Robust Tracking Performance Research Articles

Full Attitude Control of a Quadrotor Using Fast Nonsingular Terminal Sliding Mode With Angular Velocity Planning

In this article, a fast nonsingular terminal sliding mode combined with angular velocity planning (FNTSM-AVP) controller is developed to achieve high-speed, accurate, and robust attitude tracking performance for a quadrotor. The proposed controller employs a sliding surface function with a continuous arctangent function, which can effectively eliminate chattering behavior on the quadrotor while retaining the speed and accuracy of attitude tracking. Unlike conventional methods, the proposed controller preferentially recovers the roll and pitch angles at desired attitude by planning the shortest arrival path, which is effective to improve the tracking performance. Finally, the finite time convergence and zero tracking error properties of the closed-loop control system under the proposed FNTSM-AVP are analyzed and proved. The aggressive flight experiments demonstrate the advantage of faster convergence and fewer chattering behavior of the proposed method compared to the existing FNTSM controller.

Intelligent Vision-based Autonomous Ship Landing Of VTOL UAVs

The paper discusses an intelligent vision-based control solution for autonomous tracking and landing of vertical take-off and landing (VTOL) capable unmanned aerial vehicles (UAVs) on ships without utilizing GPS signals. The central idea involves automating the Navy helicopter ship landing procedure where the pilot utilizes the ship as the visual reference for long-range tracking; however, it refers to a standardized visual cue installed on most Navy ships called the "horizon bar" for the final approach and landing phases. This idea is implemented using a uniquely designed nonlinear controller integrated with machine vision. The vision system utilizes machine learning based object detection for long-range ship tracking and classical computer vision for the estimation of aircraft relative position and orientation utilizing the horizon bar during the final approach and landing phases. The nonlinear controller operates based on the information estimated by the vision system and has demonstrated robust tracking performance even in the presence of uncertainties. The developed autonomous ship landing system was implemented on a quad-rotor UAV equipped with an onboard camera, and approach and landing were successfully demonstrated on a moving deck, which imitates realistic ship deck motions. Extensive simulations and flight tests were conducted to demonstrate vertical landing safety, tracking capability, and landing accuracy.

Robust high-precision tracking control for a class of nonlinear piezoelectric micropositioning systems with time-varying uncertainties

Piezoelectric micropositioning systems (PMSs) have been widely utilized in the high-precision manipulation applications, but are also subjected to undesired nonlinearities, like hysteresis, and parameter uncertainties. To solve this problem, this paper proposes a new robust sliding mode control scheme for a class of nonlinear PMSs with time-varying uncertainties. Different from the conventional sliding mode control (SMC), the proposed controller further combines the Fourier series-based function estimation technique, fuzzy logic system and adaptive learning algorithm to realize online estimation and compensation of system time-varying uncertainties without their boundary information. The adaptive laws of Fourier coefficients and fuzzy adjustable parameters are obtained via the Lyapunov stability theory. Compared with the existing SMC methods, the proposed control effectively eliminates the control chattering problem, and guarantees the convergence of the tracking error in finite time in the presence of time-varying uncertainties. Theoretical analysis and numerical simulation results show that the proposed control strategy can meet the high-speed, high-precision robust tracking performance requirements of PMSs for micro/nano-manipulation applications.

Global Integral Sliding-Mode Control With Improved Nonlinear Extended State Observer for Rotary Tracking of a Hydraulic Roofbolter

To effectively control the drilling of a hydraulic roofbolter is of importance because inappropriate rotary speed may lead to the collapse of roof in a roadway. Two difficulties facing for controlling a rotation system are its inherent characteristics, including dead zone and disturbance, and the specific performance requirement, such as no overshoot and chattering. To address the issues, a novel integral sliding-mode control (NISMC) with improved nonlinear extended state observer and uncertain gain adaptive law is proposed. A rotation system is first modeled after compensating dead zone. NISMC composed of an active disturbance rejection control (ADRC) unit, a sliding-mode control (SMC) unit, and an adaptive unit is then developed. In the ADRC unit, an improved nonlinear extended state observer is presented to efficiently estimate disturbances and suppress noise. In order to achieve a robust tracking performance, a novel sliding-mode control law is designed in the SMC unit, and the adaptive unit is put forward to deal with uncertain gain. Experimental results on a practical rotation system show that the proposed method tracks the expected rotary speed with better dynamic and steady-state performance, especially without overshoot.

Learning-based robust optimal tracking controller design for unmanned underwater vehicles with full-state and input constraints

In this article, the optimal tracking control problem for unmanned underwater vehicles (UUVs) with full-state and input constraints under the presence of external disturbances and internal dynamic uncertainties is addressed. To achieve preassigned state constraints on UUVs, the traditional UUVs model is transformed into an unconstrained one by using two different nonlinear mappings (NMs). Then the robust tracking control problem of traditional UUVs model under position/Euler angles and velocity constraints is transformed to an optimal control problem of the transformed system without any constraints. A learning-based optimal control method is designed to solve the optimal control problem of the transformed system by employing the optimized backstepping (OB) paradigm and reinforcement-learning (RL) technique, achieving uniformly ultimately boundedness (UUB) subject to optimal cost. To deal with lumped disturbances for the velocity control loop, a neural-network (NN) identifier is employed and incorporated into actor–critic architecture, attaining robust tracking performance. Due to the adopted nonquadratic cost function with respect to the control input, the optimal control solution is established in the form of a hyperbolic tangent function to handle the input constraints. Compared with traditional PID method and MPC approach, the proposed controller can improve tracking performance of UUV by 32.04% and 79.64%, respectively.

Simplified Nonlinear Current-Mode Control of DC-DC Cuk Converter for Low-Cost Industrial Applications.

This paper presents a robust nonlinear current-mode control approach for a pulse-width modulated DC-DC Cuk converter in a simple analog form. The control scheme is developed based on the reduced-state sliding-mode current control technique, in which a simplified equivalent control equation is derived using an averaged power converter model in continuous conduction mode. The proposed controller does not require an output capacitor current sensor and double proportional-integral compensators as in conventional sliding-mode current controllers; thus, the cost and complexity of the practical implementation is minimized without degrading the control performance. The simplified nonlinear controller rejects large disturbances, provides fast transient response, and maintains a constant switching frequency. The nonlinear control scheme is developed using an analog circuit with minimal added components, which is suitable for low-cost industrial applications. The control law derivation, control circuit design, controller gains selection, and stability analysis are provided. The proposed control methodology is verified via simulating the closed-loop nonlinear power converter model in MATLAB/SIMULINK under abrupt changes in load current and input voltage. The simulation results show that the proposed control scheme provides robust tracking performance, a low percentage overshoot, fast transient response, and a wide operating range. The maximum percentage overshoot and settling time of the closed-loop power converter response during line disturbance are 5.6% and 20 ms, respectively, whereas the percentage overshoot and settling time during load disturbance are 2.8% and 15 ms, respectively.

Swin-JDE: Joint Detection and Embedding Multi-Object Tracking in Crowded Scenes Based on Swin-Transformer

Multi-object tracking (MOT) is a highly valued and challenging research topic in computer vision. To achieve more robust tracking performance, recently published MOT methods tend to use anchor-free object detectors, which have the advantage of dealing with the identity ambiguity problem encountered by anchor-based methods in learning appearance features. However, in practical applications, it is found that the detection accuracy of the anchor-free object detector based on classical convolutional neural networks in crowded scenes will be significantly reduced. In order to have better detection and tracking performance in crowded scenes, this paper proposes an anchor-free joint detection and embedding (JDE) MOT method based on Transformer architecture, called Swin-JDE. The proposed method includes a novel Patch-Expanding module, which can improve the spatial information of feature maps by up-sampling processing through neural network learning and Einops Notation-based rearrangement to enhance the detection and tracking performance of the MOT model. In terms of training method, we propose a two-step training method that trains the detection branch separately from the appearance branch to enhance the detection robustness of anchor-free predictors. Furthermore, during the training process, we also propose an examination method to remove occluded targets from the training dataset to improve the accuracy of the appearance embedding layer. In terms of data association, we propose a new post-processing method, which simultaneously considers the three factors of detection confidence, appearance embedding distance, intersection-over-union (IoU) distance to match each tracklet and the detection information to improve the tracking robustness of the MOT model. Experimental results show that the proposed method achieves 70.38% multiple object tracking accuracy (MOTA) and 69.53% identification F1-score (IDF1) results in the MOT20 benchmark dataset, and the identification switch (ID Switch) is reduced to 2026. Compared with FairMOT, the proposed method improves MOTA and IDF1 by 8.58% and 2.23%, respectively.

Fault-Tolerant Tracking Control Optimization of Constrained LPV Systems Based on Embedded Preview Regulation and Reference Governance

This article presents some new improvements to the relevant constrained predictive fault-tolerant tracking control (FTTC) methods through embedding optimal preview regulation and reference governance. The main novelty of such a strategy is that some valuable information of finite future references can be adequately scheduled to optimize the robust tracking performance and significantly enlarge the fault-tolerant admissible region. In order to better describe the wide applicability of the proposed strategy, the robust FTTC problem for a class of LPV systems with state/input constraints is considered. Overall, the involved key designs consist of three parts. First, an unconstrained FTTC component is constructed by combining tracking error feedback, reference input regulation, and fault signal compensation. It is used to guarantee the robust tracking stability of closed-loop systems when the constraints are not activated. Second, a tube-based predictive FTTC policy with an embedded optimal preview regulator is designed to achieve the robust constraint satisfaction and transient response improvement. Third, an embedded reference governor is additionally integrated to significantly enlarge the size of the fault-tolerant admissible region. This design further reinforces the feasibility of constrained FTTC optimization. The effectiveness of these results is finally validated by a case study of a single transistor dc/dc Forward converter.

Online Learning Based Mobile Robot Controller Adaptation for Slip Reduction

Slip is a very common phenomena present in wheeled mobile robotic systems. It has undesirable consequences such as wasting energy and impeding system stability. To tackle the challenge of mobile robot trajectory tracking under slippery conditions, we propose a hierarchical framework that learns and adapts gains of the tracking controllers simultaneously online. Concretely, a reinforcement learning (RL) module is used to auto-tune parameters in a lateral predictive controller and a longitudinal speed PID controller. Experiments show the necessity of simultaneous gain tuning, and have demonstrated that our online framework outperforms the best baseline controller using fixed gains. By utilizing online gain adaptation, our framework achieves robust tracking performance by rejecting slip and reducing tracking errors when the mobile robot travels through various terrains.

A Multi-Subobject Approach to Dynamic Formation Target Tracking Using Random Matrices

Bird flocks are typical group targets with various linear formations and high dynamics due to swarm intelligence. This leads to several problems in traditional multi-subobject group target tracking such as shape model mismatch and false correlations. This paper proposes a multi-subobject approach to dynamic formation target tracking. The algebraic graph theory is introduced to analyze the structure of formation targets, then measurements are clustered and combined into subobjects. Based on the existing random matrix approach, two additional filtering branches, including collective filtering and internal structure filtering, are introduced to achieve the robust tracking performance of formation targets. The KL divergence between the prediction and updated densities of the collective filtering is used to determine the change of formation shape. The correct association between the measurements and the subobjects is realized by the guidance of the internal structure filter. Finally, the effectiveness of the proposed method is verified by the simulation and experimental results.

Event-triggered global sliding mode controller design for anti-sway control of offshore container cranes

This paper proposes an event-triggered global sliding mode based anti-sway control for offshore container cranes. Its main objective is to guarantee the robust tracking performance of containers transferred by cranes installed on cargo ships, while saving the transmission resources. To this end, a suitable sliding manifold is formulated with the desired motion properties. A global sliding mode control is then synthesized to maintain stability of the system and ensure robustness against disturbances such as sea waves and wind induced movements. Finally, the event-triggered strategy is considered in the implementation phase to generate the sampling instant at which the control signal is updated, thereby minimizing communication resources and execution time. Using the Lyapunov's stability theory, it is proven that the controlled system reaches the manifold and equilibrium point from any initial state. We also prove that the proposed approach is free from the Zeno solution, i.e., a phenomenon when the infinite triggering instants exist in a limited time range. Eliminating the reaching phase of traditional sliding mode control, overcoming the effect of the disturbances during the entire dynamics, optimizing resources at the implementation phase, and removing the Zeno phenomenon are among the main advantages of the proposed control approach.

Finite-Time Attitude Tracking Control for Hypersonic Flight Vehicles with Actuator Saturation

This paper investigates finite-time attitude tracking control strategies for hypersonic flight vehicles (HFVs) with parameter uncertainties, external disturbances, and actuator saturations by applying sliding model control, adaptive mechanism, and nonlinear disturbance observer techniques. A nonlinear dynamic model of HFV attitude system in reentry flight phase is established. Then, a basic attitude control method of the HFV system is designed based on a terminal sliding mode control (TSMC) scheme to accommodate the system-lumped disturbance torques and guarantee the finite-time stability. To relax the prior knowledge of bounded lumped disturbance of the TSMC-based HFV attitude system, an adaptive TSMC (ATSMC) scheme is proposed. In order to relax the limit of compound uncertainties and attenuate chattering phenomenon of the TSMC-based HFV attitude system, a nonlinear disturbance observer-based TSMC (DO-TSMC) scheme is presented, which enhances the disturbance attenuation and robust tracking performance. Finally, simulation results of a generic X-33 nonlinear model exhibit the effectiveness of the proposed TSMC, ATSMC, and DO-TSMC schemes.

Composite disturbance rejection and tracking control design for fuzzy model based systems with cyber attacks

This paper addresses the output tracking control problem and lumped disturbance rejections for nonlinear cyber-physical systems described by fuzzy model systems with parametric uncertainties, external disturbances and cyber attacks. Especially, to assure both the robust tracking and lumped disturbance estimation performances, an uncertainty and disturbance estimator (UDE)-based modified repetitive control (MRC) protocol is devised for the addressed control system. In detail, the impact of unpredictable factors, such as disturbances and uncertainties in the system model is robustly tackled by the UDE method with an appropriate filter. To be specific, the MRC protocol is amalgamated into the UDE technique for estimating the changes that occur in the afore-said unpredictable factors to ensure the asymptotic tracking performance of the system under study. Notably, to improve the tracking precision, the parallel distributed compensation strategy and the estimated lumped disturbance signal are incorporated into the control design. Meanwhile, the cyber attacks are depicted in this study as a stochastic variable that adheres to the Bernoulli distribution. Further, the required controller gain matrices are determined to affirm the robust asymptotic tracking performances with the aid of Lyapunov stability theory. Eventually, the method proposed in this paper is validated through the simulation results with two numerical examples, which include the continuous stirred tank reactor (CSTR) plant model. Further, the result reveals that the proposed control law provides the minimum tracking error when compared with an existing work.

Simplified Double-Integral Sliding-Mode Control of PWM DC-AC Converter with Constant Switching Frequency

In this paper, a simplified double-integral sliding-mode control method for pulse-width-modulated dc-ac buck conversion is introduced. The control equation is derived based on the equivalent control method, in which the control-oriented model is developed using the averaged dynamics of the power converter in continuous conduction mode. In contrast with the conventional sliding-mode control schemes, the complexity of adding a capacitor current sensor, variable ramp voltage, and other relevant components is avoided. Furthermore, the control equation is translated into a simple electronic circuit with minimal added components, which reduces the practical implementation cost. The proposed control method rejects large disturbances, tracks the reference signal, and maintains a constant switching frequency. Systematic design procedure, control parameters selection, and stability conditions are presented. The design methodology is verified via simulating the proposed control circuit using Simscape Electrical in MATLAB. The control method is also compared with the conventional double-integral sliding-mode control scheme under load disturbances. The results show that the simplified control approach provides a fast transient response and robust tracking performance.

Moving Objects Tracking Based on Geometric Model-Free Approach With Particle Filter Using Automotive LiDAR

In this paper, we propose a Geometric Model-Free Approach with a Particle Filter (GMFA-PF) through the use of automotive LiDAR for real-time tracking of moving objects within an urban driving environment. GMFA-PF proved to be lightweight, capable of finishing the process within the sensing period of the LiDAR on a single CPU. The proposed GMFA-PF tracks and estimates moving objects without any assumptions on the geometry of the target. This approach enables efficient tracking of multiple object classes, with robustness to a sparse point cloud. Point cloud on moving objects is classified via the predicted Static Obstacle Map (STOM). A likelihood field is generated through the classified point cloud and is used in particle filtering to estimate the moving object’s pose, shape, and speed. Quantitative and qualitative comparisons - with Geometric Model-Based Tracking (MBT), Deep Neural Network (DNN), and GMFA - are performed for GMFA-PF using urban driving and scenario driving data gathered on an autonomous vehicle fitted with close-to-market sensors. The proposed approach shows robust tracking and accurate estimation performance in both sparse and dense point clouds; GMFA-PF achieves improved tracking performance in dense traffic and reduces yaw estimation delay compared to the others. Autonomous vehicles with GMFA-PF demonstrated auto-nomous driving on urban roads.

Integrated Tracking and ISAR Imaging Using an Integrated Kalman Filter With Wideband Radar

Traditionally, inverse synthetic aperture radar (ISAR) imaging and target tracking are two separate and independent processes. ISAR imaging is usually achieved after data collecting and tracking for a long time, resulting in the problems of low imaging efficiency, information loss, and probing resource allocation difficulty. In this article, we proposed an integration method of tracking and imaging with wideband radar by constructing the relation among signal, data, and image domains, which can realize accurate tracking and high data-rate imaging at the same time. In this integrated imaging and tracking method, a complex-valued reference high-resolution range profile (HRRP) is generated and predicted by exploiting the relation among range-Doppler image, echo in time domain, and target’s motion state. Then, a Bayes estimation model is built to extract range phase error between the predicted HRRP and the real-measured HRRP, and the Kalman filter can update the motion state. In addition, in order to increase the efficiency, the decentralized Kalman filter is also introduced to realize the subaperture parallel processing and data fusion in this article. Finally, the sequential motion compensation is carried out to realize accurate motion compensation and sequential imaging. The simulated and real-measured data confirm the proposed algorithm’s robust imaging and tracking performance. Experimental results show that the proposed method has excellent efficiency and can simultaneously realize sequential imaging and accurate tracking.

Deep Learning-Based Long-Horizon MPC: Robust, High Performing, and Computationally Efficient Control for PMSM Drives

This article presents a computationally efficient and high performing approximate long-horizon model predictive control (MPC) for permanent magnet synchronous motors (PMSMs). Two continuous control set MPC (CCS-MPC) formulations are considered: the classical current tracking delta MPC (Del-MPC) and the torque tracking economic MPC (EMPC). To achieve offset-free torque tracking under model uncertainties and in all regions of operation, a disturbance observer and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -current reference generator are used. To enable real-time implementation of the long-horizon CCS-MPC, the development of a real-time capable solver is not required, since MPC approximation based on deep neural networks (DNNs) is considered and utilized for controller’s evaluation at run time. The approximation is done by training the DNN to learn the MPC functionality based on offline-generated training data and in an open-loop manner. The robust and offset-free tracking performance of the proposed DNN-based approximate long-horizon Del-MPC and EMPC has been validated through simulation and real-time implementation at test bench and compared to the state-of-the-art field oriented control (FOC) using internal model controller with field-weakening (FW) part and to the exact short-horizon MPC based on the fast gradient method (FGM-MPC). Results show that the long-horizon MPC can achieve significantly faster torque transient responses in comparison with the short-horizon FGM-MPC and the conventional FOC, especially in FW region.

Robust adaptive tracking control for a class of switched time‐delay systems with application of vehicle roll dynamic control

AbstractThe problems of stability and robust tracking control for a class of switched nonlinear systems with uncertain input and state delays are investigated in this study. In the presence of unknown functions and external disturbances, an adaptive fuzzy technique based on nonlinear disturbance observer is used to counteract the effects of external disturbances and approximate unknown functions. For this purpose, it is first assumed that a common Lyapunov function exists for the switched system, and then a new common Lyapunov–Krasovskii functional is built using the terms of the hypothetical common Lyapunov function. By employing this new function, the delay‐dependent input‐to‐state stability (ISS) under an arbitrary switching signal is provided. The robust tracking control problem for this system is investigated next. Finally, to assure ISS and robust tracking performance, an adaptive control law is developed that ensures conditions of the aforementioned hypothetical common Lyapunov function. In addition, to demonstrate the efficiency of the proposed strategy, the aforesaid method is applied to a vehicle roll dynamic as well as a mass‐spring system.

Integral sliding mode control of networked robotic manipulator: a dynamic event-triggered design

This paper addresses the trajectory tracking control of an uncertain robotic manipulator in a networked environment. To avoid the frequent usage of communication and computational resources and to maintain a robust tracking performance of the manipulator, an event-triggered integral sliding mode controller is designed. The controller comprises of a proportional-derivative feedback control loop for the control of the unperturbed /nominal system. The integral sliding mode controller is introduced to guarantee robustness by enforcing the sliding motion from the initial time instant eliminating the reaching phase. Further, to reduce the network usage, a dynamic triggering mechanism is incorporated to reduce the number of control updates. The controller gains are redesigned in an event-based framework to ensure the stability of the system. The closed-loop system with the proposed event-based control law is shown to exhibit Zeno-free behaviour, i.e. the triggering mechanism executes finite sampling instances in finite time. Simulation results are illustrated to prove the theoretical contributions along with the comparative analysis.

Digital Sliding Mode Based Model-Free PWM Current Control of Switched Reluctance Machines

This article presents a digital sliding mode based robust model-free pulsewidth mode current control method for switched reluctance machine (SRM) drives. The model-free nature of the proposed controller allows complete elimination of the phase inductance or flux-linkage identification process. From the controller point of view, this exclusion yields excellent computational efficiency and low memory usage. In the proposed control method, the model information required to achieve a consistent closed-loop dynamic response is treated as an extended state and it is estimated online via unit time-step delayed approximation. The effect of this delay becomes significant as the operating speed increases. The estimation accuracy of the extended state is also affected by the noise in the current measurement. The effect of these factors on the control performance is compensated by using an auxiliary control action derived from a Lyapunov energy function analysis in the discrete-time domain. The resultant controller achieves accurate tracking of the reference phase current profile at fixed switching frequency modulation. It demonstrates a robust tracking performance throughout the controllable operating range of the SRM drive amid low signal-to-noise ratio.