Adaptive Tracking Research Articles

Coordinated adaptive consensus tracking of multiple impacting oscillator robot systems via distributed interactions

Coordinated adaptive consensus tracking of multiple impacting oscillator robot systems via distributed interactions

Adaptive finite-time consensus tracking for nonlinear second-order multi-agent systems based on integral sliding mode

This paper delves into the investigation of finite-time consensus for second-order nonlinear multi-agent systems (MASs) with external disturbances under directed topology. The MASs considered in this study consist of n followers and a leader, all of which are subject to bounded disturbances. First, a continuous nonsingular integral terminal sliding mode is designed, which can effectively eliminate the singularity and chattering. Then, a finite-time disturbance observer is introduced to estimate and compensate for disturbances. Subsequently, an integral sliding mode adaptive controller is designed to enhance system’s robustness, improve response speed, and increase tracking accuracy. Furthermore, Lyapunov theory is utilized to demonstrate that the system achieves finite-time consensus under a directed connected topology. Finally, we apply a simulation to verify the efficacy of the proposed consensus control protocol.

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

Adaptive RISE-based tracking control of uncertain nonlinear systems: A FAS approach

A fully actuated system (FAS) approach integrated with adaptive robust integral of the sign of the error (ARISE) feedback control strategy is proposed for multi-input multi-output nonlinear systems in the presence of both external disturbances and parametric uncertainties. Owing to an inability to eliminate unmeasured disturbances and model inaccuracies simultaneously, the existing results based on the FAS approaches are typically limited to the uniformly ultimate boundedness of the tracking errors. To achieve the asymptotic tracking performance confronted with parametric uncertainties and time-varying disturbances, an ARISE feedback controller with desired compensation is synthesized to suppress the adverse effects arising from nonlinearity and uncertainty of the system. The improvements compared to the traditional RISE feedback control are attributed to two aspects: (i) the feedback gains in the RISE term are adjustable-online without having to know the prior bounds of disturbances and their time derivatives; (ii) a desired compensation-based adaptive feedforward term, primarily employing the desired trajectories in place of the measured states, could weaken the underlying interaction between the adaptive compensation and robustness part. A rigorous stability analysis is provided to demonstrate that the system state can asymptotically track a bounded desired trajectory in spite of bounded disturbances and parametric uncertainties. Comparative simulations on an under-actuated planar manipulator, possessing an equivalent multi-order FAS model, have been conducted to verify the effectiveness and merits of the developed controller. Experimental validation on a two-wheeled self-balancing robot is also provided to show the feasibility of the proposed approach.

Adaptive Robust Tracking Control With Active Learning for Linear Systems With Ellipsoidal Bounded Uncertainties

Adaptive Robust Tracking Control With Active Learning for Linear Systems With Ellipsoidal Bounded Uncertainties

Towards Sustainable Transportation: Adaptive Trajectory Tracking Control Strategies of a Four-Wheel-Steering Autonomous Vehicle for Improved Stability and Efficacy

The objective of continuous increase in the evolution of autonomous and intelligent vehicles is to attain a trustworthy, economical, and safe transportation system. Four-wheel steering (4WS) vehicles are favored over traditional front-wheel steering (FWS) vehicles because they have excellent dynamic characteristics. This paper exhibits the trajectory tracking task of a two degree of freedom (2DOF) underactuated 4WS Autonomous Vehicle (AV). Because the system is underactuated, MIMO, and has a nontriangular form, the traditional adaptive backstepping control scheme cannot be utilized to control it. For the purpose of rectifying this issue, two-state feedback-based methods grounded on the hierarchical steps of the block backstepping controller are proposed and compared in this paper. In the first strategy, a modified block backstepping is applied for the entire dynamic system. Global stability of the overall system is manifested by Lyapunov theory and Barbalat’s Lemma. In the second strategy, a block backstepping controller has been applied after a reduction of the high-order model into various first-order subsystems, consisting of Lyapunov-based design and stability warranty. A trajectory tracking controller that can follow a double lane change path with high accuracy is designed, and then simulation experiments of the CarSim/Simulink connection are carried out against various vehicle longitudinal speeds and road surface roughness to demonstrate the effectiveness of the presented controllers. Furthermore, a PID driver model is introduced for comparison with the two proposed controllers. Simulation outcomes show that the proposed controllers can attain good response implementation and enhance the 4WS AV performance and stability. Indeed, enhancement of the stability and efficacy of 4WS autonomous vehicles would afford a sustainable transportation system by lessening fuel consumption and gas emissions.

Approximation-based adaptive two-bit-triggered bipartite tracking control for nonlinear networked MASs subject to periodic disturbances

Purpose This paper aims to investigate the problem of adaptive bipartite tracking control in nonlinear networked multi-agent systems (MASs) under the influence of periodic disturbances. It considers both cooperative and competitive relationships among agents within the MASs. Design/methodology/approach In response to the inherent limitations of practical systems regarding transmission resources, this paper introduces a novel approach. It addresses both control signal transmission and triggering conditions, presenting a two-bit-triggered control method aimed at conserving system transmission resources. Additionally, a command filter is incorporated to address the problem of complexity explosion. Furthermore, to model the uncertain nonlinear dynamics affected by time-varying periodic disturbances, this paper combines Fourier series expansion and radial basis function neural networks. Finally, the effectiveness of the proposed methodology is demonstrated through simulation results. Findings Based on neural networks and command filter control method, an adaptive two-bit-triggered bipartite control strategy for nonlinear networked MASs is proposed. Originality/value The proposed control strategy effectively addresses the challenges of limited transmission resources, nonlinear dynamics and periodic disturbances in networked MASs. It guarantees the boundedness of all signals within the closed-loop system while also ensuring effective bipartite tracking performance.

Adaptive exponential tracking control of port-Hamiltonian system via contraction and timed IDA-PBC method

Adaptive exponential tracking control of port-Hamiltonian system via contraction and timed IDA-PBC method

Event-Triggered Backstepping Control of Fractional-Order Chaotic Systems with Dead Zone Via Disturbance Observer

This paper comes up with an adaptive event-triggered tracking control scheme for a kind of fractional-order strict feedback nonlinear systems with actuator dead-zone inputs and uncertain external disturbances. The presented strategy can overcome the defect in standard backstepping control scheme which leads to the “explosion of complexity” problem of virtual signals, and strengthen the transmission efficiency by an event-triggered scheme. An error compensation mechanism is presented for promoting the tracking accuracy and reducing the effect filter error. The designed controller guarantees that the tracking error converges into a small neighborhood near the origin, and all closed-loop signals are bounded. Moreover, Zeno behavior will be avoided. The numerical simulation is given to verify the accuracy and effectiveness of the designed technique.

Adaptive control of magnetic levitation system based on fuzzy inversion

A novel adaptive tracking controller for magnetic levitation systems (MLS) is developed. The controller is based on a special adaptive control scheme incorporating fuzzy model of electromagnetic acceleration enabling fast and accurate fuzzy inversion. The controller ensures accurate tracking of any smooth desired position signal, despite unknown MLS parameters. The closed-loop system stability, in the sense of uniform ultimate boundedness (UUB) of error trajectories, is proved using Lyapunov approach. The closed-loop system performance is investigated during numerical experiments. Finally the proposed controller is verified by successful implementation on a DSP board controlling a typical magnetic levitation ball system. Performed tests and experiments demonstrate that the proposed control technique is robust against discretization and unknown MLS model parameters, provides high tracking accuracy, is easily implementable, and simple to tune.

Adaptive asymptotic tracking control of constrained nonlinear MIMO systems subject to unknown hysteresis input: A novel network-based strategy

This paper presents a referential adaptive asymptotic tracking control scheme for nonlinear multi-input and multi-output (MIMO) systems with time-varying full-state constraints and unknown hysteresis input. In order to achieve good asymptotic tracking effect, a zero-limit positive continuous function is introduced into the adaptive backstepping design process while thoroughly considering the impact of disturbance-like terms on the tracking effect. Additionally, a new variable is also imported to replace the reciprocal of unknown coefficient of the Bouc–Wen hysteresis model. Then, a new adaptive law about the new variable is added by combining the positive function, which can not only lessen the impact of the unknown hysteresis input on the tracking effect, but also avoid the “singularity” problem. Aiming at the time-varying full-state constraints, a appropriate time-varying log-type barrier Lyapunov function (TLBLF) is constructed to ensure that all the states of the system are restricted within the constraint scope. Finally, a significant adaptive asymptotic tracking scheme is designed based on multi-dimensional Taylor network (MTN) approximation technology. Apart from achieving high-precision asymptotic tracking performance, the proposed scheme ensures the boundedness of all signals of the closed-loop system without violating the full-state constraints. And, three simulations are given to verify the effectiveness of the scheme.

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.

Event-triggered V2V communication-based cooperative adaptive tracking control for nonlinear vehicle platoon systems with unknown lag time

Event-triggered V2V communication-based cooperative adaptive tracking control for nonlinear vehicle platoon systems with unknown lag time

Application of an Alternating Current Motor System Based on Fuzzy Adaptive Tracking Control for Nonlinear Systems

Application of an Alternating Current Motor System Based on Fuzzy Adaptive Tracking Control for Nonlinear Systems

Event-triggered finite-time adaptive neural network control for quadrotor UAV with input saturation and tracking error constraints

In this article, an event-triggered finite-time adaptive neural network control tracking strategy is proposed for quadrotor unmanned aerial vehicle (UAV) with input saturation and error constraints. Firstly, the radial basis function neural networks (RBFNNs) are adopted to identify the unknown uncertainty of quadrotor UAV model from the installation errors, gyroscope errors and so on. An auxiliary equation is constructed to deal with input physical saturation from the actuator motors. Additionally, by combining the performance function and error transformation, the issue of error constraint is solved. Based on the Lyapunov stability theory and event-triggered mechanisms, a finite-time adaptive neural network scheme is developed to ensure that the closed-loop quadrotor UAV system is semi-globally practically finite-time stable, and save the computation, resources, and transmission load. Finally, the simulation results illustrate the good tracking performance of quadrotor UAV by using the proposed control strategy.

Adaptive content-aware spatial regularized correlation filter tracking algorithm

In order to solve the annoying boundary effects in correlation filter (CF) trackers induced by cyclic shift when sampling training patches, and improve the tracking performance, an adaptive content aware spatially regularized correlation filter (ACSRCF) is proposed. Firstly, real negative samples are generated from the background area around the target object, so as to alleviate the filter degradation by the fake negative samples induced from the circularly shifted object patches. Secondly, the locality sensitive histogram (LSH) based foreground feature is extracted and incorporated with the spatial regularization weight which is updated adaptively according to the varied object-oriented appearances. Thereafter, the CF model is optimized using the alternative direction method of multipliers (ADMM) in which the model is decomposed into two sub-problems and the LSH-based features are used in iteration for obtaining the optimal solutions. Finally, the proposed method is evaluated on 5 public benchmarks. The experimental results show that the accuracy and success rate scores of our method on OTB 50 dataset are 90.3%and 66.1%, respectively, exceeding the other CF trackers .The data on the OTB100 dataset is 92.2%and 69.2%, and the accuracy first ranks among all the trackers, while the success rate is ahead of other CF trackers.

Adaptive Saturated Obstacle Avoidance Trajectory Tracking Control for Euler–Lagrange Systems With Velocity Constraints

ABSTRACTThis article pays attention to the obstacle avoidance trajectory tracking control problem for uncertain Euler–Lagrange (EL) systems subject to velocity constraints and input saturation. The existing obstacle avoidance results do not consider velocity constraints under input saturation, which means that an EL system may not be able to obtain sufficient control inputs to avoid a collision with an obstacle if it has a high speed when approaching the obstacle. Therefore, the velocity constraints in the obstacle avoidance tracking control are considered in this paper. A novel velocity constraint function that depends on the distance between the system and the obstacle is proposed. Integral‐multiplicative Lyapunov‐barrier functions (LBFs) are constructed and incorporated into the backstepping procedure to design an adaptive fuzzy obstacle avoidance tracking control scheme. Moreover, an auxiliary dynamic system is designed by constructing a bounded nonlinear vector related to an auxiliary variable to compensate for the effects of saturation. Through the Lyapunov method and boundedness analysis for the barrier function, it is shown that the protocol achieves obstacle avoidance for the EL system without violating the velocity constraints inside the obstacle detection region, while also guaranteeing the ultimate uniform boundedness of all the closed‐loop signals. Numerical simulations are presented to demonstrate the efficacy of the proposed control strategy.

High precision DSRC and LiDAR data integration positioning method for autonomous vehicles based on CNN

In order to improve the safe driving and automatic positioning capability of autonomous vehicles, a high-precision DSRC and LiDAR data integration positioning technology for autonomous vehicles based on CNN is proposed. Import the data of Dedicated Short Range Communications and Light Detection and Ranging for automatic driving vehicle positioning, carry out kinematic analysis of autonomous driving vehicles under multi-sensor fusion, and transform the data of DSRC and LiDAR sensors into tightly coupled coordinate systems; The CNN depth learning method is used to compensate the position and attitude tracking estimation error under the overall time stamp synchronization of the sensor through adaptive information tracking; The first and second order feedforward compensation is made for the positioning parameters of the autonomous driving vehicle using the PID model, and the point cloud feature matching model is fused to complete the estimation of the positioning attitude parameters of the autonomous driving vehicle. In order to eliminate the noise interference under the DSRC communication mechanism, the Kalman filter function is used to automatically optimize the constraint parameters in the point cloud feature detection model, and the positioning error parameters are dynamically filtered and adjusted; Kinematics analysis is carried out for the driving state of the vehicle, and the positioning error in the vehicle movement is controlled through the difference technology to achieve high-precision DSRC and LiDAR data integration positioning. The simulation results show that this method can integrate and locate the high-precision DSRC and LiDAR data of the autonomous vehicle, and the attitude estimation and positioning accuracy of the vehicle is good, while the error of the attitude parameter estimation of the autonomous vehicle is low.

Adaptive dual closed-loop trajectory tracking control for a wheeled mobile robot on rough ground

Adaptive dual closed-loop trajectory tracking control for a wheeled mobile robot on rough ground

Effectiveness of a Digital Health Intervention Leveraging Reinforcement Learning: Results From the Diabetes and Mental Health Adaptive Notification Tracking and Evaluation (DIAMANTE) Randomized Clinical Trial.

Digital and mobile health interventions using personalization via reinforcement learning algorithms have the potential to reach large number of people to support physical activity and help manage diabetes and depression in daily life. The Diabetes and Mental Health Adaptive Notification and Tracking Evaluation (DIAMANTE) study tested whether a digital physical activity intervention using personalized text messaging via reinforcement learning algorithms could increase step counts in a diverse, multilingual sample of people with diabetes and depression symptoms. From January 2020 to June 2022, participants were recruited from 4 San Francisco, California-based public primary care clinics and through web-based platforms to participate in the 24-week randomized controlled trial. Eligibility criteria included English or Spanish language preference and a documented diagnosis of diabetes and elevated depression symptoms. The trial had 3 arms: a Control group receiving a weekly mood monitoring message, a Random messaging group receiving randomly selected feedback and motivational text messages daily, and an Adaptive messaging group receiving text messages selected by a reinforcement learning algorithm daily. Randomization was performed with a 1:1:1 allocation. The primary outcome, changes in daily step counts, was passively collected via a mobile app. The primary analysis assessed changes in daily step count using a linear mixed-effects model. An a priori subanalysis compared the primary step count outcome within recruitment samples. In total, 168 participants were analyzed, including those with 24% (40/168) Spanish language preference and 37.5% (63/168) from clinic-based recruitment. The results of the linear mixed-effects model indicated that participants in the Adaptive arm cumulatively gained an average of 3.6 steps each day (95% CI 2.45-4.78; P<.001) over the 24-week intervention (average of 608 total steps), whereas both the Control and Random arm participants had significantly decreased rates of change. Postintervention estimates suggest that participants in the Adaptive messaging arm showed a significant step count increase of 19% (606/3197; P<.001), in contrast to 1.6% (59/3698) and 3.9% (136/3480) step count increase in the Random and Control arms, respectively. Intervention effectiveness differences were observed between participants recruited from the San Francisco clinics and those recruited via web-based platforms, with the significant step count trend persisting across both samples for participants in the Adaptive group. Our study supports the use of reinforcement learning algorithms for personalizing text messaging interventions to increase physical activity in a diverse sample of people with diabetes and depression. It is the first to test this approach in a large, diverse, and multilingual sample. ClinicalTrials.gov NCT03490253; https://clinicaltrials.gov/study/NCT03490253. RR2-10.1136/bmjopen-2019-034723.