Coordinated Tracking Control Research Articles

Refined Fractional-Order Fault-Tolerant Coordinated Tracking Control of Networked Fixed-Wing UAVs Against Faults and Communication Delays via Double Recurrent Perturbation FNNs.

This article investigates the fault-tolerant coordinated tracking control problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults and communication delays. By supplementing the commonly used Gaussian functions in the fuzzy neural networks (FNNs) with sine-cosine functions and constructing two kinds of recurrent loops within the FNN architecture, double recurrent perturbation FNNs are cleverly designed to learn the unknown terms containing faults and uncertainties. Then, adaptive laws are designed for double recurrent perturbation FNNs. Moreover, by assimilating fractional-order calculus into the sliding-mode surfaces and the control signals, refined transient-state and steady-state adjustment performances can be obtained. It is shown by Lyapunov stability analysis that all fixed-wing UAVs can coordinately track their desired trajectories and the tracking errors are uniformly ultimately bounded. Comparative simulation results are provided to show the effectiveness of the proposed control strategy.

A novel adaptive coordinated tracking control scheme for a morphing aircraft with telescopic wings

Inspired by flight biology, morphing flight technology has great potential to improve the adaptability and maneuverability of aircraft. This paper is devoted to the flight control problem of morphing aircraft, and aimed at safe and fuel-saving flight through morphing actively. Specifically, the longitudinal dynamics of a morphing aircraft with telescopic wings is modelled as a strict-feedback nonlinear system. Through fitting the expression of aerodynamic parameters by the morphing ratio, the model uncertainties induced by morphing errors are embedded in the dynamics. To meet the safety and fuel-saving requirements, an Adaptive Coordinated Tracking Control Scheme (ACTCS) is then proposed, which consists of a morphing control module and a tracking control module. For the morphing control module, an on-line morphing decision model is given in an optimization process with respect to the morphing ratio, and a second-order tracking filter is introduced to smooth the decision output and ensure the physical realizability. For the tracking control module, the novel adaptive controllers for the velocity and altitude subsystems are proposed based on the dynamic surface control method, in which adaptive mechanisms are designed to compensate for the model uncertainties. Finally, the proposed ACTCS is simulated in nine different cases of the test flight mission, to verify its effectiveness, robustness and fuel-saving effect.

Error-constrained Coordinated Tracking Control for High-order Multiagent Systems Based on Barrier Lyapunov Function

Error-constrained Coordinated Tracking Control for High-order Multiagent Systems Based on Barrier Lyapunov Function

Coordinated Saturated Output-Feedback Control of an Autonomous Tractor-Trailer and a Combine Harvester in Crop-Harvesting Operation

This paper proposes a coordinated tracking controller for a tractor-trailer to keep a desired distance and heading angle relative to a combine harvester for the autonomous unloading of harvested crops in automatic agriculture operations. A coordinate transformation and the prescribed performance control (PPC) technique are employed to develop a second-order Euler-Lagrange formulation of the tracking errors. Then, a neural adaptive saturated velocity observer-based controller is proposed to force that the trailer center is exactly placed under the end point of the harvester unloading tube to collect cereal grains. By an effective application of the PPC technique and computing a safe motion boundary, controller non-singularity, collision avoidance and connectivity between two vehicles are preserved continuously. The model uncertainties including unknown vehicle parameters, variable trailer mass and moment of inertia during the crop collection, surface friction, climate and crop conditions and external disturbances are compensated by an effective combination of a neural network and an adaptive robust control law. Lyapunov’s direct method is employed to prove that the tracking errors are semi-globally uniformly ultimately bounded and converge to a neighborhood of the origin with a pre-specified maximum overshoot, convergence rate and steady-state accuracy. Finally, comparative simulation results are presented to demonstrate the effectiveness of the proposed controller for the crop harvesting operations in a practical perspective.

Adaptive Cooperative Tracking Control of Multiple Trains With Uncertain Dynamics and External Disturbances Based on Adjacent Information

Automatic train operation is an important part of the train control system. As train operating intervals continue to shorten and train speeds continue to increase, multiple train cooperative control is currently an important technology to further improve the efficiency of train operation and line passing capacity. However, considering various factors such as the nonlinearity and uncertainty of the train dynamics model and the complexity of the line conditions, this creates even greater demands on the design of the controller. In this study, we propose an adaptive cooperative tracking control method for multiple trains using adjacent information. For the multiple-train coordinated tracking control in the presence of model uncertainties, unknown parameters, and external disturbances, a distributed cooperative control scheme for multiple trains is designed using the displacement, velocity, and acceleration information of adjacent trains, combined with radial basis function neural networks and adaptive methods. A fast high-order sliding mode observer is used to estimate the train velocity and acceleration information. Stability and convergence are proved for single and multiple trains utilizing Lyapunov stability analysis. Simulation examples demonstrate the effectiveness of the proposed controller.

Fixed-time formation control for spacecraft with prescribed performance guarantee under input saturation

This paper investigates the fixed-time stability of spacecraft formation flying by considering input saturation and prescribed performance constraints, including the safe distance constraint between members and control accuracy. By analyzing the relationship between the positional tracking error and the minimum safe distance between spacecrafts, a new form of the prescribed performance function with fixed-time convergence is developed to constrain the overshoot and accuracy of the tracking error. A nonsingular saturated terminal sliding-mode surface is developed to stabilize the system states in fixed time. A fixed-time coordinated tracking controller with input and rate saturation is finally developed by combining sliding-mode control, the back-stepping method, fixed-time observers, an anti-windup saturation compensator, and an auxiliary system. The stability of the closed-loop system is validated by Lyapunov stability theory. The results of simulation verified the effectiveness of the proposed control algorithm.

Distributed Adaptive Coordinated Control of Multiple Euler–Lagrange Systems considering Output Constraints and Time Delays

In this paper, we mainly investigate the coordinated tracking control issues of multiple Euler–Lagrange systems considering constant communication delays and output constraints. Firstly, we devise a distributed observer to ensure that every agent can get the information of the virtual leader. In order to handle uncertain problems, the neural network technique is adopted to estimate the unknown dynamics. Then, we utilize an asymmetric barrier Lyapunov function in the control design to guarantee the output errors satisfy the time-varying output constraints. Two distributed adaptive coordinated control schemes are proposed to guarantee that the followers can track the leader accurately. The first scheme makes the tracking errors between followers and leader be uniformly ultimately bounded, and the second scheme further improves the tracking accuracy. Finally, we utilize a group of manipulator networks simulation experiments to verify the validity of the proposed distributed control laws.

Relative position coordinated control for spacecraft formation flying with obstacle/collision avoidance

The problem of the relative position coordinated control for spacecraft formation flying with a leader spacecraft under the obstacle environment is the focus of this paper. To avoid obstacle/collision and maintain the formation configuration, the Null-Space-Based behavioral control architecture is built by defining the priorities of the basic tasks and computing the corresponding velocity vectors. Through the null-space projection, the desired velocity of each follower spacecraft can be calculated by merging the basic tasks. Moreover, due to the partial access to the dynamic leader spacecraft’s states, the distributed estimators are presented for each follower spacecraft. Then, based on the desired velocity, the adaptive coordinated tracking control algorithm incorporated with the barrier Lyapunov function is designed such that the states satisfy the time-varying constraints, even subject to uncertainties and unknown disturbances. Finally, numerical simulations are performed to illustrate the main results.

Virtual Guidance-Based Coordinated Tracking Control of Multi-Autonomous Underwater Vehicles Using Composite Neural Learning.

This article proposes a virtual leader-based coordinated controller for the nonlinear multiple autonomous underwater vehicles (multi-AUVs) with the system uncertainties. To achieve the coordinated formation, a virtual AUV is set as the leader, while the desired command is designed using the relative position between each AUV and the virtual leader. The controller is designed based on the back-stepping scheme, and the online data-based learning scheme is used for uncertainty approximation. The highlight is that compared with previous learning methods which mostly focus on stability, the learning performance index is constructed using the collected online data in this article. The index is further used in the composite update law of the neural weights. The closed-loop system stability is analyzed via the Lyapunov approach. The simulation test on the five AUVs under fixed formation shows that the proposed method can achieve higher tracking performance with improved approximation accuracy.

Bilateral coordination control of flexible master–slave manipulators using a partial differential equation model

In this study, we evaluate the coordination tracking control problem of a flexible master–slave teleoperation system. The system under consideration is based on a dynamic model described by a set of partial differential equations. Existing research on bilateral controllers is based on teleoperation systems composed of rigid master robots and rigid or flexible slave robots. In this work, we consider teleoperation systems with flexible master and slave robots. We dynamically model flexible master–slave manipulators using partial differential equations. Based on the dynamic model, a bilateral coordination controller is developed to realize the coordination angle tracking and vibration suppression of flexible master–slave manipulators. The teleoperation system is proven to be asymptotically stable under the control scheme. Numerical simulation results illustrate that the proposed controller is effective.

Total-Amount Coordinated Finite-Time Control of Multi-Motors With Saturation Constraints

For the multi-motor traction finite-time control (FTC) system with input saturation constraints, considering parameter simplification of “adding a power integrator” (AAPI) and anti-windup (AW), a total-amount coordinated tracking control (TACTC) strategy based on nonsingular terminal sliding mode (NTSM) is proposed to ensure the global finite-time stability and effectively reduce energy consumption. First, a traction model of multiple permanent magnet synchronous motors (PMSMs) with uncertain parameter perturbations and external disturbances was established. Next, a finite-time auxiliary AW system was designed and the auxiliary state was fed back to TACTC. Then, the strong constraint of parameters of AAPI was relaxed, the setting rules of the corresponding parameters were given and the proof of finite-time Lyapunov stability was completed. Finally, the excellent performance of parameter simplification of AAPI and the controller was verified by the Matlab/Simuink simulation and RT-Lab semi-physical experiment of the multi-motor traction system, which shows that the control strategy can suppress the input saturation effect in the multi-motor traction system effectively.

Coordinated Tracking Control With Asynchronous Edge-Based Event-Triggered Communications

This paper studies the distributed event-triggered tracking control problem of general linear multiagent systems with a dynamic leader, whose control input might be nonzero and unknown. The existence of the leader's unknown input renders the network heterogeneous and largely increases the difficulty of designing distributed event-based protocols. To solve this problem, we establish a novel asynchronous edge-based event-triggered mechanism, under which communication is not required from the leader to its out-neighbors (informed followers) or by the edge, unless the embedded triggering functions are triggered. Using this mechanism, we design a static asynchronous edge-based event-triggered protocol and an adaptive one, both of which guarantee the achievement of the leader–follower consensus and the exclusion of the Zeno behavior. Note that the adaptive event-based protocol is fully distributed, requiring no global information of the network topology, such as the network's scale, the smallest nonzero eigenvalue of the Laplacian matrix, and the upper bound of the leader's control input.

Distributed adaptive fuzzy tracking algorithms for multiple uncertain mechanical systems

PurposeThis paper aims to investigate the distributed coordinated fuzzy tracking problems for multiple mechanical systems with nonlinear model uncertainties under a directed communication topology.Design/methodology/approachThe dynamic leader case is considered while only a subset of the follower mechanical systems can obtain the leader information. First, this paper approximates the system uncertainties with finite fuzzy rules and proposes a distributed adaptive tracking control scheme. Then, this paper makes a detailed classification of the system uncertainties and uses different fuzzy systems to approximate different kinds of uncertainties. Further, an improved distributed tracking strategy is proposed. Closed-loop systems are investigated using graph theory and Lyapunov theory. Numerical simulations are performed to verify the effectiveness of the proposed methods.FindingsBased on fuzzy control and adaptive control theories, the desired distributed coordinated tracking control strategies for multiple uncertain mechanical systems are developed.Originality/valueCompared with most existing literature, the proposed distributed tracking algorithms use fuzzy control and adaptive control techniques to cope with system nonlinear uncertainties of multiple mechanical systems. Moreover, the improved control strategy not only reduces fuzzy rules but also has higher control accuracy.

Backstepping-Based Distributed Finite-Time Coordinated Tracking Control for Multiple Uncertain Euler–Lagrange Systems

Based on the backstepping control theory, this paper investigates the distributed finite-time coordinated tracking control for Euler–Lagrange systems under directed graphs. We consider that only a portion of followers can receive information from the dynamic leader. Two cases are discussed in this paper: (1) the system having parameter uncertainties which can be linearized, and (2) the system having structured uncertainties and external disturbances which cannot be linearized. For the first case, parameter-linearity property is used to approximate the parametric uncertainties. For the second case, neural networks are used to approximate the nonlinear uncertainties and external disturbances. For the controller design, first, we design an auxiliary variable. Then, backstepping method and Lyapunov stability theory are used to prove that the tracking errors and adaptive estimation errors are bounded. Finally, the finite-time convergence property of the tracking errors is proved by increasing control gains. Numerical examples and comparisons with other methods are provided to show the effectiveness and superiorities of the proposed methods.

Connectivity enhancing coordinated tracking control of multi-agent systems with a state-dependent jointly-connected dynamic interaction topology

We revisit the problem of virtual leader tracking by a group of agents of second order dynamics when only a portion of the agents are informed of the information of the virtual leader, under a state-dependent interaction topology. By proposing a simpler controller structure, we obtain results on coordinated tracking control under a jointly connected topology, both in the presence and in the absence of the virtual leader velocity information. The simplification in control protocols also enables us to arrive at our second contribution on connectivity enhancing coordinated control. In comparison with the existing connectivity preserving control algorithms, our proposed connectivity enhancing mechanism is effective in maintaining both the initially existent topology edges and those newly formed topology edges that were not initially existent. Besides, our proposed algorithms are designed in such a way that the local Lipschitz condition is satisfied and thus technical issues on solution uniqueness of the closed-loop system are avoided. Our proposed technique applies to both consensus tracking and flocking.

Distributed Coordinated Tracking Control for Multiple Uncertain Euler–Lagrange Systems With Time-Varying Communication Delays

In this paper, under-directed topology, distributed coordinated tracking control schemes are proposed for multiple Euler–Lagrange systems with time-varying communication delays, nonlinear uncertainties, and external disturbances. Concerning with different leader velocities, both constant leader velocity case and time-varying leader velocity case are addressed by designing two different distributed observers. Combining with the proposed distributed leader velocity observers, two coordinated tracking control schemes are developed by the effort of neural network approximation and sliding mode technique, which can compensate the nonlinearities and uncertainties. For the first case, tracking errors are rigorously proved to be globally asymptotically converged by using Lyapunov–Krasovskii method. To further eliminate chattering caused by the discontinuous sign function, the saturation function is used for the second case, and the proposed control algorithm ensures the same convergence of tracking errors via Lyapunov analysis. Finally, the effectiveness of the proposed distributed tracking control schemes is verified by the numerical examples.

Adaptive Nonsingular Terminal Sliding Mode Backstepping Control for the Speed and Tension System of the Reversible Cold Strip Rolling Mill Using Disturbance Observers

In order to weaken the influences of system uncertainties and coupling items on the coordinated tracking control performance of the speed and tension system of the reversible cold strip rolling mill, an adaptive nonsingular terminal sliding mode (ANTSM) backstepping control strategy using disturbance observers is proposed. First, time-varying gain extended state observers (TVGESOs) are constructed to dynamically observe the system's mismatched uncertainties, which weaken the initial peak phenomenon of the traditional extended state observer (ESO), and effectively improve the system's tracking control precision. Next, based on the backstepping control and the second order sliding mode integral filters, adaptive nonsingular terminal sliding mode controllers (ANTSMCs) for the speed and tension system of the reversible cold strip rolling mill are designed, which solve the “differential explosion” problem of conventional backstepping control, and make sliding mode variables converge in finite time. Again, neural network adaptive method is used to approximate the system's matched uncertainties, and the approximation values are introduced into the designed controllers for compensations. Finally, simulation research is carried out on the speed and tension system of a 1422 mm reversible cold strip rolling mill by using actual data, and the results show the validity of the proposed control strategy.

Adaptive coordinated tracking control for multi-robot system with directed communication topology

In this article, an adaptive coordinated tracking control problem for a group of nonholonomic chained systems has been discussed under the assumption that the desired trajectory is available only t...

Hierarchical Distributed Motion Control for Multiple Linear Switched Reluctance Machines

This paper investigates a distributed, coordinated motion control network based on multiple direct-drive, linear switched reluctance machines (LSRMs). A hierarchical, two-level synchronization control strategy is proposed for the four LSRMs based motion control network. The high-level, reference signals agreement algorithm is first employed to correct the asynchronous behaviors of the position commands. Then, the low-level tracking synchronization method is applied for the collaborative position control of the four LSRMs. The proposed two-level, fault-tolerant control strategy eliminates the asynchrony of the reference signals and it also guarantees the coordinated tracking control performance of the four LSRMs. Experimental results demonstrate that effective coordinated tracking control can be ensured, based on the successful agreement of reference signals and an absolute tracking error falling within 2 mm can be achieved.

Neural Network Dynamic Surface Backstepping Control for the Speed and Tension System of Reversible Cold Strip Rolling Mill

AbstractTo weaken the influences of uncertainties and system coupling items on the coordinated tracking control performance of the speed and tension system of a reversible cold strip rolling mill, a control strategy is proposed based on nonlinear disturbance observers (NDOs), dynamic surface backstepping control, and neural network adaptive approximation. First, the transformation form of the system model is given, and then NDOs are developed to counteract the unmatched uncertainties. Next, controllers for the speed and tension system are presented by combining backstepping with dynamic surface control. Again, the neural network adaptive method is used to approximate the matched uncertainties of the system, and the approximation values are introduced into the designed controllers for compensation. Finally, simulation research is carried out on the speed and tension system of a 1422 mm reversible cold strip rolling mill by using the actual data, and the results show the validity of the proposed control strategy in comparison with the decentralized overlapping control strategy.