ADAPTIVE TRACKING CONTROL FOR A CLASS OF NONLINEAR COMPOSITE SYSTEMS *

In this paper, the problem of adaptive tracking control for a class of nonlinear large scale systems with unknown parameters entering linearly is discussed. Based on the theory of input output linearization of nonlinear systems, direct adaptive control schemes are presented to achieve bounded tracking. The proposed control schemes are robust with respect to the uncertainties in interconnection structure as well as subsystem dynamics. A numerical example is given to illustrate the efficiency of this method.

Adaptive tracking algorithm based on 3D variable turn model

Satisfactory results cannot be obtained when three-dimensional (3D) targets with complex maneuvering characteristics are tracked by the commonly used two-dimensional coordinated turn (2DCT) model. To address the problem of 3D target tracking with strong maneuverability, on the basis of the modified three-dimensional variable turn (3DVT) model, an adaptive tracking algorithm is proposed by combining with the cubature Kalman filter (CKF) in this paper. Through ideology of real-time identification, the parameters of the model are changed to adjust the state transition matrix and the state noise covariance matrix. Therefore, states of the target are matched in real-time to achieve the purpose of adaptive tracking. Finally, four simulations are analyzed in different settings by the Monte Carlo method. All results show that the proposed algorithm can update parameters of the model and identify motion characteristics in real-time when targets tracking also has a better tracking accuracy.

Adaptive tracking control for a class of uncertain chaotic systems

The paper is concerned with adaptive tracking problem for a class of chaotic system with time-varying uncertainty, but bounded by norm polynomial. Based on adaptive technique, it proposes a novel controller to asymptotically track the arbitrary desired bounded trajectory. Simulation on the Rossler chaotic system is performed and the result verifies the effectiveness of the proposed method.

Adaptive tracking H-infinity control for switched nonlinear systems with unknown control gain sign

This paper addresses the adaptive tracking control scheme for switched nonlinear systems with unknown control gain sign. The approach relaxes the hypothesis that the upper bound of function control gain is known constant and the bounds of external disturbance and approximation errors of neural＇networks are known. RBF neural networks （NNs） are used to approximate unknown functions and an H-infinity controller is introduced to enhance robustness. The adaptive updating laws and the admissible switching signals have been derived from switched multiple Lyapunov function method. It＇s proved that the resulting closed loop system is asymptotically Lyapunov stable such that the output tracking error performance and H-infinity disturbance attenuation level are well obtained. Finally, a simulation example of Forced Duffing systems is given to illustrate the effectiveness of the proposed control scheme and improve significantly the transient performance.

Adaptive Object Tracking Discriminate Model for Multi-Camera Panorama Surveillance in Airport Apron

Autonomous intelligence plays a significant role in aviation security.Since most aviation accidents occur in the take-off and landing stage,accurate tracking of moving object in airport apron will be a vital approach to ensure the operation of the aircraft safely.In this study,an adaptive object tracking method based on a discriminant is proposed in multi-camera panorama surveillance of large-scale airport apron.Firstly,based on channels of color histogram,the pre-estimated object probability map is employed to reduce searching computation,and the optimization of the disturbance suppression options can make good resistance to similar areas around the object.Then the object score of probability map is obtained by the sliding window,and the candidate window with the highest probability map score is selected as the new object center.Thirdly,according to the new object location,the probability map is updated,the scale estimation function is adjusted to the size of real object.From qualitative and quantitative analysis,the comparison experiments are verified in representative video sequences,and our approach outperforms typical methods,such as distraction-aware online tracking,mean shift,variance ratio,and adaptive colour attributes.

Adaptive Tracking for Beam Alignment Between Ship-Borne Digital Phased-Array Antenna and LEO Satellite

In mobile satellite communication networks employing digital beam forming technology,beam alignment imposes great influence on link quality and network efficiency.Owing to complex coupling motion by low earth orbit(LEO)satellite and ship,direction of arrival(DOA)of target satellite varies rapidly and nonlinearly.It then causes difficulty to accurately track the DOA.In this work,an adaptive tracking algorithm is proposed by exploiting advantages of flexible parameter configuration of digital phased-array antenna.The alignment process basically consists of observation and tracking.In the observation stage,two-dimensional(2-D)multiple signal classification(MUSIC)is applied by the ship-borne digital phased-array antenna to estimate beam direction of satellite;in the tracking stage,an extended Kalman filter(EKF)based adaptive tracking is designed to achieve fast and accurate alignment.The proposed adaptive tracking improves performance by adaptively estimating tracking parameters in EKF firstly.The estimation results are then used as feedback to adaptively adjust digital phased-array antenna parameters to improve estimation accuracy of DOA.Simulation results under sea state 5 show that the proposed tracking algorithm improves tracking accuracy and stability over conventional ones.

Fuzzy adaptive tracking control within the full envelope for an unmanned aerial vehicle

Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.

AN EXPONENTIAL INEQUALITY FOR AUTOREGRESSIVE PROCESSES IN ADAPTIVE TRACKING

A wide range of literature concerning classical asymptotic properties for linear models with adaptive control is available, such as strong laws of large numbers or central limit theorems. Unfortunately, in contrast with the situation without control, it appears to be impossible to find sharp asymptotic or nonasymptotic properties such as large deviation principles or exponential inequalities. Our purpose is to provide a first step towards that direction by proving a very simple exponential inequality for the standard least squares estimator of the unknown parameter of Gaussian autoregressive process in adaptive tracking.

Adaptive tracking control of high-order MIMO nonlinear systems with prescribed performance

In this paper,an observer-based adaptive prescribed performance tracking control scheme is developed for a class of uncertain multi-input multi-output nonlinear systems with or without input saturation.A novel finite-time neural network disturbance observer is constructed to estimate the system uncertainties and external disturbances.To guarantee the prescribed performance,an error transformation is applied to transfer the time-varying constraints into a constant constraint.Then,by employing a barrier Lyapunov function and the backstepping technique,an observer-based tracking control strategy is presented.It is proven that using the proposed algorithm,all the closedloop signals are bounded,and the tracking errors satisfy the predefined time-varying performance requirements.Finally,simulation results on a quadrotor system are given to illustrate the effectiveness of the proposed control scheme.

Mobility-Aware Adaptive Beam Tracking for Vehicles in Mm Wave Communication Networks

The millimeter wave(mm Wave)is a potential solution for high data rate communication due to its availability of large bandwidth.However,it is challenging to perform beam tracking in vehicular mm Wave communication systems due to high mobility and narrow beams.In this paper,an adaptive beam tracking algorithm is proposed to improve the network throughput performance while reducing the training signal overhead.In particular,based on the mobility prediction at base station(BS),a novel frame structure with dynamic bundled timeslot is designed.Moreover,an actor-critic reinforcement learning based algorithm is proposed to obtain the joint optimization of both beam width and the number of bundled timeslots,which makes the beam tracking adapt to the changing environment.Simulation results demonstrate that,compared with the traditional full scan and Kalman filter based beam tracking algorithms,our proposed algorithm can improve the time-averaged throughput by 11.34%and 24.86%respectively.With the newly designed frame structure,it also outperforms beam tracking with conventional frame structure,especially in scenarios with large range of vehicle speeds.

Adaptive Human Tracking Across Non-overlapping Cameras in Depression Angles

To track human across non-overlapping cameras in depression angles for applications such as multi-airplane visual human tracking and urban multi-camera surveillance,an adaptive human tracking method is proposed,focusing on both feature representation and human tracking mechanism.Feature representation describes individual by using both improved local appearance descriptors and statistical geometric parameters.The improved feature descriptors can be extracted quickly and make the human feature more discriminative.Adaptive human tracking mechanism is based on feature representation and it arranges the human image blobs in field of view into matrix.Primary appearance models are created to include the maximum inter-camera appearance information captured from different visual angles.The persons appeared in camera are first filtered by statistical geometric parameters.Then the one among the filtered persons who has the maximum matching scale with the primary models is determined to be the target person.Subsequently,the image blobs of the target person are used to update and generate new primary appearance models for the next camera,thus being robust to visual angle changes.Experimental results prove the excellence of the feature representation and show the good generalization capability of tracking mechanism as well as its robustness to condition variables.

Adaptive practical output tracking of a class of nonlinear systems

Focus is laid on the adaptive practical output-tracking problem of a class of nonlinear systems with high-order lower-triangular structure and uncontrollable unstable linearization. Using the modified adaptive addition of a power integrator technique as a basic tool, a new smooth adaptive state feedback controller is designed. This controller can ensure all signals of the closed-loop systems are globally bounded and output tracking error is arbitrary small.

Multifeature Statistical TV Tracker

A multifeature statistical image segmentation algorithm is described. Multiple features such as grey, edge magnitude and correlation are combined to form a multidimensional space statistics. The statistical algorithm is used to segment an image using the decision curved surface determined by the multidimensional feature function. The segmentation problem which is difficult to solve using the features independently will be readily solved using the same features jointly. An adaptive segmentation algorithm is discussed. Test results of the real-time TV tracker newly developed have shown that the segmentation algorithm discussed here improves effectively the image segmentation quality and system tracking performance.

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 mor-phing 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 com-pensate 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.

HiTL-based adaptive fuzzy tracking control of MASs:A distributed fixed-time strategy

Human-in-the-loop(HiTL)control is promising for the cooperative control problem of multi-agent systems(MASs)under the complicated environment.By considering the effect of human intelligence and decision making,the system robustness and security are notably enhanced.Hence,a distributed fixed-time tracking control problem is investigated in this paper for heterogeneous MASs based on the HiTL idea.First,a lemma of practically fixed-time stable is given where an explicit relationship of settling time and convergence domain is clearly shown.Then,under the framework of the adaptive backstepping approach,a series of modified intermediate control signals is designed to avoid the singularity problem by taking advantage of power transformation,fuzzy logic systems,and inequality schemes.Finally,the numerical example and comparison results are utilized to testify the effectiveness of the proposed method.

Systematic error real-time registration based onmodified input estimation

In order to estimate the systematic error in the processof maneuvering target adaptive tracking, a new method is proposed.The proposed method is a linear tracking scheme basedon a modified input estimation approach. A special augmentationin the state space model is considered, in which both the systematicerror and the unknown input vector are attached to thestate vector. Then, an augmented state model and a measurementmodel are established in the case of systematic error, andthe corresponding filter formulas are also given. In the proposedscheme, the original state, the acceleration and the systematicerror vector can be estimated simultaneously. This method can notonly solve the maneuvering target adaptive tracking problem in thecase of systematic error, but also give the system error value inreal time. Simulation results show that the proposed tracking algorithmoperates in both the non-maneuvering and the maneuveringmodes, and the original state, the acceleration and the systematicerror vector can be estimated simultaneously.

ADAPTIVE PRACTICAL TRACKING CONTROL BY OUTPUT FEEDBACK FOR A CLASS OF NONLINEAR SYSTEMS

This paper investigates the problem of adaptive practical tracking control by output feedback for a class of nonlinear systems in which the nonlinearities are restricted to be upper bounded by a constant plus a polynomial function of the output multiplied by unmeasured states. To solve the problem, an observer with a dynamic high-gain is first introduced, and then an adaptive output feedback tracking controller is successfully designed using the universal control methodology. It is shown that all the states of the closed-loop system are bounded, and the tracking error can be made arbitrarily small by an appropriate choice of the design parameters after some large enough time. A simulation example is also provided to illustrate the correctness of the theoretical results.

Adaptive asymptotic fault-tolerant tracking of uncertain nonlinear systems with unknown control directions

In the article,the issues of asymptotic adaptive tracking control for the uncertain nonlinear systems in the presence of actuator faults and unknown control directions are investigated.By using the properties of the Nussbaum function and backstepping technique,the problems resulting from the unknown signs of the nonlinear control functions are circumvented successfully.Moreover,a new adaptive asymptotic tracking control method is presented with the fault-tolerant control framework,which is capable of realising zero-tracking performance.The stability of the controlled system is ensured through fractional Lyapunov stability analysis.Finally,the validity of the raised scheme is verified by a simulation example.

Modeling and adaptive motion/force tracking for vertical wheel on rotating table

This paper is devoted to the problem of modeling and adaptive motion/force tracking for a class of nonholonomic dynamic systems with affine constraints(NDSAC): a vertical wheel on a rotating table. Prior to the development of tracking controller,the dynamic model of the wheel in question is derived in a meticulous manner. A continuously differentiable friction model is also considered in the modeling. By exploiting the inherent cascade interconnected structure of the wheel dynamics, an adaptive motion/force tracking controller is presented guaranteeing that the trajectory tracking errors asymptotically converge to zero while the contact force tracking errors can be made small enough by tuning design parameters. Simulation results are provided to validate the effectiveness of the proposed tracking methodology.

Event-based adaptive asymptotic tracking control of nonlinear time-varying systems with prescribed performance

This paper is concerned with the adaptive tracking control problem of nonlinear time-varyingsystems. Based on the backstepping technology, an event-based prescribed performance controlscheme is developed. And the time-varying uncertainties of the system are handled byutilising bound estimation method. The proposed controller not only ensures the prescribedtracking performance, but also reduces the communication burden. By using Lyapunov stabilityanalysis, it is proven that all of the closed-loop signals are bounded, and the tracking errorcan converge to zero. Simultaneously, Zeno behaviour is excluded. Finally, the simulation resultsare utilised to illustrate the effectiveness of the proposed adaptive control scheme.