Feedback Tracking Research Articles

Practical prescribed-time sliding-mode output feedback tracking control for heterogeneous vehicle platoon

This paper proposes a new output feedback tracking control for platoon systems of heterogeneous vehicles subject to nonlinear disturbances. First, a distributed prescribed-time extended-state observer (PTESO) is presented to estimate the unmeasurable system states and nonlinear disturbances. On this basis, a practical prescribed-time sliding-mode output feedback control (PPTSMC) law is designed, such that the constructed closed-loop system always guarantees its preset performance ability regardless of the initial conditions. Finally, the effectiveness and superiority of the developed method are verified by theoretical analysis and simulation.

Improved sliding mode control for induction motor based on twisting algorithm

<p>In this paper, the output feedback tracking issue of induction motors is resolved by applying the sliding mode approach. We designed and implemented two robust sliding mode (SM) techniques to achieve high-performance control of induction motor drive; the second-order sliding mode (SOSM) approach using the twisting algorithm was compared with the classical sliding mode control. The method of decoupling electromagnetic torque and rotor flux for the induction motor was derived from the rotor field orientation control in the synchronous reference frame. The objective of the proposed methods is to control the rotor speed and the square of the rotor flux separately, in order to obtain robust control against disturbances and parametric uncertainties, and at the same time minimize the chattering phenomenon—the most significant drawback in the actual implementation of this technique. The stability of the proposed first-order sliding mode control was confirmed using Lyapunov stability theory. The availability and effectiveness of the proposed techniques were demonstrated through experimental results. The comparison between the results of the two proposed methods shows that the second-order sliding mode control using the twisting algorithm not only guarantees the same robustness and dynamic performances of traditional first-order sliding mode control but also achieves the reduction of the chattering phenomenon.</p>

The Effectiveness of Digital Health Interventions on Diabetes Self-Management and Quality of Life in Adolescents with Type 1 Diabetes: A Comprehensive Review

This comprehensive review explores the effectiveness of digital health interventions in enhancing diabetes self-management and quality of life among adolescents with Type 1 diabetes (T1D). As traditional management approaches often fall short in engaging this population, digital health tools, including mobile applications, telemedicine, continuous glucose monitoring, and online support communities, have emerged as promising alternatives. This review synthesized current literature to evaluate how these interventions improve accessibility, engagement, personalized feedback, and health data tracking, ultimately fostering better self-management behaviors. The discussion highlighted the multifaceted nature of effectiveness, addressing challenges such as accessibility, data privacy, and individual variability in engagement. Future directions emphasized the need for personalized interventions, integration with healthcare providers, and longitudinal studies to assess long-term impacts. Methodologically, this article utilized a systematic approach to review and synthesize existing studies on digital health interventions in the context of T1D management. The findings underscored the potential of digital health interventions to transform diabetes care for adolescents, providing a framework for further research and practical applications in clinical settings. Keywords: Digital Health Interventions, Type 1 Diabetes, Adolescents, Self-Management, Quality of Life.

A124: Experimental Study on KDL Online Intervention Effect on the Slow Development of Children's Gross Movements

Background/Purpose: Children's rough movement development delay is not good, and the current health status of children is not optimistic. Therefore, this study relied on the concept of KDL sports games to implement a systematic and comprehensive sports intervention of "online intervention, school and family exercise promotion". Using an AI sports app, according to the characteristics of children's physical and mental development, comprehensive design structured online KDL movement experiment scheme, effective organization and tracking feedback, was used to verify the KDL sports games in 3-6 years old big movement slow children, and to create online exercise intervention mode, enrich exercise intervention form, and to provide ideas. Methods/Design: In this study, we randomly selected three kindergartens in Pudong New Area of Shanghai, including 50 children (male 23 and 27 female) aged 3-6 years as the research objects, and implemented online exercise intervention for half an hour a day, 7 days a week for 6 months. Online intervention combined with KDL sports game concept, using an AI sports APP was given priority to with interesting scene teaching, through the design of "walking, running, jumping" theme of personalized sports intervention games, decorate "personal movement ability" and "family movement" online sports clock, family, school, community, daily to parents targeted sports feedback, monthly to the teacher targeted movement feedback. AI exercise APP was used before and after the intervention. Data were processed by descriptive statistics, using paired sample T-test and ANOVA. Results: The paired sample T-test of children's motor ability scores showed that the overall level of children's motor ability before and after the experiment varied significantly (t = -24.859, p = 0.000 < 0.01, see Table for details). Therefore, KDL online sports games had obvious effects on rough movements. ANOVA on locomotor scores before and after the test. According to the results of ANOVA, there was no significant difference in the exercise capacity before and after the experiment (F = 1.248, p = 0.292 > 0.05). Online intervention had a balanced effect on improving children's exercise ability. Conclusion: Through the organic combination of the concept of KDL sports games and the new mode, the KDL online sports games with rich types and various forms are reasonably designed, which can effectively improve the rough movement level of children ages 3-6 years old. Online KDL sports game intervention fits with the sensitive period of children's movement development, and comprehensively improves the sports ability of children.

Developing an AI-Based clinical decision support system for basal insulin titration in type 2 diabetes in primary Care: A Mixed-Methods evaluation using heuristic Analysis, user Feedback, and eye tracking.

The progressive nature of type 2 diabetes often, in time, necessitates basal insulin therapy to achieve glycemic targets. However, despite standardized titration algorithms, many people remain poorly controlled after initiating insulin therapy, leading to suboptimal glycemic control and complications. Both healthcare professionals and people with type 2 diabetes have expressed the need for novel tools to aid in this process. Traditional titration methods often lack the precision needed to address individual differences in glycemic response. Recent studies have highlighted the potential of AI-driven solutions, which can leverage large datasets to model patient-specific characteristics. Therefore, this study aims to develop a digital platform for an AI-based clinical decision support system to assist healthcare professionals in primary care with personalized and optimal basal insulin titration for people with type 2 diabetes. An iterative mixed-method approach was used for system development, incorporating usability engineering principles. Initial requirements were gathered from domain experts and followed by heuristic evaluation and user-based evaluation. Data from these evaluations guided successive iterations of the prototype. The initial prototype featured a retrospective graph of insulin doses and fasting glucose levels and a dose adjustment simulation environment. Heuristic evaluation identified 92 issues, primarily related to minimalistic and aesthetic design. The second prototype addressed these concerns, but user-based evaluation found 66 additional usability problems, notably with HbA1c presentation and the need for more glucose measures. The final prototype showed high usability, with a median System Usability Scale score of 93.8. Task completion rates were high (task 1: 87.5%, task 2: 75.0%, and task 3: 100%). Eye-tracking data showed minimal distractions. The AI-based Clinical Decision Support System shows promise in managing basal insulin titration for people with type 2 diabetes, addressing clinical inertia, and providing a user-friendly, efficient tool to improve glycemic control during insulin titration.

Anti-disturbance reliable memory feedback tracking control for unmanned aerial vehicle systems

The problem of memory feedback tracking control for unmanned aerial vehicle systems subject to nonlinear actuator faults and disturbances is investigated in this paper. First, a generalized extended state observer is constructed to get the estimates of the system states and the disturbances. Then, by incorporating the obtained estimates in the control design, an anti-disturbance memory feedback tracking control law is designed such that the resulting closed-loop augmented system is asymptotically stable, and thereby the states of the unmanned aerial vehicle system track the given reference trajectories with satisfactory disturbance rejection performance. Notably, by constructing appropriate asymmetric Lyapunov–Krasovskii functional, the stability and stabilization conditions for guaranteeing the desired tracking and disturbance attenuation performance are derived in terms of linear matrix inequalities. Further, the observer gains are evaluated using the pole placement approach. Finally, numerical simulations are given to verify that the proposed control strategy is viable.

A tracking guidance method with funnel scheduling for Mars landing

Powered descent guidance is an enabling technology for extraterrestrial soft landing and reusable rocket recovery. Although the focus of guidance research has tilted towards trajectory optimization recently, feedback tracking control is still necessary to suppress the effects of flight perturbations and uncertainties. In this paper, for the Mars landing mission, the behavior of the powered descent closed-loop system is considered and a guidance method based on funnel (finite-time invariant set) scheduling is proposed. Firstly, the basic model of the powered descent problem is given and its state feedback closed-loop system is constructed. Subsequently, considering the thrust uncertainty as well as saturation constraints, the powered descent funnel model is established, and a point-by-point computation method based on Sum of Squares Programming (SOSP) is presented. On this basis, multiple nominal trajectories and their nearby funnels are solved. The guidance scheme based on funnel scheduling is formulated, which utilizes the funnels to provide a good basis for tracking different nominal trajectories, enhancing the adaptability to the initial state dispersions. The simulation results show that for the thrust deviation considered, the states within the funnel inlet can ensure convergence. The similar level of performance on fuel consumption and terminal accuracy of funnel scheduling guidance with convex programming guidance implies the bottleneck in feedback control performance.

Revolutionizing education with AI: The adaptive cognitive enhancement model (ACEM) for personalized cognitive development

Abstract. The integration of artificial intelligence (AI) into education has opened doors to personalized learning experiences. This paper introduces the Adaptive Cognitive Enhancement Model (ACEM), a cutting-edge AI-driven framework designed to personalize cognitive development for students. Leveraging advanced machine learning algorithms and quantitative analysis, ACEM adapts educational content and learning strategies to individual cognitive needs. The model encompasses five key components: cognitive profiling, adaptive learning paths, intelligent feedback, motivational strategies, and longitudinal tracking. Through quantitative analysis and mathematical modeling, the paper demonstrates how ACEM significantly enhances learning outcomes compared to traditional education models. The discussion section provides a detailed exploration of each model component, its architecture, and its role in optimizing personalized cognitive development. Furthermore, challenges such as data privacy, scalability, and model interpretability are examined, alongside potential solutions. The conclusion underscores the transformative potential of ACEM in revolutionizing education.

COMBINED FIRST AND SECOND-ORDER SLIDING MODE CONTROL OF INDUCTION MOTOR

This paper uses hybrid sliding mode control to solve an output feedback tracking problem for the induction motor (IM). The main idea of this approach lies in the design of a law that ensures a smooth transition between two sliding regimes based on the choice of two sliding surfaces, one being linked to the other. The control is a combination of first and second-order sliding modes. The proposed approach controls the speed and rotor flux amplitude of the induction motor and guarantees the stability of the decoupled speed and rotor flux by using a Lyapunov method. The hybrid sliding mode algorithm provides robustness despite external disturbances and parameter variations. The controller has been implemented on an experimental setup, and results are presented to confirm the proposed method's effectiveness, robustness, and good performance.

Position measurement and output feedback tracking method of hydraulic servo system based on pressure sensor

Instead of using position sensor, a position measurement and output feedback tracking method is proposed based on pressure sensor for a class of hydraulic servo system. First, the desired accumulator pressure is calculated and obtained in an open-loop way according to the desired cylinder position. Then, the position tracking task can be indirectly realized by achieving the pressure control. As a result, only one pressure sensor is required instead of position sensor, which is different from almost all the traditional output feedback position controller of hydraulic system. For the closed-loop pressure tracking, a desired compensation method and an auxiliary variable-based state observer are integrated to form the nonlinear controller. The theoretical and numerical simulation results demonstrate that the presented controller can attain satisfactory pressure tracking performance.

MITIGATION OF POWER QUALITY PROBLEMS IN STEEL PLANTS USING STATIC SYNCHRONOUS COMPENSATOR

This paper focuses on mitigating various power quality issues from electric arc furnaces (EAF) in steel plants, including voltage flicker, sag, swell, and harmonics. Arc furnaces are recognized as one of the industry's most nonlinear electrically polluting loads. The proposed solution in this article to address all power quality issues caused by EAF is implementing a flexible power supply strategy known as a static synchronous compensator (STATCOM) for injecting reactive energy into the electrical grid. In this study, an experimental model of EAF is utilized based on real measurements of the variation of arc resistance and arc reactance during the melting phase. The pulse width modulation (PWM) technique is employed for current tracking feedback control to generate the required reactive current. A fuzzy logic controller (FLC) is employed for the DC bus.

Robust output group formation tracking control of heterogeneous multi-agent systems with multiple leaders using reinforcement learning

This paper studies the distributed output formation tracking problem of grouped heterogeneous multi-agent systems under multiple leaders and uncertainties using reinforcement learning (RL). The outputs of followers are supposed to achieve robust tracking to the respective convex point of group leaders while generating an expected time-varying formation configuration. First, a distributed adaptive observer is designed under a directed graph to coordinate the multiple group leaders while estimating the leaders’ dynamics in finite-time. The adaptive mechanism avoids global information of the graph. Second, an optimal tracking problem with respect to the observer is formulated for each follower, while the feedback tracking controller is derived using an action-dependent RL algorithm. An extended learning process for essential dynamics is constructed using the same data, while the output regulation equations are solved equivalently. Third, the robust formation controller and feasibility condition are further proposed based on previous learning results. Stability of the synthetical data-driven controller is analyzed under internal uncertainties and external disturbances. Finally, simulation results are provided to demonstrate the effectiveness of the hierarchical control framework.

Semiglobal Exponential Stability for Dual-Quaternion-Based Rigid-Body Tracking Control

Semi-Global Exponential Stability (SGES) is proven for the combined attitude and position rigid-body motion tracking problem, presenting a significant upgrade compared to previously proposed controllers which can only guarantee asymptotic stability. Dual quaternions are used to jointly represent the rotational and translational tracking error dynamics of the rigid body. A novel nonlinear feedback tracking controller is proposed, and a Lyapunov-based analysis is provided to prove SGES of the closed-loop dynamics. The analysis does not place any restrictions on the reference trajectory or the feedback gains. This stronger SGES result aids in further analyzing the robustness of the rigid-body system by establishing input-to-state stability (ISS) in the presence of time-varying additive and bounded external disturbances. Motivated by the fact that, in many aerospace applications, stringent adherence to safety constraints such as approach path and input constraints is critical for overall mission success, a framework for safe control of spacecraft is presented that combines the proposed feedback controller with control barrier functions. Numerical simulations are provided to verify the SGES and ISS results and also showcase the efficacy of the proposed nonlinear feedback controller in several nontrivial scenarios, including trajectory tracking, transposition and docking, flip maneuvers, collision avoidance, and rendezvous.

Nonconvex distributed feedback optimization for aggregative cooperative robotics

Distributed aggregative optimization is a recently emerged framework in which the agents of a network want to minimize the sum of local objective functions, each one depending on the agent decision variable (e.g., the local position of a team of robots) and an aggregation of all the agents’ variables (e.g., the team barycenter). In this paper, we address a distributed feedback optimization framework in which agents implement a local (distributed) policy to reach a steady-state minimizing an aggregative cost function. We propose AGGREGATIVE TRACKING FEEDBACK, i.e., a novel distributed feedback optimization law in which each agent combines a closed-loop gradient flow with a consensus-based dynamic compensator reconstructing the missing global information. By using tools from system theory, we prove that AGGREGATIVE TRACKING FEEDBACK steers the network to a stationary point of an aggregative optimization problem with (possibly) nonconvex objective function. The effectiveness of the proposed method is validated through numerical simulations on a multi-robot surveillance scenario.

Tracking optimal feedback control under uncertain parameters

Optimal control problems of tracking type for a class of linear systems with uncertain parameters in the dynamics are investigated. An affine tracking feedback control input is obtained by considering the minimization of an energy-like functional depending on a finite ensemble of training/sample parameters. It is computed from the solution of an associated differential Riccati equation. Simulations are presented showing the tracking performance of the computed input for trained as well as untrained parameters.

Robust Output Feedback Stabilization and Tracking for an Uncertain Nonholonomic Systems with Application to a Mobile Robot.

This paper presents a novel robust output feedback control that simultaneously performs both stabilization and trajectory tracking for a class of underactuated nonholonomic systems despite model uncertainties, external disturbance, and the absence of velocity measurement. To solve this challenging problem, a generalized normal form has been successfully created by employing an input-output feedback linearization approach and a change in coordinates (diffeomorphism). This research mainly focuses on the stabilization problem of nonholonomic systems that can be transformed to a normal form and pose several challenges, including (i) a nontriangular normal form, (ii) the internal dynamics of the system are non-affine in control, and (iii) the zero dynamics of the system are not in minimum phase. The proposed scheme utilizes combined backstepping and sliding mode control (SMC) techniques. Furthermore, the full-order high gain observer (HGO) has been developed to estimate the derivative of output functions and internal dynamics. Then, full-order HGO and the backstepping SMC have been integrated to synthesize a robust output feedback controller. A differential-drive type (2,0) the wheeled mobile robot has been considered as an example to support the theoretical results. The simulation results demonstrate that the backstepping SMC exhibits robustness against bounded uncertainties.

A non-identification adaptive backstepping approach to global output feedback tracking control of nonlinear systems with input quantization

A non-identification adaptive backstepping approach to global output feedback tracking control of nonlinear systems with input quantization

Control of combustion distributions in compression-ignition engines using rate-constrained model predictive control with reference preview

To support the transition toward sustainable alternative fuels, advanced combustion control strategies can enable operation of compression-ignition engines with a wide range of fuels under challenging inlet conditions. This work presents a rate-constrained model predictive controller that uses state estimate feedback and integral tracking to control combustion phasing distributions by coordinating fuel injection timing with the power supplied to an electrically heated in-cylinder ignition assist device. The controller was validated in simulation using a statistical virtual engine that replicates both transient and steady-state stochastic combustion behavior. This virtual engine was tuned with data from experiments conducted at a low pressure-temperature inlet condition that induced highly variable combustion behavior akin to operating with a low cetane fuel. The controller achieves rapid tracking of combustion phasing step commands by supplying ignition assist power when needed to support fuel injection timing. All the while, it maintains closed-loop combustion variability at less than 6% higher than the open-loop system variability, and enforces ignition assist power range and rate constraints to reduce thermo-mechanical stress on the actuator. Furthermore, reference tracking is ensured even if combustion sensitivity to the ignition assist actuator deviates by as much as 83% from the controller’s internal model, without the need for retuning control parameters. Finally, the controller can coordinate actuators early and speed up tracking when a reference trajectory is previewed ahead of time, and its horizons can be tuned in a manner that maintains desirable control performance without compromising on computational tractability.

Output feedback distributed optimization algorithms of higher‐order uncertain nonlinear multi‐agent systems

AbstractOutput feedback distributed optimization problem is studied for higher‐order multi‐agent systems with uncertain nonlinearities, which are assumed to satisfy linear growth conditions. The agents dynamics are permitted to be heterogenous with different nonlinearities. The problem is solved by embedded control approach based output feedback distributed optimization algorithms. First, a first‐order optimal signal generator is designed, of which outputs reach the minimizer of the global cost function. Second, embedding the generator into the feedback loop and taking the outputs of the generator as the reference outputs of the agents, output feedback tracking controllers are designed for the higher‐order multi‐agent systems on the basis of nonseparation principle and feedback domination technique. Under the proposed control algorithms, all the agents outputs asymptotically approach a bounded neighbor region of the global minimizer. Simulations demonstrate the effectiveness of the proposed output feedback distributed optimization algorithms.

Adaptive Output Feedback Tracking Control for Nonlinear Systems with Unknown Growth Rate

Article Adaptive Output Feedback Tracking Control for Nonlinear Systems with Unknown Growth Rate Manman Yuan 1,2,*, and Wei Qian 1,2,* 1 School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China 2 Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Jiaozuo 454003, China * Correspondence: ymmamhappy@163.com; qwei@hpu.edu.cn Received: 4 September 2023 Accepted: 7 November 2023 Published: 26 March 2024 Abstract: In this paper, the problem of adaptive output feedback tracking is considered for a class of nonlinear systems with lower-triangular structures. A novel dynamic gain is introduced to deal with the unknown growth rate. By coupling the dynamic gain with the observer and the controller, an adaptive output tracking controller is developed, which can guarantee that all signals of the closed-loop system are globally bounded. Finally, the effectiveness of the presented control scheme is illustrated by a numerical example.