Adaptive Tracking Scheme Research Articles

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.

Modified Quadratic Spacing Policy and Extended State Observer-Based Adaptive Platoon Tracking Scheme for Heterogeneous Vehicles

Modified Quadratic Spacing Policy and Extended State Observer-Based Adaptive Platoon Tracking Scheme for Heterogeneous Vehicles

Distributed dynamic obstacle avoidance design to connectivity-preserving formation control of uncertain underactuated surface vehicles under a directed network

This paper presents a Lyapunov-based recursive design approach for unifying distributed connectivity-preserving formation control and dynamic obstacle avoidance problems of uncertain multiple underactuated surface vehicles (USVs) with heterogeneous range constraints. Compared with related works, the main contribution of this study is to develop a novel control design for preserving network connectivity under dynamic obstacle avoidance in the distributed formation tracking framework. Because of the limited communication ranges, it is difficult to maintain the network interactions among USVs during dynamic obstacle avoidance. To deal with this problem, a collision-avoiding function is defined as a local error surface for the recursive control design, and a distributed complementary signal vector is introduced in the connectivity-preserving formation error surface. The complementary signals are designed to prevent network disconnections when the formation error is increased by the avoidance action and to ensure the stability of the collision-avoiding error dynamics, which eventually lead to preventing network disconnections during dynamic obstacle avoidance. An adaptive formation tracking scheme is designed via the neural networks-based command-filtered backstepping. The stability of the overall closed-loop system with preserved network connectivity and obstacle avoidance is proved based on the Lyapunov stability theorem. Finally, the theoretical approach is demonstrated through simulations.

Explicit Near-Optimal Guidance and Pulse-Modulated Control for Lunar Descent and Touchdown

This research considers the descent path of a space vehicle, from periselenium of its operational orbit to the lunar surface. The trajectory is split in two arcs: (a) approach, up to a specified altitude, and (b) terminal descent and soft touchdown. For phase (a), a new locally flat near-optimal guidance is used, which is based on iterative projection of the spacecraft position and velocity, and availability of closed-form expressions for the related costate variables. Attitude control is aimed at pursuing the desired spacecraft orientation, and uses an adaptive tracking scheme that compensates for the inertia uncertainties. Arc (b) is aimed at gaining the correct vertical alignment and low velocity at touchdown. For phase (b) a predictive bang-off-bang guidance algorithm is proposed that is capable of guaranteeing the desired final conditions, while providing the proper allocation of side jet ignitions. Actuation of side jets is implemented using pulse width modulation, in both phases. Monte Carlo simulations prove that the guidance and control architecture at hand drives the spacecraft toward safe touchdown, in the presence of non-nominal flight conditions.

Video analytics-based real-time intelligent crossing detection system (RICDS): Killer app for edge computing

In this paper, we design and implement a video analytics based real-time intelligent crossing detection system (RICDS) as a novel safety service for the smart city. This system spans across the edge device and visualization unit, with reduced latency. To enhance object tracking in the proposed RICDS on the edge devices, which has limited computational resources, we first design an adaptive queue management-based object tracking (QMOT) scheme. This scheme adaptively controls the maximum queue size to meet the target delay requirement for the RICDS while the image frames come into the proposed queue to wait for object detection and tracking. In this sense, the maximum queue size is calculated by the target delay requirement and weighted-average of processing time, so that some redundant frames for object detection are automatically dropped due to the overflow of the maximum queue size. Moreover, we introduce the real-world-real-time (RR) tracking scheme that lightly tracks multiple objects (i.e., pedestrians in this scenario) by predicting the future position of the object and assigning unique IDs to them. Specifically, to predict the future positions of multiple objects in a lightweight manner, the possible movement boundary of every single object is determined by considering both the movement directions and movement speeds of each object to reduce search space for mapping IDs to the objects. Here, the movement directions and movement speeds are also calculated based on the real-world coordinate system after calibrating of image frame’s coordinate system into the real-world one. Finally, we implement the proposed RICDS on various commercial edge devices and a visualization unit (i.e., the LED panel). Moreover, the GUI application at the remote server is provided for (i) monitoring the result of the detection and tracking and (ii) dynamically changing the configuration settings. The measurement-based experiments show that the proposed tracking scheme achieves a latency reduction of about 33%–62% compared to the benchmark schemes on the NVIDIA Xavier while exhibiting similar multi object tracking accuracy performance compared to the benchmark schemes.

Adaptive Tracking for Uncertain MIMO Nonlinear Systems With Time-Varying Parameters and Bounded Disturbance

In this article, tracking control is considered for a class of uncertain multi-input-multi-output (MIMO) nonlinear systems, where the time-varying parameters, the time-varying control coefficient and the time-varying disturbance are assumed to be unknown but to be bounded. Three stable adaptive tracking schemes for a given reference signal are proposed by devising different control algorithms. In the first scheme, bounded-error tracking is achieved in the sense that the tracking error converges exponentially to an adjustable region around the origin, where the σ-modification adaptive laws are used to ensure the boundedness of all closed-loop signals. In the second scheme, asymptotic tracking is obtained in the sense that the tracking error converges to zero asymptotically, where the strictly positive and integral functions are employed in the control law to ensure the signal boundedness and zero-error tracking. In the third scheme, exponential tracking is gotten in the sense that the tracking error exponentially converges to zero with a given convergence speed, where exponential functions are incorporated into control law and adaptive laws to ensure system stability and the faster convergence. Three adaptive tracking schemes are, respectively, applied to nonlinear chaotic Chua's circuit with control inputs. The parametric model is developed for Chua's circuit with uncertain parameters and external disturbances. The effectiveness of the proposed control algorithms is demonstrated by comparative simulation studies.

Adaptive Closed-Loop Identification and Tracking Control of an Aerial Vehicle with Unknown Inertia Parameters

Abstract In this paper, the problem of adaptive closed-loop parameter estimation and tracking control of a six degree of freedom (6-DOF) nonlinear quadrotor unmanned aerial vehicle (UAV) is studied. To manage the complexity of the problem, the system dynamics is decomposed into two subsystems, i.e. translational dynamics and rotational dynamics. A nested control architecture is adopted to develop both adaptive tracking control and parameter estimation. To stabilize the outer loop, a virtual control input is proposed using a proportional-derivative (PD) controller to track the x, y and z positions. The rotational dynamics of UAV contains unknown inertia parameters appearing in the control structure as well as in a nonlinear dynamic term. An adaptive tracking scheme is designed using the certainty equivalence principle to handle both parameter estimation and tracking control in a closed-loop. The idea behind the controller design is to cancel the nonlinear term in the inner loop by estimating the unknown system parameters. The stability of the whole closed-loop system is proved with a rigorous analytical study. Moreover, the performance of the proposed controller is verified with several numerical analyses.

Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo algorithm

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law; which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques (i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem.

An Adaptive Vector Tracking Scheme for High-Orbit Degraded GNSS Signal

Global navigation satellite system (GNSS) receivers meet numerous challenges in a high-orbit environment, including weak and discontinuous signal, and time-varying strength. To resolve these issues and enhance reliability, an innovative adaptive vector tracking loop (VTL) scheme is proposed. Non-linear models of the VTL filter are established to calculate code phase and carrier frequency errors accurately. Based on this, a deep analysis has been developed on the measurement noise. To reduce the impact of the interdependent noises among channels in VTL, an adaptive VTL algorithm assisted by the variational Bayesian (VB) learning network is proposed to estimate the measurement noise and maintain the error convergence in the time-varying noise or signal outage conditions. Further, the implementation steps of the adaptive algorithm have been designed in detail. In particular, the carrier-to-noise power ratio (C/N0) estimation method is further employed to update the a prior probability density in case of change of tracking satellite. The simulation results indicate that the proposed VTL scheme with VB algorithm is a promising method to improve the accuracy and reliability of GNSS receivers significantly under a high-orbit degraded signal environment.

An Adaptive Face Tracker with Application in Yawning Detection.

In this work, we propose an adaptive face tracking scheme that compensates for possible face tracking errors during its operation. The proposed scheme is equipped with a tracking divergence estimate, which allows to detect early and minimize the face tracking errors, so the tracked face is not missed indefinitely. When the estimated face tracking error increases, a resyncing mechanism based on Constrained Local Models (CLM) is activated to reduce the tracking errors by re-estimating the tracked facial features’ locations (e.g., facial landmarks). To improve the Constrained Local Model (CLM) feature search mechanism, a Weighted-CLM (W-CLM) is proposed and used in resyncing. The performance of the proposed face tracking method is evaluated in the challenging context of driver monitoring using yawning detection and talking video datasets. Furthermore, an improvement in a yawning detection scheme is proposed. Experiments suggest that our proposed face tracking scheme can obtain a better performance than comparable state-of-the-art face tracking methods and can be successfully applied in yawning detection.

Quantized feedback adaptive command filtered backstepping control for a class of uncertain nonlinear strict-feedback systems

An adaptive command filtered backstepping control design strategy in the presence of quantized states is presented for uncertain nonlinear systems in the strict-feedback form. A uniform quantizer quantizes all state variables, and the quantized state variables are only available for feedback. The existing quantized feedback recursive control design is only applicable to systems with nonlinearities matched to the control input because of its restriction that the partial derivatives of the virtual control laws with respect to state variables should be constants. Compared with the existing result, the primary contribution of this paper is to develop a quantized feedback recursive design using the command filtered backstepping technique for dealing with unmatched nonlinearities without this restriction. An adaptive tracking scheme using quantized states is constructed to compensate for quantization effects, and a new stability analysis strategy is established by analyzing the boundedness of the quantization errors of signals in the command filtered backstepping design framework. The stability of the closed-loop system is analyzed in the sense of uniform ultimate boundedness.

Practical Tracking Via Adaptive Event-Triggered Feedback for Uncertain Nonlinear Systems

This paper addresses the event-triggered tracking for a class of uncertain nonlinear systems. Essentially different from the related works, the nonlinear systems allow serious (parametric) uncertainties, while no further a priori information, excepting some coarse information, is required on the reference signal to be tracked. This makes event-triggered control more challenging, partially because the sampling error cannot be precisely bounded any more. As main contributions of this paper, new adaptive event-triggered tracking schemes are proposed for the systems in two event-triggering architectures with different roles of the event-triggering mechanism on the information transmitting and control computing/updating. Much importantly, a dynamic gain is incorporated in either scheme not only to counteract the serious uncertainties, but also to overcome the bad influence of the sampling error. Based on the dynamic gain, two adaptive event-triggered controllers are designed with distinct event-triggering mechanisms, respectively. Particularly, to ensure application flexibility, one of the triggering mechanisms is rendered relatively independent of the controller signal. The designed event-triggered controllers are shown to achieve the practical tracking for the systems, that is, the prespecified arbitrary tracking accuracy can be guaranteed (a comparable objective to that via continuous-time control), while avoiding infinitely fast sampling/execution.

Coordinated Attitude Synchronization and Tracking Control of Multiple Spacecraft Over a Communication Network With a Switching Topology

This paper investigates the coordinated attitude synchronization and tracking problems of multiple rigid-body spacecraft subject to inaccurate inertial parameters. Two adaptive distributed control algorithms are developed based on communications among spacecraft connected through a switching communication network, which allow each switching subgraph to be disconnected. In the first place, by introducing an adaptive strategy for the inertial parameters, a distributed attitude synchronization control algorithm is designed. In particular, a gain selection criteria is specified for the asymptotic attitude synchronization under uniform joint connectivity. In the second place, for the coordinated attitude tracking problem, we first develop a distributed observer for each spacecraft to estimate the reference information associated with a leader over a switching network with uniform joint quasi-strong connectivity. Then, we propose an adaptive tracking scheme such that each spacecraft tracks the estimated information. Finally, numerical examples are given to validate the effectiveness of two proposed adaptive distributed control algorithms.

An improved design strategy for approximation-based adaptive event-triggered tracking of a class of uncertain nonlinear systems

This paper presents an improved adaptive design strategy for neural-network-based event-triggered tracking of uncertain strict-feedback nonlinear systems. An adaptive tracking scheme based on state variables transmitted from the sensor-to-controller channel is designed via only single neural network function approximator, regardless of unknown nonlinearities unmatched in the control input. Contrary to the existing multiple-function-approximators-based event-triggered backstepping control results with multiple triggering conditions dependent on all error surfaces, the proposed scheme only requires one triggering condition using a tracking error and thus can overcome the problem of the existing results that all virtual controllers with multiple function approximators should be computed in the sensor part. This leads to achieve the structural simplicity of the proposed event-triggered tracker in the presence of unmatched and unknown nonlinearities. Using the impulsive system approach and the error transformation technique, it is shown that all the signals of the closed-loop system are bounded and the tracking error is bounded within pre-designable time-varying bounds in the Lyapunov sense.

On-Chip Photovoltaic Power Harvesting System With Low-Overhead Adaptive MPPT for IoT Nodes

Extracting maximum power from a photovoltaic (PV) harvester with minimum power transfer loss is one of the primary design goals of an energy processing circuit. This paper presents a fully integrated PV power harvesting system with a low-overhead adaptive maximum power point tracking (MPPT) scheme for Internet-of-Things (IoT) nodes. The proposed scheme tracks the MPPs within 12 $\mu \text{s}$ by utilizing an inherent negative feedback loop, within a tracking error of 0.6%. The tracking range has been improved by ~57% using a current-starved voltage-controlled oscillator (CS-VCO) instead of a polynomial VCO. The overhead area and power consumed by this tracking scheme are approximately 0.013% and 0.1%, respectively. Using commercially available solar cell of area 11.3 cm2, the proposed system can provide 833 $\mu \text{W}$ of power with a light intensity of 600 lx. The proposed energy processing circuit has been designed using 0.18- $\mu \text{m}$ CMOS technology node and the circuit simulations demonstrate that the proposed scheme can track maximum power point (MPP) under rapidly changing atmospheric conditions with a peak tracking efficiency of 99%.

Connectivity-Preserving Approach for Distributed Adaptive Synchronized Tracking of Networked Uncertain Nonholonomic Mobile Robots.

This paper addresses a distributed connectivity-preserving synchronized tracking problem of multiple uncertain nonholonomic mobile robots with limited communication ranges. The information of the time-varying leader robot is assumed to be accessible to only a small fraction of follower robots. The main contribution of this paper is to introduce a new distributed nonlinear error surface for dealing with both the synchronized tracking and the preservation of the initial connectivity patterns among nonholonomic robots. Based on this nonlinear error surface, the recursive design methodology is presented to construct the approximation-based local adaptive tracking scheme at the robot dynamic level. Furthermore, a technical lemma is established to analyze the stability and the connectivity preservation of the total closed-loop control system in the Lyapunov sense. An example is provided to illustrate the effectiveness of the proposed methodology.

Generalized GNSS Signal Carrier Tracking—Part II: Optimization and Implementation

This paper presents the performance analysis and the optimizations of the generalized phase locked loop (PLL) and the generalized frequency locked loop (FLL). Using the minimum mean square error (MMSE) criterion as a performance measure, the optimal solutions for the generalized PLL and FLL designs under various scenarios: strong and weak signal strength, low and high dynamic, and good and poor oscillator quality are investigated. An adaptive PLL based on automatically adjustment of the integration time and loop parameters in an optimized manner is proposed. An adaptive FLL that operates with the optimal loop parameters is also presented. Simulation results demonstrate the effectiveness of the proposed generalized closed-loop tracking architecture and validate the proposed adaptive tracking schemes.

Memoryless disturbance-observer-based adaptive tracking of uncertain pure-feedback nonlinear time-delay systems with unmatched disturbances

This paper presents a delay-independent nonlinear disturbance observer (NDO) design methodology for adaptive tracking of uncertain pure-feedback nonlinear systems in the presence of unknown time delays and unmatched external disturbances. Compared with all existing NDO-based control results for uncertain lower-triangular nonlinear systems where unknown time delays have been not considered, the main contribution of this paper is to develop a delay-independent design strategy to construct an NDO-based adaptive tracking scheme in the presence of unknown time-delayed nonlinearities and non-affine nonlinearities unmatched in the control input. The proposed delay-independent scheme is constructed by employing the appropriate Lyapunov-Krasovskii functionals and the same function approximators for the NDO and the controller. It is shown that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to an adjustable neighborhood of the origin.

Microwave Plasma Generation With Resonance Frequency Tracking and Power Regulation

This paper presents a palm-sized power-providing module for microwave plasma generation with resonance-frequency tracking and output-power-regulation schemes. A 2.4-GHz coaxial transmission line resonator is used as a microwave plasma generator. The power module covers the frequency range from 2.3 to 2.5 GHz with the maximum power capability of 24 W. The power module adaptively sets the output frequency so that the forward power to the resonator is minimally reflected in response to the resonance frequency shift due to plasma formation. At a forward input power of 2 W, the resonance frequency shift by plasma formation is 41 MHz. With the proposed adaptive frequency tracking scheme, the power-transfer efficiency improves from 56 to 88 %. In addition, production of $\cdot $ OH radical, which is one of the key species in biomedical applications, increases by 33 % at 2 W. The power regulation function is verified by measuring the plasma gas temperature, which is an important indicator of safety for biomedical applications. With a feedback loop control, the power module stably generates 1 W and maintains a constant plasma gas temperature of 40 °C for 300 s. The measurements reveal that the proposed schemes can improve the power efficiency and controllability of microwave plasma generation, and thereby increase the feasibility of producing a portable low-power microwave bio-plasma device.