Practical Tracking Research Articles

Practical Consensus Tracking of Asynchronously Switched Multiagent Systems

Practical Consensus Tracking of Asynchronously Switched Multiagent Systems

Adaptive Free‐Will Arbitrary Time Tracking of a Class of Uncertain SISO Nonlinear Systems

ABSTRACTThis paper investigates the free‐will arbitrary time (FWAT) tracking problem for a class of single‐input single‐output (SISO) strict‐feedback nonlinear systems without/with parametric uncertainties. First, we design an FWAT backstepping tracking controller for strict‐feedback nonlinear systems without uncertainties in a recursive manner. It is shown that the proposed virtual controllers and their derivatives are continuously differentiable for the backstepping design, and the tracking error converges to zero within the pre‐specified settling time independent of design parameters and initial conditions and remains at zero after the pre‐specified settling time. Second, we address the adaptive FWAT tracking problem of SISO strict‐feedback nonlinear systems with parametric uncertainties in the practical stability sense. The continuously differentiable and nonsingular time‐varying adjustment functions for practical FWAT tracking are developed to transform error variables for the recursive backstepping design. Then, the adaptive FWAT backstepping tracking design and stability analysis strategy are established to ensure practical FWAT convergence within the pre‐specified settling time independent of design parameters and initial conditions. The designed adaptive virtual and actual controllers are continuous for all . Furthermore, a practical adaptive prescribed‐time tracking scheme is provided using the proposed practical adaptive FWAT tracking scheme. Finally, the effectiveness of the theoretical approaches is confirmed through a simulation example and an experimental result.

Finite‐time practical tracking for a class of high‐order nonlinear systems with time‐varying full‐state constraints via event‐triggered control

AbstractThe finite‐time practical tracking problem is studied for a class of high‐order nonlinear systems with time‐varying full‐state constraints via event‐triggered control. In order to remove the feasibility conditions required by the traditional barrier Lyapunov functions (BLFs) method when dealing with state constraints, this paper introduces the nonlinear transformation functions (NTFs) for the states to transform the constrained system into an equivalent unconstrained system. An event‐triggered controller is designed with a wider application range and more suitable for the tracking control system by combining the switching threshold event‐triggered strategy. The purpose of reducing resource consumption is realized. The finite‐time stability of the closed‐loop system is proved by the finite‐time theory without breaking the state constraints. Finally, numerical and practical examples are given to verify the effectiveness of the proposed results.

Fixed-Time Practical Antisaturation Attitude Tracking Control of QUAV With Prescribed Performance: Theory and Experiments

Fixed-Time Practical Antisaturation Attitude Tracking Control of QUAV With Prescribed Performance: Theory and Experiments

Practical tracking control for a class of uncertain MIMO nonlinear systems with unmatched disturbances

This paper studies the output tracking control problem for a class of uncertain MIMO nonlinear systems. The motivation comes from how to deal with unknown coupling system dynamics between subsystems and achieve the desired tracking when the system has parameter uncertainties and external disturbances. The difficulty is to guarantee that tracking errors enter an arbitrarily prescribed neighbourhood within a finite time. Based on the construction of a time-varying dynamic gain associated with tracking errors and the effective manipulation of nonlinear parametrization, a novel robust feedback controller is built up to ensure the boundedness of all closed-loop signals and the convergence of tracking errors to a small neighborhood approaching the origin in a finite time. Finally, the feasibility of control strategy is verified by numerical and practical examples.

Practical fixed-time non-singular sliding mode control of second order nonlinear dynamic systems with chattering and overshooting avoidance

This paper introduces a Practical Fixed-Time Stabilization technique (PFxT) designed for a specific class of nonlinear second-order systems. The closed-loop systems, when appropriately parameterized, exhibit convergence within a predetermined time frame to a confined region near the origin. This outcome is achieved by amalgamating a nonlinear sliding mode approach with a practical fixed-time tracking virtual trajectory. The control algorithmś design incorporates considerations for overshoot reduction and chattering cancellation. Furthermore, the Lyapunov method is employed to establish the practical fixed-time stability of the proposed solution, providing insights into the limits within which the algorithm can be effectively deployed. To complete the algorithm specification, a parameter selection approach is introduced, enabling customization of the desired settling time. Comprehensive simulations are conducted to validate the effectiveness and viability of the proposed PFxT technique.

Adaptive Event-Triggered Fixed-Time Practical Tracking Control for Uncertain Nonlinear Systems

Adaptive Event-Triggered Fixed-Time Practical Tracking Control for Uncertain Nonlinear Systems

TRAFFIC VIOLATION PREDICTION USING DEEP LEARNING BASED ON HELMETS WITH NUMBER PLATE RECOGNITION

At the moment, two-wheelers are the most widely used kind of transportation. It is highly advised that both bike riders and soldiers wear helmets. Many academics are interested in object tracking in video surveillance, which is an important application and burgeoning field of study in image processing and machine learning. Finding objects in an image with a bounding box and different categories or shapes of the objects placed is called object detection. This study reviews tracking techniques, classifies them into several categories, and concentrates on significant and practical tracking approaches. After reviewing broad strategies under a scan of the literature on various methodologies, we analyze potential research areas. This study uses the YOLOv8 algorithm, an image processing technique, to identify motorcycle riders who do not wear helmets. Additionally, put the Optical Character Recognition method into practice to identify the license plate in a picture and retrieve user information. Next, determine the fine amount. Eventually, SMS services will be able to notify consumers in order to prevent motorbike accidents. We evaluate the framework in terms of speed and accuracy.

Repose angle prediction of railway ballast based on Hopper Flow Test and PCA-Stacking ensemble learning method

The deterioration of track ballast is mainly reflected in the fragmentation of ballast particles and the evolution of grading. The repose angle of the ballast is the internal friction angle, reflecting the shear strength of the track ballast. The angle of repose of 158 ballast samples with varying gradation parameters was tested by conducting hopper flow tests on ballast particles. Then, based on this database, a stacking ensemble regressor-based principal component analysis (PCA) was proposed for the repose angle prediction. The data are first processed by PCA and then randomized into the train (80%) and test (20%) data. Besides stacking ensemble regressor, five different individual regressors, including support vector regressor, k-nearest neighbors, random forest, gradient boosting decision tree, and adaptive boosting, are compared and analyzed. The results indicate that the stacking ensemble regressor performs better than single regressors and demonstrates enhanced robustness and generalization capabilities. Additionally, the Effect of the number of principal component indicators on the prediction accuracy is determined, and the difference in stacking ensemble prediction performance with and without PCA is discussed. Finally, a computer prediction system for ballast repose angle was developed to make the proposed stacking ensemble regressor directly applicable to the railroad construction site. This model can be used in practical track maintenance decisions, whereby the ballast gradation can facilitate rapid assessment of the angle of repose.

Event‐triggered practical tracking control via switching for a class of uncertain high‐order nonlinear systems with unknown input powers and control directions

AbstractThis article is devoted to the event‐triggered practical tracking control for a class of high‐order nonlinear systems with serious uncertainties. Remarkably, the serious uncertainties are greatly reflected from the unknown control directions and input powers which give rise to essential obstacles in control design and hence lead to incapability of the traditional control methods on this topic. To solve the control problem, a novel switching event‐triggered control scheme is proposed by a skillful incorporation of logic‐based switching mechanism into the event‐triggered one. Specifically, two pivotal events are smartly chosen to give the event‐triggered mechanism for the determination of the sampling times. In particular, one of the events is used to give the switching varying mechanism, by which the controller parameters are respectively switched in two groups of sequences which are carefully chosen for the compensation of serious uncertainties. Under the proposed switching event‐triggered mechanism, a state‐feedback controller is designed which guarantees that all the signals of the closed‐loop system are bounded while system output practically tracks the reference signal, along with a finite number of switching of controller parameters as well as the exclusion of the Zeno phenomenon. Finally, simulation examples are provided to validate the effectiveness of the proposed theoretical results.

Event-based appointed-time cooperative formation for linear multiagent systems with actuator saturation

This paper considers the time-varying formation (TVF) tracking issue of a class of multiagent systems (MASs) with input saturation. Meanwhile, the prescribed transient and steady-state performance of MASs is guaranteed. Compared to the existing prescribed performance control (PPC) approaches, the maximum convergence time of the designed algorithm is preassigned. To save network resources, an event-based protocol is introduced to achieve lower information communication frequency, where Zeno behavior is excluded. Furthermore, an improved control strategy is employed where an auxiliary system is designed to improve the performance under actuator saturation. According to Lyapunov stability theory, the expected practical TVF tracking control problem is solved for the MASs. Finally, the effectiveness of the designed schemes is demonstrated by the simulation examples.

Smooth least absolute deviation estimators for outlier-proof identification

The paper proposes to identify the parameters of linear dynamic models based on the original implementation of least absolute deviation estimators. It is known that the object estimation procedures synthesized in the sense of the least sum of absolute prediction errors are particularly resistant to occasional outliers and gaps in the analyzed system data series, while the classical least squares procedure unfortunately becomes of little use for reliably identifying systems in the presence of destructive measurement errors. Bearing in mind that the classic task of minimizing the quality functional of absolute deviations encounters fundamental analytical problems, it is proposed to use a dedicated iterative estimator for off-line evaluation of the parameters of the analyzed process. In addition, a simplified recursive version of the absolute deviation estimation procedure was developed, which allows for practical on-line tracking of the evolution of variable parameters of non-stationary systems. Importantly, a novel refinement of the discussed absolute deviation estimators was proposed to effectively overcome some inconvenient numerical effects. We also present an interesting comparison of the improved (by non-linear modification) iterative absolute-deviation estimator with the classical Gauss-Newton gradient algorithm, which leads to constructive conclusions. Finally, using computer simulations, the operation of the developed iterative and recursive estimators minimizing the absolute deviation is illustrated. The work ends with an indication of directions for further research.

Global adaptive practical tracking control for high‐order uncertain nonstrict feedback nonlinear systems with unknown control coefficients

SummaryIn this article, the problem of global adaptive practical tracking for high‐order uncertain nonstrict feedback nonlinear systems with unknown control coefficients is studied. To avoid the algebraic loop problem associated with the nonstrict feedback condition and guarantee the controllability of the tracking error, a novel dual dynamic gain scaling method is introduced to compensate nonlinearities and the tracking error simultaneously. Besides, by incorporating the sign functions into the design of adding a power integrator, a general approach for the handing of unknown control coefficients and the direction design of the controller is developed. The presented control scheme can ensure that all system states are globally bounded without constraints on state variables while the reference signal is tracked with expected precision. Three simulation examples, including a practical application, are provided to illustrate the validity of the control scheme.

Practical Tracking Control for High-order Nonlinear Systems With Dynamic Uncertainties and Unknown Powers via Event-triggered Mechanism

Practical Tracking Control for High-order Nonlinear Systems With Dynamic Uncertainties and Unknown Powers via Event-triggered Mechanism

Global Practical Output Tracking for a Class of Uncertain Nonlinear Systems

This paper addresses the problem of global practical output control using output feedback for a class of uncertain nonlinear systems with time-varying delay. First, a homogeneous output feedback controller is designed for a nominally uncertain system by adding power integrator technology. Then, with the help of an appropriate Lyapunov-Krasovsky functional and a reduced-order observer, using the homogeneous dominance approach and adding a power integrator method, an output feedback controller is successfully designed to ensure that all states of the closed-loop system remain constrained while simultaneously making the tracking error arbitrarily small.

Practical consensus tracking control for networked Euler–Lagrange systems based on UDE integrated with RBF neural network

This paper solves the practical consensus tracking control problem for networked Euler–Lagrange (EL) systems using the uncertainty and disturbance estimator (UDE) in combination with the radial basis function (RBF) neural network. An integrated consensus algorithm is proposed for the networked EL systems, where the RBF neural network is first utilized to generate online estimation of the uncertain EL dynamics for enhancing the system adaptability, and then the UDE is applied to compensate external disturbances dynamically for improving the system robustness. Furthermore, a unified analysis framework for the closed-loop control system is established through performing the proper filter design and Lyapunov-like analysis. The proposed consensus tracking algorithm possesses a simple system structure for implementation and has a good robust performance in dealing with both uncertainties and disturbances. Numerical simulations are used to verify the effectiveness of the developed integrated consensus scheme.

Neuroadaptive Output Formation Tracking for Heterogeneous Nonlinear Multiagent Systems With Multiple Nonidentical Leaders.

This article investigates the practical time-varying output formation tracking (TVOFT) problem for heterogeneous nonlinear multiagent systems (MASs) having multiple leaders, where agents herein could have heterogeneous dynamics and interact with each other under event-triggered communications. It is required that the outputs of followers not only track the predefined convex combination of multiple leaders but also achieve the desired time-varying formation simultaneously. The existing works on formation tracking problems for MASs with multiple leaders depend on the assumption that each follower is a well-informed or uninformed follower, where the well-informed follower is required to have all the leaders as its neighbor. To remove the limitation, a fully distributed observer-based formation tracking control protocol is developed and employed. First, an adaptive state observer with an edge-based event-triggered mechanism for estimating the states of multiple leaders is proposed based on the neighboring interactions, which eliminates the unexpected Zeno behavior. Second, a novel observer is constructed for each follower by exploiting the output information of the follower, in which the adaptive neural network (NN)-based approximation is exploited to compensate for the unknown nonlinearity. A practical TVOFT control protocol is then generated by the proposed observers, where the parameters are determined by an algorithm including five steps. With the help of Lyapunov stability theory and output regulation method, a practical TVOFT criterion for the considered closed-loop system is derived. Finally, the effectiveness of the proposed control scheme is illustrated by a numerical example.

A dual-gain scaling approach to event-triggered adaptive tracking for large-scale nonlinear systems via output feedback

A novel non-backstepping output feedback control approach, called as the dual-gain scaling approach, is proposed by combining the extended Lyapunov matrix inequalities with dual-domination idea. This approach provides partially decentralised adaptive output feedback controllers with three event-triggered mechanisms to achieve the global practical tracking of a family of uncertain large-scale nonlinear systems subject to unknown control coefficients and strongly interconnected nonlinearities with output-polynomial growth rate. The dual gains are embedded in the observers and controllers, which can effectively compensate for time-varying control coefficients and unknown constant growth rate multiplied by output-polynomial function. Three different threshold schemes including fixed threshold, relative threshold and time-varying threshold are introduced to save communication resources. The stability analysis shows that the solutions of the closed-loop system are globally uniformly bounded and the tracking error converges to a preset compact set after a finite time; also, there will be no zeno behaviour in the closed-loop response.

Global practical tracking for uncertain nonlinear systems with unknown control coefficients and polynomial growth

This paper studies the problem of global output-feedback tracking control for a class of uncertain nonlinear systems. The system studied in this paper has unknown time-varying control coefficients and unknown reference signal. Notably, the nonlinearities of the system are bounded by the lower triangular linear unmeasured states multiplying the unknown constant, the polynomial-of-output and the polynomial-of-input growth rates, which indicates the presence of serious uncertainties. Motivated by the closely related works, by combining the ideas of universal control and deadzone with backstepping technique, this paper presents a new adaptive tracking control scheme based on two dynamic high-gains in the new forms. The proposed control scheme ensures that the tracking error converges to a specified arbitrarily small interval range after a finite time while the state of the resulting closed-loop system is globally bounded. Finally, the effectiveness of the control scheme is verified by an illustrative example.

Command filter-based adaptive neural tracking control of nonlinear systems with multiple actuator constraints and disturbances

In this paper, the adaptive practical finite-time tracking control problem for a class of strictly feedback nonlinear systems with multiple actuator constraints is investigated using backstepping techniques and practical finite-time stability theory. The effects of deadband and saturated nonlinear constraints on the controller design of nonlinear systems are addressed by the equivalent transformation method. The problem of complexity explosion due to the derivatives of virtual control signals is solved by using the virtual control signals as inputs to the command filters and using the outputs of the command filters to perform the corresponding control tasks. An adaptive neural network tracking backstepping control strategy based on the command filter technique and the backstepping design algorithm is proposed by approximating an unknown nonlinear function using a neural network. The control strategy ensures the boundedness of all variables in the closed-loop system, and the output tracking error fluctuates in a small region near the origin. Finally, simulations verify the effectiveness of the control strategy designed in this paper.