posture error model and their influences on tooth flank errors

Crucial errors identification of worm grinding process of face gears based on tooth flank posture error model and their influences on tooth flank errors

Suboptimal control law for a near-space hypersonic vehicle based on Koopman operator and algebraic Riccati equation

This paper presents a novel suboptimal attitude tracking controller based on the algebraic Riccati equation for a near-space hypersonic vehicle (NSHV). Since the NSHV’s attitude dynamics is complexly nonlinear, it is hard to directly construct an appropriate algebraic Riccati equation. We design the construction based on the Chebyshev series and the Koopman operator theory, which includes three steps. First, the Chebyshev series are considered to transform the error dynamics of the NSHV’s attitude into a polynomial system. Second, the Koopman operator is used to obtain a series of high-dimensional linear dynamics to approximate each of the polynomial system’s vector fields. In this step, our contribution is to determine a well-posed linear dynamics with the minimal dimension to approximate the original nonlinear vector field, which helps to design the control law and analyze the control performance. Third, based on the high-dimensional dynamics, the NSHV’s attitude error dynamics is separated into the linear part and the nonlinear part, such that the algebraic Riccati equation can be constructed according to the linear part. Then, the suboptimal error feedback control law is derived from the algebraic Riccati equation. The closed-loop control system is proved to be locally exponentially stable. Finally, the numerical simulation demonstrates the effectiveness of the suboptimal control law.

Adaptive coordinated motion constraint control for cooperative multi-manipulator systems

Constrained motion and redundant degrees of freedom control exist in a multi-manipulator collaboration system. In other words, the multi-manipulator collaboration technology must solve the problems of uncertain environment interaction and coordinated control. Few studies have been conducted on the coordination control of a multi-manipulator, and the control effect is not good. To solve the coordinated motion problem of the multi-manipulator cooperative system, this study divides the multi-manipulator coordinated motion into two forms, namely coupled and superimposed motions, and proposes an adaptive coordinated motion constraint scheme under different motion forms. The coupled and superimposed motions are investigated through coordinated handling and coordinated drawing circle tasks, respectively. The proposed coordinated control scheme has a good effect. Without position detection and positioning, the kinematic constraint algorithm can maintain the relative motion relationship between end-effectors. When an external disturbance occurs, the slave manipulator can automatically adjust based on the position of the main manipulator, avoiding error accumulation. The experimental results show a maximum trajectory tracking error of 2.131 mm and maximum attitude error of 0.176°, indicating that the proposed control scheme has strong adaptive ability and high control accuracy.

Distributed adaptive specified-time synchronization tracking of multiple 6-DOF fixed-wing UAVs with guaranteed performances

Different from the finite/fixed-time control methodologies on longitudinal/attitude synchronization or 2-D motion of UAVs, this article attempts to propose a distributed adaptive specified-time control scheme for synchronization tracking of networked 6-degree-of-freedom (DOF) UAVs. To be specific, the novel specified-time performance functions (STPFs) are designed in such a way that the desired performance bounds can be imposed on velocity and attitude tracking errors. Based on the transformed errors, by utilizing the barrier Lyapunov functions (BLFs), a distributed specified-time control scheme is constructed with adaptive robustifying terms to enhance the fault-tolerant ability and compensate the modeling uncertainties. By means of Lyapunov stability theory, it is proved that the resulting control scheme can guarantee the boundedness of all closed-loop state variables, and preserve the guaranteed performance bounds for synchronization tracking errors of velocity and attitude at the same time. Theoretical results are confirmed by experiment and simulation validations.

A High‐Lift Micro‐Aerial‐Robot Powered by Low‐Voltage and Long‐Endurance Dielectric Elastomer Actuators

AbstractDielectric elastomer actuators (DEAs) are a special class of artificial muscles that have been used to construct animal‐like soft robotic systems. However, compared with state‐of‐the‐art rigid actuators such as piezoelectric bimorphs and electromagnetic motors, most DEAs require higher driving voltages, and their power density and lifetime remain substantially lower. These limitations pose significant challenges for developing agile and powered autonomous soft robots. Here, a low‐voltage, high‐endurance, and power‐dense DEA based on novel multiple‐layering techniques and electrode‐material optimization, is reported. When operated at 400 Hz, the 143 mg DEA generates forces of 0.36 N and displacements of 1.15 mm. This DEA is incorporated into an aerial robot to demonstrate high performance. The robot achieves a high lift‐to‐weight ratio of 3.7, a low hovering voltage of 500 V, and a long lifetime that exceeds 2 million actuation cycles. With 20 s of hovering time, and position and attitude error smaller than 2.5 cm and 2°, respectively, the robot demonstrates the longest and best‐performing flight among existing sub‐gram aerial robots. This important milestone demonstrates that soft robots can outperform their state‐of‐the‐art rigid counterparts, and it provides an important step toward realizing power autonomy in soft robotic flights.

Study on Calibration of 3-PR(4R)R Three-Degree-of-Freedom Parallel Mechanism

A kinematic calibration approach is proposed for a triple-planar 3-PR(4R)R parallel mechanism whose attitude error cannot be corrected by motion. First, the kinematic principle of the 3-PR(4R)R parallel mechanism is presented. Then, an error mapping function model is designed based on the mechanism’s closed-loop vector equations. The model reveals the presence of coupling errors in the mechanism, i.e., the coupling of attitude and position errors, and is used to analyze error sensitivity as an indicator of component machining and assembly accuracy. A kinematic inverse solution is used to build a zero-point calibration and a full calibration method. Calibration is performed by a laser tracker-based calibration system, which generates much lower error values and delivers much improved accuracy. Experiments demonstrate that the proposed approach can potentially be used to calibrate parallel mechanisms with non-compensable error terms.

Quaternion Constrained Robust Attitude Control Using Barrier Lyapunov Function Based Back-stepping

A novel, robust quaternion constrained attitude control law for rigid bodies is presented in this paper. The controller is formulated using the backstepping philosophy, and Barrier Lyapunov Functions (BLFs) are used to prevent attitude constraint violation. This is done by ensuring the boundedness of BLFs in the closed-loop Lyapunov stability analysis. The analogy between a standard Quadratic Lyapunov Function based attitude control law and the BLF based control law is highlighted, and a systematic procedure to select control constants is detailed. Finally, the BLF based control law is verified by carrying out numerical simulations of the rigid body in the presence of initial attitude errors and bounded disturbance torques. In the first case, the attitude errors are asymptotically driven to zero while strictly satisfying the quaternion constraints. In the presence of bounded disturbance torques, the attitude errors remain bounded within the constraint boundary.

Fuzzy Integral Sliding Mode Observer-Based Formation Control of Mobile Robots With Kinematic Disturbance and Unknown Leader and Follower Velocities

Fuzzy integral sliding mode observer (FISMO) based leader-follower formation control with the use of ceiling-mounted camera information is proposed for mobile robots with kinematic disturbance and no information of the velocities of leader and follower robots. Using only the posture information of both the leader and follower robots obtained from the camera sensor, the follower robot is made to follow the trajectory of a target robot that is introduced to render the leader-follower formation control more efficient. As a result, a kinematic tracking error model is proposed in terms of both the posture error between the target and follower robots and the posture error between the origin of the inertial reference frame and follower robot. Because the kinematic disturbance and the velocities of the leader and follower robots are not available, an FISMO is designed to simultaneously estimate the kinematic disturbance as well as the linear and angular velocities of the leader and follower robots. FISMO-based leader-follower output feedback formation control is then proposed based on the estimates of the kinematic disturbance and velocities and the backstepping-like feedback linearization tracking control. The experimental results comparing the proposed FISMO-based method with the robust distance-based tracking control method and integral sliding mode observer (ISMO)-based method are provided to verify the effectiveness of the proposed method.

Path tracking control for car-like robots by pid controller with time-varying parameters

Wheel mobile robots have been widely applied to many areas of life, from serving people in services and industrial logistics to military fields. Applications of wheeled mobile robots such as floor cleaning robots, supermarket robots, logistics robots in hospitals, autonomous robots that guide automatic guidance in intelligent factories, military robots and agricultural robots. Thereby, we can see the great potential of mobile robots in the future. However, among the wheel mobile robots, the differential-drive mobile robot structure is most commonly used because of its simplicity in structure and control. Nevertheless, the navigation and driving are done synchronously through the two drive wheels, which leads to slippage and difficulty in controlling posture errors when navigating, especially on roads curved with a small radius. In order to overcome the disadvantages of the steering and navigation of differential-drive mobile robots, the structure of the automobile has been applied to design a wheel-mobile robot called a car-like robot. Many studies have shown that the advantage of this design option is that the navigation control and robot drive are separated. As a result, the dynamic control loop can ignore the navigation part, while the kinematic model determines the navigation law. Therefore, the work proposes the design of a PID controller with time-varying parameters for a car-like robot to track a given trajectory with minimum error. The nonlinear kinematic model of the robot is linearized along a reference trajectory, and the obtained linear model is used in the controller design process. A PID controller is designed where the controller parameters are tuned to minimize the tracking error. Our model achieves a tracking error value, including the minor position is 5.1 mm, and the maximum postural error is 2o. The simulation results showed the proposed controller's effectiveness in position and posture errors, and it shows we can apply this research to control car-like mobile robots in logistics services.

Markerless monocular vision-based localisation for autonomous inspection drones

This paper presents a markerless vision-based localisation algorithm to enable an autonomous inspection drone to determine its pose relative to a known inspection target. During an offline mapping phase, a 3D catalogue of the inspection target's persistent image features is created and stored. A neural network is trained on regions of interest of the images for fast segmentation. During the online localisation phase, the images are first segmented and the detected features in the segmented areas are matched with the stored features in the target's 3D catalogue. A pose estimation algorithm is applied to the matched features to determine the pose of the drone relative to the target. Practical experiments show promising results with small position and attitude errors.

Adaptive Fault Tolerant Attitude Control of a Nano-Satellite with Three Magnetorquers and One Reaction Wheel

In this paper, a passive fault tolerant attitude control is investigated for a nano-satellite with three magnetorquers and one reaction wheel in the presence of actuator constraints, external disturbances, and partial loss of actuator effectiveness as unknown actuator faults. For this purpose, a variable structure control approach is employed due to its ability to deal with the partial loss of actuator effectiveness and adjusting the maximum amplitude of the control signal by a control gain. A modified sliding variable is designed based on angular velocity and attitude errors with an adaptive parameter coefficient. The dynamics equation of the adaptive parameter is extracted during the stability proof of the closed-loop system. Changing the value of this parameter changes the effect intensity of the quaternion errors on the sliding variable and causes the attitude errors to maintain within a limited range. Applying this parameter to the modified sliding variable makes the controller perform well even if the adaptive parameter is significantly reduced. Furthermore, directly using the adaptive parameter in the structure of control law makes the controller generate a chattering-free torque. Comparing the simulation results with the results of two variable structure control methods designed in previous articles shows that its performance in attitude tracking commands has improved. In addition, Monte Carlo simulations with random actuator faults are performed to show the robustness of the proposed controller in the presence of actuator constraints and external disturbances.

Positioning for High-Speed Maneuverable Platform Based on Wrapped InSAR Interferogram

Interferometric Synthetic aperture radar (InSAR) aided inertial navigation system (INS) is developed to introduce terrain elevation into registration, which gives out accumulated error of INS. Unwrapped InSAR interferogram plays a key role in previous InSAR/INS frameworks, while airborne and side-looking assumptions are usually made. However, phase unwrapping is time-consuming and high squint geometry with nonlinear trajectory is common for applications with InSAR/INS. In this article, a novel wrapped InSAR interferogram based positioning method for high-speed maneuverable platform is proposed. A novel back-projection based InSAR (BP-InSAR) signal model for slightly curved diving trajectory with high squint angle is firstly presented. Then process which generates phase gradient template from given DEM is presented, considering both the estimated and measured trajectories. Error analysis is also made to discuss the performance of the proposed method in single dual-channel observation. Finally, a sequential maximum likelihood estimator based on error analysis is implemented to deal with the remained uncertainty in single observation. Numerical experiments verify the proposed method and fully demonstrate the performance of it with curved trajectory and random errors in both position and attitude.

Event-Triggered Adaptive Fault-Tolerant Synchronization Tracking Control for Multiple 6-DOF Fixed-Wing UAVs

In contrast with most existing results concerning unmanned aerial vehicles (UAVs) wherein two-degree or only attitude/longitudinal dynamics are considered, this article proposes an event-triggered cooperative synchronization fault-tolerant control (FTC) methodology for multiple fixed-wing UAVs whose dynamics are six-degree-of-freedom (DOF) with twelf-state-variables subject to actuator faults, modeling uncertainties, and external disturbances. More precisely, an event-triggering mechanism is devised to determine the time instants of updating control signals, which reduces the signal transmission burden, while saving on system resources. The Zeno phenomenon is excluded in the sense of guaranteeing that the time between two consecutive switchings is lower bounded by a positive constant. The actuator faults as well as the network induced errors are handled via the bound estimation approach and some well-defined smooth functions. By strict Lyapunov arguments, all closed-loop signals are proved to be semi-globally uniformly ultimately bounded (SGUUB) and the synchronization tracking errors of speed and attitude converge to a residual set around origin whose size can be made arbitrarily small through selecting appropriate design parameters.