High Tracking Performance Research Articles

Active Disturbance Rejection Control for Distributed Energy Resources in Microgrids

Motivated by the significant efforts developed by researchers and engineers to improve the economic and technical performance of microgrids (MGs), this paper proposes an Active Disturbance Rejection Control (ADRC) for Distributed Energy Resources (DER) in microgrids. This approach is a nonlinear control that is based on a real-time compensation of different estimated disturbances. The DER operates along with the electrical grid to provide the load requirements. This load has a nonlinear and uncertain character, which presents a source of unmodeled dynamics and harmonic perturbations of the MG. The main objective of this paper is to ensure the stability and the continuity of service of the distributed generation resources by controlling the DC-AC converter. The ADRC as a robust control technique is characterized by its ability to compensate for the estimated total disturbances caused by the load variation and the external unmodeled perturbations to guarantee the high tracking performance of sinusoidal reference signals in the DER system. The ADRC technique is characterized by its nonlinear function, which provides a high robustness to the controlled system. However, in order to simplify the control structure by keeping its high reliability, this paper proposes to replace the nonlinear function with a simple error (termed linear ADRC), compares the impact of this modification on the system performances, and evaluates its operation in the presence of linear and nonlinear load variations. Simulation results are presented to demonstrate the efficiency of the proposed control approach for a three-phase DER.

Modelling, control design, and analysis of the inner control's loops intended for single‐phase voltage‐controlled inverter‐based microgrid

AbstractIn voltage‐controlled voltage source inverters (VSIs)‐based microgrids (MGs), the inner control is of prime interest task for guaranteeing safe and stable operation. In this paper, an in‐depth investigation of the modelling, control design, and analysis of the voltage and current inner control loops intended for single‐phase voltage‐controlled VSIs is established. The main objective of this work is to provide a comprehensive study of the mathematical modelling, control design, and performance evaluation of the inner control's loops considering different proportional‐integral (PI) controller types with and without compensation, and to determine the optimal scheme that can offer better performance in terms of implementation simplicity, robustness, and transient and steady‐state responses. Thus, the mathematical closed‐loop models of designed outer voltage and inner current control schemes based on PI, P, and feedforward controllers with and without compensation are, first, derived. Following this, a systematic and effective control design for tuning the different PI controllers’ parameters is proposed. Furthermore, an analysis revealing the performance of the designed voltage and current control schemes is provided. This analysis enables us to choose a P controller and PI feedforward controller for the current control loop and the voltage control loop, respectively. The chosen P and PI controllers should be simple; meanwhile, they should offer a wide bandwidth. A simulation study is carried out in MATLAB/Simulink software to assess the performance of the adopted inner control scheme for both linear and non‐linear loads. In addition, an experimental setup, based on a TMS320F2837xD μC, of a single‐phase VSI supplying linear and non‐linear loads is built to verify the effectiveness and the robustness of the adopted inner controller. The results demonstrated: (1) the necessity of introducing the compensation term, which is responsible for offering control improvement against voltage perturbation, (2) the high tracking performance of the chosen controller in terms of dynamic and steady‐state responses as well as its simplicity of implementation.

Designing a Model Predictive Controller for Displacement Control of Axial Piston Pump

Variable displacement hydraulic pumps are widely used for energy efficiency, and they often have a mechanical feedback mechanism to ensure target tracking control performance and stability of tilt angle control. Furthermore, there are many examples which add electronic control to realize higher tracking control performance. However, in such cases, the control performance is significantly affected by the dynamic characteristics of the mechanical feedback mechanism, and this problem prevent its widespread use. Additionally, tilt angle control is susceptible to changes in dynamic characteristics and load pressure depending on the operating point, and there are constraints on the tilt angle. Hence, high control performance cannot be obtained without considering these nonlinearities. In this study, the variable displacement pump without mechanical feedback mechanism is focused on, and the objective of this study is to design a displacement control system for a hydraulic pump based on a model predictive control (MPC) that can consider various constraints on the design step. An adaptive system, which handles changes in dynamic characteristics and the effects of load pressure, is introduced. Additionally, the control performance of adaptive MPC is compared to adaptive model matching-based MPC with inverse optimization that can optimally design the weight matrices of the evaluation function without trial and error. Furthermore, in order to improve the transient response, a variable control input constraints are added in these two control systems. Experimental results of control performance have shown that the proposed method achieved a high tracking performance and short settling time, which confirmed the effectiveness of the variable control input constraints.

A forward-inverse dynamic model for the hydraulic damping(magnetorheological) actuator based on cyclic stress–strain

Magnetorheological dampers (MRDs) are applied to hydraulic systems, which not only improve the underdamped characteristics of valve-controlled cylinder systems, but also help hydraulic actuators to resist high load impact. However, the high power density leads to the complexity of the internal flow channel of the damper, which seriously affects the output accuracy of the damping force. It can lead to the fact that existing dynamics models cannot accurately describe the hysteresis characteristics of the MRD. Therefore, this study proposes a simple and general dynamic model of MRD, which solves the problem that existing models are complex and difficult to invert. Firstly, the hydraulic damping actuator with the series MRD is taken as the research object. Based on the stress–strain hysteresis characteristics under the cyclic constitutive model, the hyperbolic tangent curve is reorganized and normalized. It can accurately describe the yield formation and yield dissipation stages of the hysteresis loop. Secondly, the relationship between the parameters of the dynamic model and the current is obtained according to the mechanical experimental data. Then the inverse model of the MRD is established by using the method of section-backstepping. Finally, in the static experiment, the mean absolute percentage error (MAPE) of the force at different velocity is less than 7.5%; in the dynamic experimental test, the MAPE of the force is 9.7%. The inverse dynamics model is verified to have high tracking performance under both static and dynamic forces. And it also indirectly confirms the effectiveness of the forward model.

Model-free scheme using time delay estimation with fixed-time FSMC for the nonlinear robot dynamics

<abstract><p>This paper presents a scheme of time-delay estimation (TDE) for unknown nonlinear robotic systems with uncertainty and external disturbances that utilizes fractional-order fixed-time sliding mode control (TDEFxFSMC). First, a detailed explanation and design concept of fractional-order fixed-time sliding mode control (FxFSMC) are provided. High performance tracking positions, non-chatter control inputs, and nonsingular fixed-time control are all realized with the FxSMC method. The proposed approach performs better and obtains superior performance when FxSMC is paired with fractional-order control. Furthermore, a TDE scheme is included in the suggested strategy to estimate the unknown nonlinear dynamics. Afterward, the suggested system's capacity to reach stability in fixed time is determined by using Lyapunov analyses. By showing the outcomes of the proposed technique applied to nonlinear robot dynamics, the efficacy of the recommended method is assessed, illustrated, and compared with the existing control scheme.</p></abstract>

Speed & gear dynamic planning and velocity tracking control considering traffic light information

To optimize fuel economy and improve vehicle longitudinal control performance, this paper proposed a speed & gear planning strategy considering traffic light information and velocity tracking control. First, to guarantee that the vehicle can pass through traffic lights without stop if possible and retain the highest terminal speed, this article discusses a Propelling or Coasting (PoC) serialization scheme. The speed & gear planning are realized by adjusting acceleration while propelling, with the upper and lower speeds set according to vehicle status and traffic light information; Second, to improve the accuracy and anti-perturbation performance of longitudinal control, a compound control method using improved Global Fast Terminal Sliding Mode Control (GFT-SMC) is developed, which enables the reduction of control output while maintaining high tracking performance and smooth control ability. Finally, both the hardware-in-loop (HIL) and real-vehicle experimental results show that the proposed control method effectively improves fuel economy with better speed tracking performance and strong perturbation robustness.

Gear injury equilibrium-oriented self-adjusted shifting strategy and advanced servo control

Abstract. The application of a self-adjusted strategy has received increasing attention in current transmission design. This paper proposes a gear injury equilibrium oriented self-adjusted shifting strategy (EASS), which can make injury of different gears consistent. First, the injury value of different gears was judged in real time, and the working time of the gear with the largest injury value was reduced by adjusting the shift curve. Second, by considering key points as constraints, an optimal displacement trajectory for a synchronizer was proposed, and then a compound control method using adaptive nonsingular global fast-terminal sliding-mode control (AN-GFTSMC) was developed, which enables a high tracking performance and a better parameter perturbation rejection ability. Finally, both the simulation and real-vehicle experimental results showed that, compared with conventional control, the designed control strategy had good performance: response time was decreased, gear injury tended to be consistent, and transmission reliability was improved.

Trajectory generation and tracking control for aggressive tail-sitter flights

We address the theoretical and practical problems related to the trajectory generation and tracking control of tail-sitter UAVs. Theoretically, we focus on the differential flatness property with full exploitation of actual UAV aerodynamic models, which lays a foundation for generating dynamically feasible trajectory and achieving high-performance tracking control. We have found that a tail-sitter is differentially flat with accurate (not simplified) aerodynamic models within the entire flight envelope, by specifying coordinate flight condition and choosing the vehicle position as the flat output. This fundamental property allows us to fully exploit the high-fidelity aerodynamic models in the trajectory planning and tracking control to achieve accurate tail-sitter flights. Particularly, an optimization-based trajectory planner for tail-sitters is proposed to design high-quality, smooth trajectories with consideration of kinodynamic constraints, singularity-free constraints, and actuator saturation. The planned trajectory of flat output is transformed into state trajectory in real time with optional consideration of wind in environments. To track the state trajectory, a global, singularity-free, and minimally parameterized on-manifold MPC is developed, which fully leverages the accurate aerodynamic model to achieve high-accuracy trajectory tracking within the whole flight envelope. The proposed algorithms are implemented on our quadrotor tail-sitter prototype, “Hong Hu,” and their effectiveness are demonstrated through extensive real-world experiments in both indoor and outdoor field tests, including agile SE(3) flight through consecutive narrow windows requiring specific attitude and with speed up to 10 m/s, typical tail-sitter maneuvers (transition, level flight, and loiter) with speed up to 20 m/s, and extremely aggressive aerobatic maneuvers (Wingover, Loop, Vertical Eight, and Cuban Eight) with acceleration up to 2.5 g. The video demonstration is available at https://youtu.be/2x_bLbVuyrk.

Adaptive finite-time prescribed performance tracking control for hydraulic servo systems with friction compensation

Hydraulic systems present numerous challenges in the development of high-performance tracking controllers due to their highly nonlinear characteristics and heavy modeling uncertainties. Addressing this issue, this paper presents a friction compensation-based finite-time tracking control strategy with prescribed performance constraints. The system model is first derived, taking into consideration the nonlinear behaviors and potential uncertainties encountered in practical scenarios. Then, an adaptive finite-time prescribed performance controller is synthesized based on the backstepping framework. A novel form of finite-time performance function is introduced to constrain the tracking error, which provides the closed-loop system with a less complex guarantee of finite-time convergence and can also deliver the required transient performance and steady-state accuracy. Additionally, given the impact of uncertainty terms and the difficulty in obtaining the virtual command derivative, we employ neural networks (NNs) to estimate unknown dynamics and introduce command filters to acquire the intermediate signals, simplifying the backstepping control design process. Theoretical analysis indicates that the proposed control strategy can achieve the desired tracking performance and ensure the stability of the entire closed-loop system. Comparative numerical simulations are presented to confirm the usefulness of the suggested approach.

STDFormer: Spatial-Temporal Motion Transformer for Multiple Object Tracking

Mainstream multi-object tracking methods exploit appearance information and/or motion information to achieve interframe association. However, dealing with similar appearance and occlusion is a challenge for appearance information, while motion information is limited by linear assumptions and is prone to failure in nonlinear motion patterns. In this work, we disregard appearance clues and propose a pure motion tracker to address the above issues. It dexterously utilizes Transformer to estimate complex motion and achieves high-performance tracking with low computing resources. Furthermore, contrastive learning is introduced to optimize feature representation for robust association. Specifically, we first exploit the long-range modeling capability of Transformer to mine intention information in temporal motion and decision information in spatial interaction and introduce prior detection to constrain the range of motion estimation. Then, we introduce contrastive learning as an auxiliary task to extract reliable motion features to compute affinity and introduce bidirectional matching to improve the affinity computation distribution. In addition, given that both tasks are dedicated to narrowing the embedding distance between the motion features of the tracked object and the detection features, we design a joint-motion-and-association framework to unify the above two tasks in one framework for optimization. The experimental results achieved with three benchmark datasets, MOT17, MOT20 and DanceTrack, verify the effectiveness of our proposed method. Compared with state-of-the-art methods, the proposed STDFormer sets a new state-of-the-art on DanceTrack and achieves competitive performance on MOT17 and MOT20. This demonstrates the advantage of our method in handling associations under similar appearance, occlusion or nonlinear motion. At the same time, the significant advantages of the proposed method over Transformer-based and contrastive learning-based methods suggest a new direction for the application of Transformer and contrastive learning in MOT. In addition, to verify the generalization of STDFormer in unmanned aerial vehicle (UAV) videos, we also evaluate STDFormer on VisDrone2019. The results show that STDFormer achieves state-of-the-art performance on VisDrone2019, which proves that it can handle small-scale object associations in UAV videos well. The code is available at https://github.com/Xiaotong-Zhu/STDFormer.

SOCF: A correlation filter for real-time UAV tracking based on spatial disturbance suppression and object saliency-aware

The discriminative correlation filter (DCF) is commonly used in aerial object tracking due to its high tracking accuracy and computing speed. However, when similar object disturbances emerge in the background, the response map will generate sub-peaks, which may eventually lead to tracking failure. Meanwhile, the lack of attention to the tracked object can also cause tracking performance degradation. To these concerns, this paper proposes a novel correlation filter algorithm for real-time aerial tracking based on spatial disturbance suppression and object saliency-aware, i.e., SOCF. Firstly, this paper designs a novel spatial disturbance suppression strategy. Using the temporal information in the historical response maps, we construct a context response map, deviating it from the current response map to detect disturbance information in the background. Then, construct a spatial interference map, divide it into n×n non-overlapping regions, and suppress the negative samples in the disturbance region within the main regression. Furthermore, an object saliency-aware strategy is proposed, using a saliency detection algorithm to calculate the object-aware mask and multiplying it with the detection filter to obtain the object-aware filter. By constructing the object-aware regularization in the training phase, the trained detection filter focuses more on the object itself and can effectively separate the object from the background. Extensive experiments on four widely used unmanned aerial vehicle (UAV) datasets demonstrate that the proposed SOCF tracker achieves high tracking performance. Meanwhile, our tracker can maintain real-time aerial tracking at 48 FPS on a single CPU.

Adaptive-Neuro-Learning Tracking Control for the Permanent Magnet Synchronous Motor with Full-State Prescribed Performances and Time Delays

High-performance tracking control is essential for permanent magnet synchronous motors in the perturbed environment. Given this, a new hybrid controller is proposed in this study for a permanent magnet synchronous motor with load disturbances as well as time delays. First, a new prescribed performance method is proposed to achieve the full-state performance constraints with load disturbances. Second, a time-varying filter is proposed for the first time to avoid the “complexity explosion” problem of the backstepping method while guaranteeing the convergence of the filtering error. Third, by combining Lyapunov–Krasovskii functionals with adaptive neural networks, the time-delay disturbance and unknown nonlinear dynamics of the control system have been solved. The stability analysis proves that all signals in the closed-loop system are bounded. To show the effectiveness of the intelligent controller, the comparison simulations are given to confirm the advantages of the proposed adaptive neural control scheme.

Tracking control based on proportional resonant controller for electro‐hydraulic systems with input delay

AbstractThe growth of marine resources exploitation has led to increased research interest in electro‐hydraulic systems for marine simulation. This study proposes a proportional plus resonant (PR) controller to achieve high tracking performance for electro‐hydraulic systems. Due to the complex structure and non‐linear flow dynamics, establishing accurate mathematical models of electro‐hydraulic systems is challenging. The local linearization technique is applied to simplify the process of model establishment. The system gain is limited by the stability margin due to the input delay, resulting in a phase delay between the real trajectory and the desired reference. To compensate for the phase delay in tracking periodic reference with the traditional proportional and integral controller, a resonant item is added into the controller. The stability analysis of the closed‐loop system is performed using Routh‐Hurwitz criterion. Furthermore, the controller parameters are tuned based on the frequency characteristic to achieve high tracking performance and maintain the adequate stability margin. The effectiveness of the proposed PR controller in tracking periodic references is validated through numerical simulations and practical experiments.

Modeling and Trajectory Tracking Control for a Multi-Section Continuum Manipulator

This paper presents a dynamic modeling and trajectory tracking control design for a hyper-redundant continuum manipulator (HRM), which is able to continuously bend along its length. The HRM has the advantages of high flexibility, large workspace, and low inertia, but also poses challenges of complex kinematics, and dynamic coupling. To address these challenges, the kinematics model was developed based on the piecewise constant curvature (PCC) assumption that captures the relationship between the cable lengths, joint angles, and end-effector pose. Inverse kinematics is presented geometrically by solving trigonometric equations. The dynamic model for the continuum robot (CR) is derived using the Euler–Lagrange representation, which incorporates the effects of gravity and elasticity. Accordingly, three control strategies are developed and applied to a two-section continuum robot, which are the inverse dynamic proportional integral derivative controller (PID), fuzzy logic controller (FLC), and sliding mode controller (SMC). We validate our proposed methods through simulations on various 3D trajectories, utilized MATLAB symbolic math toolbox in conjunction with Simulink simulation; demonstrate and evaluate different close-loop dynamic control responses for different scenarios. This simulation used in the creation of a unique animated simulation for a whole 3D continuum robot while tracking its desired trajectories. Finally, the simulation results demonstrate that our proposed methods can effectively control the HRM with high tracking performance, exhibiting enhanced responses in terms of settling time, rising time, and steady-state error. While the developed SMC outperforms Poth's PID and FLC in terms of robustness and settling time (51% and 10.3%), respectively.

Safety-Guaranteed, Robust, Nonlinear, Path-Following Control of the Underactuated Hovercraft Based on FTESO

On account of the external disturbances and difficult maneuverability of a hovercraft, this paper devises a safety-guaranteed, robust, nonlinear, path-following control strategy of a hovercraft targeted for unknown dynamics, unavailable velocity, and unknown external ocean disturbances. Firstly, for the sake of accurately observing unavailable lumped disturbances and unavailable velocity measurements, a finite-time extended state observer (FTESO) is proposed. Secondly, a line-of-sight (LOS) guidance law constructed with a bounded-gain-forgetting (BGF) adaptive estimator is devised to follow the desired path while considering external environmental disturbances accurately, in which the tracking errors and the parameter estimation are both proven to be bounded. In addition, for the sake of safety, a safety-guaranteed auxiliary system that can constrain the drift angle during the hovercraft’s navigation is proposed. Thirdly, the robust, nonlinear, path-following controllers achieved high tracking performance with the constructed safety-guaranteed compensation backstepping method. Finally, according to the Lyapunov and homogeneous theories, the observation error can be guaranteed to zero and the tracking error can converge to an arbitrarily small region near zero in finite time. Numerical simulations illustrate the effectiveness for the proposed robust, nonlinear, path-following scheme.

Predefined Time Active Disturbance Rejection for Nonholonomic Mobile Robots

This article studies the fast path tracking problem for nonholonomic mobile robots with unknown slipping and skidding. Firstly, considering the steering problem, a new mathematical model of nonholonomic mobile robot is derived. Secondly, to estimate the unknown slipping and skidding of a nonholonomic mobile robot quickly and accurately, new predefined time observers, which can attain a predefined settling time, are established. Thirdly, based on the observers and the sliding mode approaches, predefined time active controllers are proposed to achieve high precision control performance of the nonholonomic mobile robot tracking. The method proposed in this article can achieve uniformly global stability within a predefined time, which makes the adjustment of the convergence time of the nonholonomic mobile robot easier and convenient. Finally, the simulation results validated the theoretical results.

Q-Learning-Based Multi-Rate Optimal Control for Process Industries

This paper studies the multi-rate optimal control problem for a class of industrial processes, whose controlling rate will be set faster than the sampling rate sometimes. This multi-rate phenomenon makes the accurate modeling of control systems challenging and difficult. In this brief, we present a model-free self-learning control scheme for the real-time solution of this problem, combining the lifting technology and Q-learning. For the asynchronous periods, the lifting system is established first to reconstruct the input and output by stacking the control and sampling signals to a frame period, maintaining the original dynamic information. Then, Q-learning is adopted to learn the optimal control policy with the real-time data and the convergence analysis of the proposed algorithm is derived. In this way, the control actions are executed at a faster rate to obtain the better dynamic performance. Finally, a hardware-in-loop (HIL) simulation study for process industries is carried out, showing that the proposed approach has high tracking and real-time performance.

A Fast Sliding Mode Speed Controller for PMSM Based on New Compound Reaching Law With Improved Sliding Mode Observer

To reduce the convergence time and improve the anti-disturbance capability of the permanent magnet synchronous motor (PMSM) drivers, a fast sliding mode speed controller based on a new compound reaching law (NCRL) and disturbance observer compensation technique is proposed. Firstly, to solve the conflict between fast convergence rate and chattering reduction in the existing solutions, the NCRL is proposed by introducing the exponential function and the power term piecewise function. The convergence of NCRL is proved and the reaching time is certified to be independent of the system’s initial state. The proposed NCRL can effectively reduce the reaching time, and smooth the output signal while maintaining the high tracking performance of the controller under different conditions. However, external load disturbances may degrade the performance of the proposed NCRL-based speed controller. The proposed controller is then combined with an improved sliding mode observer (ISMO), named the NCRL+ISMO method, to improve the estimation accuracy of the lumped disturbance and the robustness of the system. The stability of the proposed method is analyzed by the Lyapunov function. Experimental results show that the proposed method can reduce speed fluctuation and convergence time, and enhance anti-disturbance capability.

Learning to Propose and Refine for Accurate and Robust Tracking via an Alignment Convolution

Precise and robust feature extraction plays a key role in high-performance tracking to analyse the videos from drones, surveillance and automatic driving, etc. However, most existing Siamese network-based trackers mainly focus on constructing complicated network models and refinement strategies, while using comparatively simple and heuristic conventional or deformable convolutions to extract features from the sampling positions that may be far away from a target region. Consequently, the coarsely extracted features may introduce background noise and degrade the tracking performance. To address this issue, we present a propose-and-refine tracker (PRTracker) that combines anchor-free style proposals at the coarse level, and alignment convolution-driven refinement at the fine level. Specifically, at the coarse level, we design an anchor-free model to effectively generate proposals that provide more reliable interested regions for further verifying. At the fine level, an alignment convolution-based refinement strategy is adopted to improve the convolutional sampling positions of the proposals, thus making the classification and regression of them more accurate. Through using alignment convolution, the convolution sampling positions of the proposals can be efficiently and effectively re-localized, thus improving the accuracy of the extracted features. Finally, a simple yet robust target mask is designed to make full use of the initial state of a target to further improve the tracking performance. The proposed PRTracker achieves a competitive performance against six tracking benchmarks (i.e., UAV123, VOT2018, VOT2019, OTB100, NfS and LaSOT) at 75 FPS.

Adaptive Two-Degrees-of-Freedom Current Control for Solenoids: Theoretical Investigation and Practical Application

In this article, an adaptive two-degrees-of-freedom current control algorithm for solenoids is presented comprising an adaptive pole placement controller in combination with a regularized least-squares parameter estimation law. An additional adaptive feedforward controller takes advantage of the estimated plant parameters to further enhance the tracking performance. The stability of the overall closed-loop system is rigorously proven. The proposed solution differs from existing approaches by the adaptive feedforward controller and the way the parameter estimation is performed. The control concept is applied with the same controller parametrization to three solenoids from different applications, with substantially differing parameters. The experimental results show high tracking performance and fast parameter convergence even with poor initial estimates and despite the nonlinear dependence of the inductance on the current and position. The experimental results are also compared to two benchmark control design paradigms known from the literature, i.e. a second-order sliding mode controller and a nonlinear model reference adaptive control solution, which are both outperformed by the proposed controller.