Angular Velocity Error Research Articles

Adaptive prescribed performance control for spacecraft attitude synchronization subject to external disturbances with guaranteed steady‐state performance

AbstractThis paper proposes an adaptive prescribed performance control approach for multiple spacecraft attitude synchronization with the undirected topology. A novel distributed observer is proposed to estimate the states of the leader spacecraft while the estimation value of attitude is still a unit quaternion. Then we design the adaptive prescribed performance control algorithm restrain the external disturbances and parameter uncertainty. By the unit quaternion estimation value of attitude, we make the extension from single spacecraft to multiple spacecraft. Compared to some previous results, the bounds of attitude tracking error and angular velocity error in the steady state can be adjusted by users.

An innovative AR-assisted self-adjusting digital twin for speed control of a servomechanism

A machine might undergo changes in its system due to prolonged operation. These changes can significantly impact the performance and behavior of the machine’s controller. The general digital twin solutions typically use compensators to adjust the physical entity instead of updating the internal model parameters of the digital twin. Therefore, if deviations are significant, the physical entity may never fully match its digital twin. To address potential degradation in controller performance, this study introduces an AR (augmented reality)-assisted self-adjusting digital twin (SADT) servomechanism monitoring system. The primary objective of this system is to provide real-time monitoring, maintenance, and adjustments to ensure the optimal operation of the machine’s controller, even in the presence of significant deviations caused by extended operation or environmental changes. In smart manufacturing, servomechanisms are often used in wheeled mobile robots and conveyor mechanisms. Hence, this study selects servomechanisms as the target application. The AR-assisted SADT accomplishes this by comparing the angular velocity error between the servomechanism and its digital twin to identify when significant changes have occurred. Upon detecting such a change, the SADT calculates new internal model parameters, generating a modified digital twin. This allows resynchronization with the changed physical entity, restoration of the virtual-physical mapping, and continuous, accurate monitoring and optimized control. Furthermore, to enhance human–machine interaction, AR technology is used to offer users visual information, enabling intuitive monitoring of the servomechanism’s performance. In summary, the AR-assisted SADT system makes human–machine interaction more intuitive for operators and enhances the adaptability and accuracy of system monitoring.

Improved contactless attitude control law of uncooperative spacecraft by laser ablation

Laser ablation technology enables contactless debris removal, providing the advantages of safety and cost efficiency. To deorbit space debris using laser ablation, the attitude motion of the debris must be controlled in advance. Although a previous study derived an attitude control procedure, it empirically determines control gains and is easily affected by assumed preconditions. To solve this problem, this paper proposes an improved attitude control law using laser ablation. Previous methods use feedback control based on the angular velocity error and error quaternions to obtain a reference torque. By analogy with a magnetic attitude control law, this study designs a reference torque based on the angular momentum errors and presents theoretical condition for designing the control gains. Numerical examples using the proposed control law are conducted to control the target attitude from a random initial rotation of 1 rpm to an arbitrary attitude, which verify the effectiveness of the proposed method. Furthermore, the position, direction, and magnitude uncertainties of laser irradiation are introduced to numerically examine their effects on the control accuracy under environmental disturbances.

Attitude Algorithm of Gyroscope-Free Strapdown Inertial Navigation System Using Kalman Filter.

A gyroscope-free strapdown inertial navigation system (GFSINS) solves the carrier attitude through the reasonable spatial combination of accelerometers, with a particular focus on the precision of angular velocity calculation. This paper conducts an analysis of a twelve-accelerometer configuration scheme and proposes an angular velocity fusion algorithm based on the Kalman filter. To address the sign misjudgment issue that may arise when calculating angular velocity using the extraction algorithm, a sliding window correction method is introduced to enhance the accuracy of angular velocity calculation. Additionally, the data from the integral algorithm and the data from the improved extraction algorithm are fused using Kalman filtering to obtain the optimal estimation of angular velocity. Simulation results demonstrate that this algorithm significantly reduces the maximum value and standard deviation of angular velocity error by one order of magnitude compared to existing algorithms. Experimental results indicate that the algorithm's calculated angle exhibits an average difference of less than 0.5° compared to the angle measured by the laser tracker. This level of accuracy meets the requirements for attitude measurement in the laser scanning projection system.

Design and Control Simulation Analysis of Tender Tea Bud Picking Manipulator

Aiming at the current complex problem of the mechanized high-quality picking of tender tea buds, this paper designs a tender tea bud-picking manipulator. In the picking process, the quality of the petiole and leaf blade of the tender tea bud is crucial, as the traditional cutting picking method destroys the cell structure of the tender tea buds, resulting in rapid oxidation of the cuts, thus losing the bright green appearance and pure taste. For this reason, this paper draws on the quality requirements of tender tea buds and traditional manual picking technology, simulating the process of the manual picking action, putting forward a ‘rotary pull-up’ clamping and ripping picking method, and designing the corresponding actuating structure. Using PVDF material piezoelectric thin-film sensors to detect the clamping force of the tender tea bud picking, the corresponding sensor hardware circuit is designed. In addition, the finite element analysis method is also used to carry out stress analysis on the mechanical fingers to verify the rationality of the automatic mechanism to ensure the high-quality picking of tender tea buds. In terms of the control of the manipulator, an SMC-PID control method is designed by using MATLAB/Simulink 2021 and Adam 2020 software for joint simulation. The way to control the closed-loop system angle and angular velocity error feedback is by adjusting the PID parameters, which quickly converts the sliding mode control to the sliding mode surface. The simulation results show that the SMC-PID control method proposed in this paper can meet the demand in tender tea bud picking and simultaneously has high control accuracy, response speed, and stability.

Integral precession calibration method of PIGA on linear vibration table

Linear vibration table can provide harmonic accelerations to excite the nonlinear error terms of Pendulous Integrating Gyro Accelerometer (PIGA). Integral precession calibration method is proposed to calibrate PIGA on a linear vibration table in this paper. Based on the precise expressions of PIGA’s inputs, the error calibration model of PIGA is established. Precession angular velocity errors of PIGA are suppressed by integer periodic precession and the errors caused by non-integer periods vibrating are compensated. The complete calibration process, including planning, preparation, PIGA testing, and coefficient identification, is designed to optimize the test operations and evaluate the calibration results. The effect of the main errors on calibration uncertainty is analyzed and the relative sensitivity function is proposed to further optimize the test positions. Experimental and simulation results verify that the proposed 10-position calibration method can improve calibration uncertainties after compensating for the related errors. The order of calibration uncertainties of the second- and third-order coefficients are decreased to 10−8 (rad·s−1)/g2 and 10−8 (rad·s−1)/g3, respectively. Compared with the other two classical calibration methods, the calibration uncertainties of PIGA’s nonlinear error coefficients can be effectively reduced and the proportional residual errors are decreased less than 3 × 10−6 (rad·s−1)/g by using the proposed calibration method.

Parallel Multi-Layer Monte Carlo Optimization Algorithm for Doubly Fed Induction Generator Controller Parameters Optimization

This work proposes a parallel multi-layer Monte Carlo optimization algorithm (PMMCOA) that optimizes proportional–integral parameters for a doubly fed induction generator-based wind turbine controller. The PMMCOA, an improved form of the Monte Carlo algorithm, realizes the optimization process via a parallel multi-layer structure. The PMMCOA includes rough search layers, precise search layers, and re-precise search layers. Each layer of the PMMCOA adopts a multi-region and multi-granularity approach to increase the diversity and randomness of the search samples. The PMMCOA is employed to tune the controller parameters for achieving maximum power point tracking and improving generation efficiency. The controller fitness function reflects the sum of the rotor angular velocity error and the reactive power error. Compared with the five metaheuristic algorithms, the PMMCOA has a higher global convergence and more accurate power tracking ability.

Sliding-mode observer-based fault diagnosis and fault-tolerant control of the main drive system of rolling mill

In order to address the problem that the main drive system of rolling mill is easily affected by the impact of biting steel, and considering the nonlinear friction damping and the external perturbations of the main drive system of rolling mill during the rolling process, a fault model of the main drive system of rolling mill is established, and a fault diagnosis and fault tolerance control method of the main drive system of rolling mill based on the nonlinear sliding-mode observer is proposed. In order to suppress the influence of external perturbations on fault diagnosis, a nonlinear sliding-mode observer is constructed for fault diagnosis and fault reconfiguration of the system, and the robustness of the observer to fault reconfiguration is improved by using the sliding-mode control rate [Formula: see text], and the stability of the designed nonlinear sliding-mode observer is proved using Lyapunov’s stability theorem. In order to ensure that the system can operate normally even after a fault occurs, a reference model is designed, and a new controller is redesigned for fault-tolerant control of the system by adding a fault compensation term to the original control scheme using fault estimation information. Through the simulation study of the main drive system of stand F4 of 2030 mm cold rolling mill, it is verified that the observer can accurately track the system state with an angular velocity error of 2.45% and detect and estimate the main drive system failure of rolling mill with an estimation error of no more than 0.04% after a fault occurs; the fault-tolerant control of the main drive system of rolling mill is carried out by using the fault information to restore the system to its normal state, and the angular velocity error is 1.89%.

Dynamic surface event-triggered control for unmanned sailboat path following based on adaptive tanh line-of-sight

In this article, a dynamic surface control algorithm based on the adaptive tanh line-of-sight with speed adjustment is proposed to solve the problem of path-following control of unmanned sailboat with event-triggered input, considering the dynamic model uncertainty and unknown external disturbance. First, the adaptive tanh line-of-sight method is used to improve the following accuracy and robustness, then an extended state observer is used to estimate the external disturbance and the uncertainty of the internal model, the heading error and the yaw angular velocity error are considered to design the recursive sliding mode dynamic surface controller, at the same time, the speed of the unmanned sailboat is constrained to improve its stability. In addition, to reduce the action frequency of the steering gear, the relative threshold event-triggered control is applied to the controller. According to the Lyapunov stability analysis, it is proved that the proposed control scheme can ensure that all errors in the closed-loop system are semi-globally consistent and ultimately bounded, while the Zeno behavior can be avoided. Finally, the effectiveness of the proposed scheme is verified by simulation and comparative experiments.

Adaptive neural network independent joint-based control for an ODE-PDE rigid-flexible manipulator with multiple constraints

This paper deals with the adaptive neural network control of a nonlinear rigid-flexible manipulator subject to external disturbances and multiple constraints. The system dynamic is modeled by ordinary and partial differential equation (ODE and PDE) based on Hamilton’s Principle. An adaptive neural network sliding mode control strategy is designed to estimate the unknown disturbances and uncertain parameter. The sliding surface consisting of angular error, angular velocity error, and end-point vibration information is proposed based on the energy analysis of the flexible beam. The tangent barrier Lyapunov function is used to ensure the angle errors and end-point deflection within the restrictions. It is shown that the joint motion and vibration of the flexible beam can be adjusted only with an independent joint control input, and no boundary force input is required for vibration suppression. The stability is obtained by semi-group theory and LaSalle’s Invariance Principle. Simulation results demonstrate the effectiveness of the control strategy.

Adaptive finite-time fault-tolerant control for nano-satellite attitude tracking under actuator constraints

In this paper, a finite-time fault-tolerant control method is developed for attitude tracking of a nano-satellite with three magnetorquers and one reaction wheel in the presence of actuator faults, inertia uncertainties, external distribution, and actuator constraints. For this purpose, the modified non-singular fast terminal sliding mode control is used due to the ability of robustness against uncertainties and finite-time convergence. In this approach, the modified sliding surface variable is constructed based on angular velocity and attitude error to converge the attitude error to zero in a finite time. In addition, three adaptive parameters are employed to reduce the adverse effect of uncertain expressions and to track the attitude commands with high control accuracy. The adaptive parameters and sliding surface variable are used in the reaching phase of the controller to decrease the chattering phenomenon. The dynamics of adaptive parameters, with regard to the angular velocities of the nano-satellite and reaction wheel and the sliding surface variable, are obtained during the stability proof of the closed-loop system. Also, the finite-time convergence of sliding surface and attitude variables are proved by the extended Lyapunov condition, and the convergence regions of attitude tracking errors are obtained. The simulations are accomplished and compared with an existing adaptive sliding mode control approach to evaluate the performance of the proposed controller. The results verify that the proposed controller performs better than the mentioned controller in terms of finite-time convergence, the accuracy of attitude tracking, and the reduction of the chattering phenomenon.

Filtering‐based concurrent learning adaptive attitude tracking control of rigid spacecraft with inertia parameter identification

SummaryThis paper investigates the attitude tracking control problem of rigid spacecraft with inertia parameter identification. Based on the relative attitude and angular velocity error dynamics, a basic adaptive backstepping based attitude tracking control scheme is firstly designed such that asymptotic attitude tracking can be achieved. However, the parameter identification error cannot decay to zero if the persistent excitation (PE) condition is not satisfied. To solve this issue, a filtering‐based concurrent learning adaptive backstepping control scheme is then proposed, by incorporating torque filtering technique with concurrent learning technique. A more mild rank condition, which consists of some collectable historical data, is provided to guarantee the convergence of parameter identification error. In addition, a valid data collection algorithm is given. It should be mentioned that a distinctive feature of the proposed filtering‐based concurrent learning adaptive control scheme is that the convergence rate of attitude tracking errors can be improved from asymptotical to exponential. Finally, simulation results are provided to illustrate the effectiveness of the proposed control schemes.

Extending arterial stiffness assessment along the circumference using beam-steered ARFI and wave-tracking: A proof-of-principle study in phantoms and ex vivo

BackgroundTo fully quantify arterial wall and plaque stiffness, acoustic radiation force impulse (ARFI)-induced wave-tracking along the entire vessel circumference is desired. However, attenuation and guided wave behavior in thin vessel walls limits wave-tracking to short trajectories. This study investigated the potential of beam-steered ARFI and wave-tracking to extend group velocity estimation over a larger proportion of the circumference compared to conventional 0° ARFI-induced wave-tracking. MethodsSeven vessel-mimicking polyvinyl alcohol cryogel phantoms with various dimensions and compositions and an ex vivo human carotid artery were imaged in a dedicated setup. For every 20⁰ phantom rotation, transverse group wave velocity measurements were performed with an Aixplorer Ultimate system and SL18–5 transducer using 0⁰/20⁰/−20⁰-angled ultrasound pushes. Transmural angular wave velocities were derived along 60⁰-trajectories. A 360⁰-angular velocity map was composed from the top-wall 60⁰-trajectories 0°-data, averaged over all physical phantom rotations (reference). For each phantom rotation, 360⁰-angular velocity maps were composed using 0°-data (0⁰-approach) or data from all angles (beam-steered approach). Percentages of rotations with visible waves and relative angular velocity errors compared to the reference map as function of the circumferential angle were determined for both approaches. ResultsReference 360°-angular velocity maps could be derived for all samples, representing their stiffness. Beam-steering decreased the proportion of the circumference where waves were untraceable by 20% in phantoms and 10% ex vivo, mainly at 0° push locations. Relative errors were similar for both approaches (phantoms: 10–15%, ex vivo: 15–35%). ConclusionBeam-steering enables wave-tracking along a higher proportion of the wall circumference than 0⁰ ARFI-induced wave-tracking.

FRACTIONAL ORDER PIλDμ FOR TRACKING CONTROL OF A NOVEL REHABILITATION ROBOT BASED ON IIMO-BP NEURAL NETWORK ALGORITHM

In this study, we develop a novel multi-posture lower limb rehabilitation robot with three postures, which can provide different amplitudes and frequencies of rehabilitation training for hip, knee and ankle joints, respectively. The kinematic and dynamic analyses of the robot are carried out to solve the kinematic forward and backward solutions and the Lagrangian dynamics equations of the lower limbs. The angle, angular velocity and angular acceleration ideal trajectory curves of the rehabilitation motion are derived by using a quintic polynomial trajectory planning scheme. An improved ions motion optimization (IIMO) algorithm is proposed and applied to optimize the initial weight of back propagation (BP) neural network, and algorithm is used to adjust five parameters of fractional order [Formula: see text] ([Formula: see text]) control in controller design. The passive training experiment results of prototype show that the designed controller has the largest average error of angle and angular velocity of hip, knee and ankle joints in high amplitude and high frequency movement mode, which are 1.091∘, 0.716∘, 0.412∘, 1.551∘/s, 1.394∘/s, 1.498∘/s, respectively. At low amplitude and low frequency, the maximum average errors are the smallest, which are 0.351∘, 0.341∘, 0.167∘; 0.833∘/s, 0.842∘/s, 0.398∘/s, respectively. The actual trajectory curve fits well with the designed one. The highest accuracy of angle and angular velocity can reach 99.165% and 99.116% through comprehensive comparison of all motion modes. Therefore, the overall error is small. The stability of rehabilitation training process is ensured, and the rationality and effectiveness of trajectory planning and control design are verified.

A Time-Varying Weight MPC-Based Motion Cueing Algorithm for Motion Simulation Platform

The motion cueing algorithm (MCA) is playing the most critical role in motion simulation platform (MSP) to reproduce the realistic motion sensation of the real car for the MSP’s users while taking into cogitation of the physical boundaries of the platform. Recently, the model predictive control (MPC) is employed for designing MCAs which led to the formation of MPC-based MCAs. The purpose of the MPC-based MCA is to recalculate the optimal values of the input signals with consideration of the MSP’s physical restrictions. All the current MPC-based MCAs have fix weights that can cause the conservative and inefficient utilization of the MSP’s workspace limitations as they have been tuned based on the worst-case scenarios to keep the platforms within their physical limitations. Then, the error of motion sensation between the real car and MSP users increases due to the conservative utilization of the MSP’s workspace boundaries. The main objective of this study is to provide more efficient workspace utilization to minimise the error of motion feeling between the real car and MSP users while respecting the workspace boundaries. A procedure according to the optimised fuzzy logic-based units is employed to calculate the appropriate MPC weights online while considering the sensed specific force error, sensed angular velocity error and the current motion status of the MSP including linear position, linear velocity and angular position of the cockpit. The proposed MPC-based MCA is designed and developed using MATLAB. The outcomes show a better motion feeling compared with the current MPC-based MCAs.

Research on hydraulic motor control system based on fuzzy neural network combing sliding mode control and time delay estimation

The precise motion control of a hydraulic motor system has some problems due to uncertain disturbance, complex nonlinear dynamics. Traditional methods are difficult to obtain the desired control performance. In this paper, a new fuzzy neural network (FNN) combined with terminal sling mode control (TSMC) and time delay estimation (TDE) is proposed. FNN is used to adjust the parameter of TSMC to reduce the time for the system to reach the equilibrium point and chatting. To increase the accuracy of the system, TDE is used to compensate the error caused by uncertain disturbance. This controller was simulated in Amesim and Simulink, and the results showed that the control scheme proposed in this paper has the smallest angular displacement error, angular velocity error and variance than other control schemes, such as PID and sliding mode control (SMC). Furthermore, the designed controller was implemented on a drill pipe automatic handling manipulator, and its control performance was verified.

Adaptive Fault-Tolerant Attitude Tracking Control for Flexible Spacecraft With Guaranteed Performance Bounds

Fault-tolerant attitude tracking control for rigid-flexible coupled spacecraft is studied in this article. Taking actuator faults, measurement errors of attitude and angular velocity, unmeasured modal displacements, model uncertainties, and external disturbances into consideration, an adaptive fault-tolerant attitude tracking control is designed. Then, based on the sequential Lyapunov method, the stability of the closed-loop system is analyzed showing that if the attitude and angular velocity measurement errors are bounded, then successively less conservative bounds on the attitude tracking error and the angular velocity error can be obtained.

Dynamic modeling and fuzzy compensation sliding mode control for flexible manipulator servo system

The flexible manipulator servo system considering the second-order modal and two-dimensional deformation is too complicated, which is not conducive to real-time control. In order to establish dynamic equations that can be controlled in real-time, it is necessary to analyze each nonlinear factor. Based on the results of dynamic analysis, a model simplification method is proposed that ignores the second-order modal deformation. What's more, an improved fuzzy compensation sliding mode control (FCSMC) strategy is proposed to control the angular velocity of the servo system. The friction torque and the ignoring nonlinear factors are compensated by the fuzzy systems. The improved FCSMC strategy can reduce the chattering phenomenon and the angular velocity errors. Simulation experiments and physical control experiments prove the effectiveness of the improved FCSMC strategy. The results show that the improved FCSMC strategy has smaller angular velocity errors. Therefore, the strategy can effectively improve the movement accuracy of flexible manipulators, which is conducive to the precise end-effectors.

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.

Attitude Control of a Quadrotor with Fuzzy Logic Controller on SO(3)

A quadrotor is an unmanned aerial vehicle (UAV) with two pairs of rotors rotating in opposite directions. Some of its unique abilities are hovering and vertical take-off and landing (VTOL). Most recent works carried out the UAVs' rotation parametrization using Euler angles and a quaternion. Those UAVs suffer from singularities and ambiguities. A geometric control is generally used to deal with those problems. Exponential coordinate in the geometric control maps R3 into SO(3). This paper presented a fuzzy logic controller on SO(3) to control the attitude of the quadrotor. The input of the fuzzy logic controller is the angular velocity (ω) and exponential coordinate error of rotation (ζ), while the output is torque (τ). The error function in this controller is a rotation matrix on SO(3). This proposed controller can control the attitude of the quadrotor based on the expected attitude for maneuvers both on one axis and all axis with a steady-state error of about 0.02 rad.