Synchronization Tracking Control Research Articles

Adaptive Neural Network Synchronous Tracking Control for Teleoperation Robots Under Event-Triggered Mechanism

Adaptive Neural Network Synchronous Tracking Control for Teleoperation Robots Under Event-Triggered Mechanism

Fully distributed prescribed time robust synchronous tracking control of multi-unmanned systems under communication link faults

Fully distributed prescribed time robust synchronous tracking control of multi-unmanned systems under communication link faults

Decentralized Adaptive Event-Triggered Fault-Tolerant Synchronization Tracking Control of Multiple UAVs and UGVs With Prescribed Performance

Decentralized Adaptive Event-Triggered Fault-Tolerant Synchronization Tracking Control of Multiple UAVs and UGVs With Prescribed Performance

Prescribed time formation synchronous tracking of unmanned aerial vehicles with denial of service attacks

This paper addresses the issue of robust prescribed time synchronous tracking control of unmanned aerial vehicles (UAVs) formation with denial of service attacks. A novel prescribed time synchronous tracking control scheme is proposed within a denial of service attack conversion mechanism. Firstly, on account of the UAV formation in the presence of aerodynamic damping and denial of service attacks, a formation cooperative control model is established under a second-order system. Secondly, to perform formation synchronous tracking within a settling time, a time-varying auxiliary function on account of synchronous state errors is designed for the convergence of prescribed time tracking. The control parameters are designed using a key eigenvalue scaling technique under the effect of denial of service attacks on network topology. The proposed scheme is also extended to UAV formation with a detailed-balanced graph. Finally, simulation results are presented to demonstrate the effectiveness of the proposed strategy.

Adaptive Practical Predefined-Time Control for Uncertain Teleoperation Systems With Input Saturation and Output Error Constraints

In this paper, the synchronization tracking control issue is investigated for uncertain teleoperation systems with input saturation and output error constraints. To this end, an adaptive practical predefined-time control scheme integrating backstepping recursive design, predefined time theory, prescribed performance function, and the anti-saturation auxiliary system, is developed for the first time. A fixed-time extended state observer is utilized to eliminate the negative influence of the lumped uncertainty, and the anti-saturation auxiliary system is constructed to address the input saturation. In contrast to finite/fixed-time controllers, the upper bound of convergence time can be obtained in advance by adjusting a single control parameter. The results indicate that the error signals can converge into a small region of the zero domain within a user-defined time and that the output error never violates the prescribed performance boundary. Simulations and experiments are performed on a teleoperation platform made up of two Phantom Touch robots to verify the effectiveness and practicality of the developed controller.

Experimental Study on Adaptive Backstepping Synchronous following Control and Thrust Allocation for a Dynamic Positioning Vessel

Cargo transfer vessels (CTVs) are designed to transfer cargo from a floating production storage and offloading (FPSO) unit into conventional tankers. The dynamic positioning system allows the CTV to maintain a safe position relative to the FPSO unit using a flexible cargo transmission pipe, and the CTV tows the tanker during operating conditions. The operation mode can be considered a synchronization tracking control problem. In this paper, a synchronization control strategy is presented based on the virtual leader–follower configuration and an adaptive backstepping control method. The position and heading of the following vessel are proven to be able to globally exponentially converge to the virtual ship by the contraction theorem. Then, the optimization problem of the desired thrust command from the controller is solved through an improved firefly algorithm, which fully considers the physical characteristics of the azimuth thruster and the thrust forbidden zone caused by hydrodynamic interference. To validate the effectiveness of the presented synchronous following strategy and thrust allocation algorithm, a scale model experiment is carried out under a sea state of 4 in a seakeeping basin. The experimental results show that the CTV can effectively maintain a safe distance of 100 m with a maximum deviation of 3.78 m and an average deviation of only 0.99 m in the wave heading 180°, which effectively verifies that the control strategy proposed in this paper can achieve safe and cooperative operation between the CTV and the FPSO unit. To verify the advantages of the SAF algorithm in the thrust allocation, the SQP algorithm and PSO algorithm are used to compare the experimental results. The SAF algorithm outperforms the SQP and PSO algorithms in longitudinal and lateral forces, with the R-squared (R2) values of 0.9996 (yaw moment), 0.9878 (sway force), and 0.9596 (surge force) for the actual thrusts and control commands in the wave heading 180°. The experimental results can provide technical support to improve the safe operation of CTVs.

Research on constellation attitude synchronous tracking control for space gravitational wave measurement

Space based gravitational wave detectors require a precision relative attitude pointing control in science mode. Their configuration drifts sensitively under the influence of perturbations. The spacecraft and telescope optical assembly attitude control loops need to respond to this change in real time to maintain an ultra-high precision laser pointing. To implement precision gravitational wave scientific measurement, distributed detector systems need to coordinate the relative attitude pointing between spacecrafts in the constellation. This article proposes a synchronization coordination control method based on state estimation feedforward and finite frequency domain optimization. The simulation results show that the synchronization and coordination tracking of the relative attitude between satellites can be achieved under configuration perturbations, and the jitter in the ultra-low frequency range can be effectively reduced. Especially in the frequency range below 10−3 Hz, the stability of the line of sight attitude pointing is improved from 10−6 rad/Hz1/2 to 10−9 rad/Hz1/2. The absolute attitude pointing performance and stability are greatly improved.

Learning-Enabled Output-Feedback-MPC-Based Synchronization Tracking Control of Multiaxis Motion Systems

The unknown and time-varying model parameters and measurement noises impose great influence on the highprecision synchronization tracking control performance of multiaxis motion systems (MAMSs). To deal with the influence of the factors on the high-precision synchronization tracking control performance in MAMSs, a learning-enabled output feedback model predictive control (MPC) strategy for MAMSs is proposed. Firstly, an efficient learning mechanism is introduced to capture the unknown and slow time-varying behavior of the system parameters. Then, considering both the existence of the measurement noises and the unavailability of the velocity signals in system and synchronization coupling errors, the learning-enabled output feedback MPC based synchronization tracking controller is designed to achieve the high-precision synchronization tracking control of the MAMSs. The stability of overall synchronization tracking system with proposed learning procedure is analyzed. Finally, simulations and experiments are performed on fourmotor motion system to demonstrate the practicability and effectiveness of the proposed control strategy.

Synchronized Tracking Control of Dynamic System of Unmanned Rear-Wheel Vehicles Based on Dynamic Analysis

From the classic automatic guided vehicle system, the system of the unmanned rear-wheel drive vehicle (URWDV) based on a dynamic analysis is studied. In the URWDV system, the relationship among the position information, velocity, and the heading angular velocity of the unmanned vehicle is established in the plane coordinate system and the coordinate system centered vehicle itself. The velocity and heading angular velocity values are obtained through a dynamic analysis and are used as control parameters. The synchronized tracking control of the unmanned vehicle is realized by the control scheme of the velocity and the heading angular velocity. Finally, the simulation examples show the effectiveness of the tracking control.

Adaptive Performance-Constrained Synchronization Control for Hypersonic Flight Vehicle Swarms: A Novel Finite-Time Convergence Protocol Methodology

This paper proposes a novel neural adaptive performance-constrained synchronization tracking control algorithm for multiple hypersonic flight vehicles (HFVs), which are subject to actuator faults and full-state constraints. The proposed method is based on advanced Lyapunov finite-time stability theory and a sophisticated backstepping design scheme. The longitudinal model of HFV is converted into velocity and altitude subsystems through functional decomposition. Our method presents three significant contributions over the existing state-of-the-art approaches: (a) ensuring finite-time convergence of HFVs systems by guaranteeing that the setting time is lower bounded by a positive constant that is related to the initial states; (b) utilizing a tan-type Barrier Lyapunov function (BLF) to ensure that the synchronization tracking errors of velocity, altitude, flight path angle, angle of attack, and pitch angle rate are maintained within certain performance bounds; and (c) designing a neural adaptive control algorithm and adaptive parameter laws by combining the backstepping design technique and radial basis function neural networks (RBFNNs) to handle unknown actuator faults and modeling uncertainties. Finally, comparative simulations are conducted to validate the efficacy of the proposed scheme.

Terminal sliding mode observer based dynamic surface quasi‐synchronization control of multiple electro‐hydraulic systems under compositive outputs constraint

AbstractThis paper focuses on the high accuracy synchronous control issue of multiple electro‐hydraulic systems (MEHS). For the parametric uncertainty and external load disturbance of electro‐hydraulic systems (EHS), a type of terminal sliding mode observer (TSMO) is introduced for MEHS in this paper, which able to estimate the parametric uncertainty and external load disturbance in finite time. Then, based on the proposed fast uncertainty estimation strategy, a synchronous controller is designed for MEHS, in which both dynamic surface design and barrier Lyapunov function (BLF) method has been adopted to realize a better tracking performance of MEHS. The feasibility and effectiveness of proposed algorithms in this paper are verified by simulation. Then, a series of comparative experimental results are provided to illustrate the advantages of both disturbance estimation capacity of TSMO and synchronous tracking control performance of proposed control strategy for MEHS.

Cooperation and Coordination Transportation for Nonholonomic Mobile Manipulators: A Distributed Model Predictive Control Approach

This article addresses the problem of cooperation and coordination transportation for decoupling nonholonomic mobile manipulators (NMMs) in a workspace with obstacles. We propose a distributed model predictive control (MPC) approach for a team of NMMs to transport a target object while satisfying significant constraints and limitations, such as the feasible state and control input constraints, parameter synchronization constraints, and obstacles within the workspace. First, under the framework of the decoupling dynamics, an auxiliary dynamics model for task-space end-effectors and null-space mobile bases is obtained by the nonlinear feedback technique based on the Euler–Lagrange description of the NMMs. Using the modified virtual structure method, the cooperation and coordination transportation problem for NMMs is simplified as two independent synchronization tracking control problems for task-space end-effectors and null-space mobile bases. A distributed constrained optimization problem is established by taking the parameter synchronization and system constraints into the cost function. A general projection neural network (GPNN) approach is employed to solve the optimization problem and obtain the optimal control input. Moreover, a sufficient condition that guarantees the stability of the closed-loop system is further developed. Simulation results show that the proposed cooperation and coordination transportation strategy is feasible and effective.

Distributed synchronization control for multiple tailless flying‐wing UAVs with predefined tracking accuracy and input nonlinearities

SummaryThis article addresses the distributed adaptive synchronization tracking control problem for multiple flying‐wing UAVs with predefined accuracy and unknown input nonlinearities. Considering that the multiple flying‐wing unmanned aerial vehicles (UAVs) system is a large‐scale nonlinear and strongly coupled system, the control schemes in existing literature which only consider longitudinal, lateral or attitude control may not be applicable in practical engineering. Thus, a full‐state flying‐wing UAV model containing both translational and rotational motions is established, whose dynamics are six‐degree‐of‐freedom with twelve‐state‐variables. Furthermore, to handle the problem that the prior knowledge of the actuator nonlinearities of multi‐UAV system are difficult to obtain, a more general input nonlinear model is established and the adaptive boundary estimation technique is developed. Finally, a performance‐oriented controller is proposed by incorporating a series of smooth functions into adaptive neural control design and Lyapunov analysis. Theoretical analysis and simulation results show that the presented control scheme can guarantee the satisfactory transient tracking error performance, and the tracking error converges to a user‐defined interval ultimately.

Distributed Adaptive NN-Based Attitude Synchronous Tracking Control with Input Saturation

The attitude synchronization tracking problem for spacecraft formation flying is investigated in this paper based on sliding-mode control and a Chebyshev neural network (ChNN). A distributed attitude cooperative controller is designed for a group of spacecrafts to guarantee that each individual spacecraft will track the reference attitude of the virtual leader in the presence of external disturbances, system uncertainties and input saturation. An adaptive ChNN is introduced to approximate the system nonlinear uncertainties and bounded external disturbances online, and a switch function, which acts as a switching signal between the adaptive ChNN controller and the robust control law, is applied to limit the output of the ChNN approximator. Then, utilizing Nussbaum-type functions, an auxiliary control system is designed to counteract the nonlinearities caused by input saturation. Finally, a numerical simulation example is provided to illustrate the robustness and effectiveness of the proposed attitude control scheme.

Enhanced Recurrent Fuzzy Neural Fault-Tolerant Synchronization Tracking Control of Multiple Unmanned Airships via Fractional Calculus and Fixed-Time Prescribed Performance Function

This article proposes a fractional-order intelligent fault-tolerant synchronization tracking control (FO-I-FTSTC) scheme for multiple unmanned airships (UAs) against actuator faults. Within the developed control architecture, fixed-time prescribed performance functions (PPFs) are first designed to transform the synchronization tracking errors into a new set of error variables, such that the original errors are strictly confined within the prescribed bounds. Then, fractional calculus and sliding mode surface are sequentially introduced to construct the FO errors. Moreover, to handle the unknown terms and bias faults in the FO sliding-mode error dynamics, fuzzy neural networks with recurrent loops are artfully constructed to act as the intelligent learning units. Furthermore, the norm of the loss-of-effectiveness fault factors is introduced for each UA to reduce the number of adaptive parameters. The distinct feature of the proposed method is that the FO-I-FTSTC performance is significantly enhanced by integrating recurrent fuzzy neural networks, fractional calculus, and fixed-time PPFs into a unified framework, leading to a high-precision control scheme. It is shown by Lyapunov analysis that all UAs can track their desired references in a synchronized manner, and the synchronization tracking errors are bounded and strictly confined within the prescribed error bounds. Comparative hardware-in-the-loop experiments are presented to show the effectiveness of the proposed FO-I-FTSTC scheme.

Synchronization tracking control of networked multi-axis motion systems: A cooperative distributed model predictive control approach

Synchronization tracking control is an essential technique in networked multi-axis motion systems (NMAMSs). However, the uncertainties induced by network, such as delay and packet disordering, have great influence on control performance of the motion systems. To improve the influence of the uncertainties induced by network on synchronization tracking control performance in NMAMSs, a cooperative distributed model predictive control (CDMPC) strategy for NMAMSs is proposed. Firstly, the synchronization tracking control of the NMAMS is designed in a networked control framework. By introducing the synchronization error into performance index as a coupling term, a local controller adopting a CDMPC strategy is designed for each subsystem. Then, a Nash optimization iteration strategy is presented to deal with the CDMPC optimization problem, and the convergence and stability of the system are also analyzed. Finally, experiments are carried out to demonstrate the practicability and effectiveness of the proposed control strategy.

Adaptive neural synchronized impedance control for cooperative manipulators processing under uncertain environments

In robotic cooperation manufacturing occasions, like grinding, assembling, welding, etc., the position-force synchronization tracking control for robotic cooperative manipulators is critical to improve the comprehensive manufacturing quality with high-precision and high-adaptability. In terms of these problems, this paper proposes an adaptive neural synchronized impedance controller (ANSIC) for cooperative manipulators processing. The proposed method includes two non-parallel control loops of the cooperative system to achieve and guarantee the desired movement trajectory and manufacturing force of the cooperation task. In the inner position tracking loop, an adaptive RBF-neural network based synchronization sliding controller is designed to simultaneously estimate the uncertain dynamic parameters of the robotic manipulators and improve the cooperative position tracking precision. In the outer force tracking loop, another RBF-neural network is applied to reform the impedance control model automatically and compensate the position and stiffness errors of the uncertain workpiece environment. Mathematical proof and experiments under various conditions are conducted. The results demonstrate the effective convergences of both the cooperative processing trajectory and force despite the uncertain environments.

Synchronization Tracking Of Uncertain Nonidentical Multi-USV system

We propose a distributed adaptive synchronous tracking control algorithm for the heterogeneous multi-unmanned surface vehicles described by the Euler-Lagrange system. In multi-unmanned surface vehicles system, the inter-communication state can be represented by a directed topology graph, and only part of unmanned surface vehicles can directly acquire the accurate information of time-varying reference trajectory. The proposed algorithm can implement synchronous tracking for multiple unmanned surface vehicles without knowing global communication information by elaborately selecting Lyapunov functions. We conduct theoretical analysis of the stability of closed-loop systems according to the Lyapunov stability theory. And we also verify the effectiveness of proposed scheme through numerical simulation.

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.

Fractional-Order Adaptive Fault-Tolerant Synchronization Tracking Control of Networked Fixed-Wing UAVs Against Actuator-Sensor Faults via Intelligent Learning Mechanism.

This article presents an enhanced fault-tolerant synchronization tracking control scheme using fractional-order (FO) calculus and intelligent learning architecture for networked fixed-wing unmanned aerial vehicles (UAVs) against actuator and sensor faults. To increase the flight safety of networked UAVs, a recurrent wavelet fuzzy neural network (RWFNN) learning system with feedback loops is first designed to compensate for the unknown terms induced by the inherent nonlinearities, unexpected actuator, and sensor faults. Then, FO sliding-mode control (FOSMC), involving the adjustable FO operators and the robustness of SMC, are dexterously proposed to further enhance flight safety and reduce synchronization tracking errors. Moreover, the dynamic parameters of the RWFNN learning system embedded in the networked fixed-wing UAVs are updated based on adaptive laws. Furthermore, the Lyapunov analysis ensures that all fixed-wing UAVs can synchronously track their references with bounded tracking errors. Finally, comparative simulations and hardware-in-the-loop experiments are conducted to demonstrate the validity of the proposed control scheme.