Adaptive Output Feedback Control Research Articles

Composite-Observer-Based Adaptive Consensus Tracking Control for Nonlinear MASs With Unknown Control Directions Against Deception Attacks.

This article primarily studies the adaptive output-feedback consensus tracking control issue for nonlinear multiagent systems (MASs) with unknown control directions against deception attacks. First, a composite observer combining the state observer and the disturbance observer is developed to concurrently estimate the states of confronting deception attacks and unmeasurable disturbances. Moreover, to resolve the unknown gains resulting from deception attacks, the adaptive attack compensator is proposed. Furthermore, in view of the logarithm Lyapunov function in the final step of the design process and the intelligent approximation technique, a new composite-observer-based adaptive consensus tracking control strategy is constructed. The suggested control strategy ensures the boundedness of all the closed-loop signals while also achieving synchronous tracking of the leader's output by the followers. Last but not least, the effectiveness of the suggested control strategy is validated through two simulation examples.

Global adaptive output feedback control of nonlinear time-delay systems with measurement uncertainty

Global adaptive output feedback control of nonlinear time-delay systems with measurement uncertainty

Reinforcement learning-based adaptive optimal output feedback control for nonlinear systems with output quantization

Reinforcement learning-based adaptive optimal output feedback control for nonlinear systems with output quantization

Output feedback control for non-strict feedback nonlinear systems: an adaptive fuzzy backstepping scheme

The adaptive fuzzy tracking control design issue is considered in this paper for a class of non-strict feedback nonlinear systems subject to external disturbances. A fuzzy high-gain state observer is constructed to reconstruct the unmeasurable states of the system by leveraging the universal approximation property of fuzzy logic systems for arbitrary unknown nonlinear functions. Within the framework of backstepping control design and in conjunction with adaptive techniques, an adaptive fuzzy control approach is presented. Subsequently, to work out the inherent ‘complexity explosion’ problem of the proposed control approach, dynamic surface control technique is introduced, leading to a simplified adaptive fuzzy output feedback control scheme. Stability analysis shows that the two proposed control schemes can ensure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded, meanwhile the system tracking error can converge to a small neighborhood around the origin. Ultimately, a numerical simulation example is provided to validate the feasibility of the proposed control scheme.

Fuzzy adaptive output feedback control for a class of stochastic nonlinear systems under input/output quantization

Fuzzy adaptive output feedback control for a class of stochastic nonlinear systems under input/output quantization

Global Regulation of Feedforward Nonlinear Systems: A Logic-Based Switching Gain Approach.

In this article, we investigate the global regulation problem for a class of feedforward nonlinear systems. Notably, the systems under consideration allow unknown input-output-dependent nonlinear growth rates, which has not been considered in existing works. A novel logic-based switching (LBS) gain approach is proposed to counteract the system uncertainties and nonlinearities. The key idea of the proposed approach is that whenever the Lyapunov function does not decrease as desired, the switching mechanism is activated so that a new gain is adopted. Furthermore, a tanh-type speed-regulation function is embedded into the switching mechanism for the first time to improve the convergence speed and transient performance. Then, a switching adaptive output feedback (SAOF) controller, which is of concise form and low complexity, is proposed based on the developed switching mechanism. It is shown that the objective of global regulation is achieved with faster convergence speed and better transient performance under the proposed controller. Moreover, it is also shown that the proposed control approach can be extended with the enhanced switching mechanism to deal with feedforward nonlinear systems with external disturbances. Finally, numerical examples are presented to demonstrate the effectiveness and advantages of our approach in comparison with the existing approaches.

Adaptive event-triggered dynamic output feedback control for networked autonomous vehicles affected by signal quantization and actuator failures

In this paper, an event-triggered sliding-mode output feedback control (SMOFC) strategy with adaptive fault-tolerance is proposed to achieve path tracking for networked autonomous vehicles, considering the challenges posed by network communication and electric steering systems. Initially, signal quantization, external disturbances, and actuator faults are incorporated into the vehicle model. This incorporation enhances the designed controller’s robustness against a broader and more demanding range of driving scenarios. Subsequently, in situations where only output feedback is available, a dynamic output compensator is designed to reconstruct the unmeasurable vehicle state. Utilizing the reconstructed vehicle state, an event-triggered strategy is devised to alleviate the network burden and determine the minimum time between triggering events to prevent Zeno behavior. Furthermore, an adaptive mechanism is employed to estimate the actuator fault boundaries. The performance of the designed controller is evaluated through simulation instances.

Differential neural network based adaptive average output feedback control design for dosage determination on cancer based immunotherapy treatment

Immunotherapy involves natural and synthetic substances to stimulate the body’s immune response. This treatment approach is practical not only for addressing immune deficiencies but also for combating malignancies. This paper describes a non-parametric approximated adaptive control process for managing cancer dynamics under immunotherapy treatment, utilizing a combination of a differential neural network (DNN) observer and nonlinear control techniques such as sliding mode and local optimal strategies. By employing the state estimation and control methods, close tracking between the estimated states provided by the neural network and the cancer model dynamics is possible. Internal model reconstruction and an observer provided by a variable structure model are essential for controlling unknown plants. Furthermore, the control design has successfully reduced tumor cells despite uncertainties and external perturbations affecting cancer dynamics. This robustness enhances the reliability of the proposed design. A virtual real-time scheme was developed to demonstrate this controller’s feasibility in real clinical scenarios. In this scheme, a simulated patient generates variables of immunotherapy dynamics as electrical signals, which are then analyzed by a real-time project.

Preface

From September 27th to 29th, 2024, Harbin, China welcomed an academic feast - the 2024 2nd International Conference on Applied Statistics, Modeling and Advanced Algorithms (ASMA 2024). The conference gathers together about 100 famous scholars, experts, engineers and technicians as well as technology developers at home and abroad to discuss the latest research results and development trends in the fields of applied statistics, modeling and advanced algorithms. The ASMA conference series holds a significant place in the academic calendar, serving as a platform for sharing cutting-edge research, fostering collaborations, and driving innovation. The inaugural ASMA conference sets a high benchmark, and it is our pleasure to continue this tradition with ASMA 2024, which aims to build upon the success of its predecessor by exploring new frontiers and deepening our understanding of these intricate and ever-evolving disciplines. As an important event for domestic and international academics, ASMA 2024 attracts the attention of researchers and scholars from around the world. Experts and scholars from international institutions such as King’s College London (UK), and University of Jaen (Spain) gathered with scholars from China’s Xiamen University, Guangdong University of Science and Technology, Harbin University of Science and Technology, etc. to share their scientific research results and practical experiences in the domains of applied statistics, modeling and advanced algorithms and their relevant disciplines. In addition, many elites from the business community also participated in the conference enthusiastically, bringing a wealth of practical cases and cutting-edge thinking to the conference. The main agenda of ASMA 2024 is meticulously designed to encompass a broad spectrum of topics, including but not limited to reinforcement learning and fuzzy model-based control, output feedback adaptive boundary control for distributed parameter systems, quadratic estimation for discrete non-Gaussian systems, and secure state estimation using encryption and decryption strategies. These themes are meticulously chosen to reflect the current trends and future directions in the fields of applied statistics, modeling, and advanced algorithms. The conference also features a diverse array of presentations, including keynote speeches, oral presentations, poster sessions, and panel discussions. During these presentations, the participants actively interacted with the speakers and each other, colliding with a lot of new sparks of ideas. In addition to the wonderful academic reports and discussions, this conference also includes a number of high-quality academic contributions, forming a rich collection of papers. These papers cover a wide range of aspects, including statistical mechanics, data mining, partial differential equations, modeling simulation input, numerical analysis, natural computing, etc., with both theoretical discussions and practical analyses, fully demonstrating the latest research dynamics and academic level in these fields. Finally, we would like to express our special thanks to all the participants for their hard work and contributions. It is their active participation and in-depth exchanges that made this conference a complete success. Meanwhile, we would also like to acknowledge the conference organizers for their careful preparations and thoughtful arrangements, which provides a good platform for academic exchanges and cooperation among the participants. The Committee of ASMA 2024 List of Committee Member is available in this Pdf.

Adaptive output feedback fault-tolerant control for a class of nonlinear systems based on a sensor fusion mechanism

This paper investigates an output feedback adaptive fault-tolerant tracking control for a class of nonlinear systems with system nonlinearities, sensor failures and external disturbances, in which sensor redundancy is employed to enhance measurement reliability. A sensor fusion mechanism, together with a novel history-based weighted average algorithm is first designed to fuse all sensor outputs. Then, an adaptive controller based on the sensor fusion output, a dynamic gain and a state observer is constructed to handle all the uncertainties caused by system nonlinearities, external disturbances, sensor failures and fusion mechanism. It is shown that by using the proposed scheme, the closed-loop system is stable, the sensor fusion mechanism can eliminate the effects of faulty sensors, and the real tracking error can be driven into a small compact set mainly affected by the fusion error. Experimental results are accomplished to validate the proposed scheme.

Event-Triggered-Based Adaptive Fuzzy Finite-Time Resilient Output Feedback Control for MIMO Stochastic Nonlinear System Subject to Deception Attacks

Event-Triggered-Based Adaptive Fuzzy Finite-Time Resilient Output Feedback Control for MIMO Stochastic Nonlinear System Subject to Deception Attacks

Steering-Angle Prediction and Controller Design Based on Improved YOLOv5 for Steering-by-Wire System.

A crucial role is played by steering-angle prediction in the control of autonomous vehicles (AVs). It mainly includes the prediction and control of the steering angle. However, the prediction accuracy and calculation efficiency of traditional YOLOv5 are limited. For the control of the steering angle, angular velocity is difficult to measure, and the angle control effect is affected by external disturbances and unknown friction. This paper proposes a lightweight steering angle prediction network model called YOLOv5Ms, based on YOLOv5, aiming to achieve accurate prediction while enhancing computational efficiency. Additionally, an adaptive output feedback control scheme with output constraints based on neural networks is proposed to regulate the predicted steering angle using the YOLOv5Ms algorithm effectively. Firstly, given that most lane-line data sets consist of simulated images and lack diversity, a novel lane data set derived from real roads is manually created to train the proposed network model. To improve real-time accuracy in steering-angle prediction and enhance effectiveness in steering control, we update the bounding box regression loss function with the generalized intersection over union (GIoU) to Shape-IoU_Loss as a better-converging regression loss function for bounding-box improvement. The YOLOv5Ms model achieves a 30.34% reduction in weight storage space while simultaneously improving accuracy by 7.38% compared to the YOLOv5s model. Furthermore, an adaptive output feedback control scheme with output constraints based on neural networks is introduced to regulate the predicted steering angle via YOLOv5Ms effectively. Moreover, utilizing the backstepping control method and introducing the Lyapunov barrier function enables us to design an adaptive neural network output feedback controller with output constraints. Finally, a strict stability analysis based on Lyapunov stability theory ensures the boundedness of all signals within the closed-loop system. Numerical simulations and experiments have shown that the proposed method provides a 39.16% better root mean squared error (RMSE) score than traditional backstepping control, and it achieves good estimation performance for angles, angular velocity, and unknown disturbances.

Flight Control Based on Adaptive Output Feedback for Quadrotors Using Quaternions

Quadrotors are expected to play an active role in out-of-sight areas such as equipment inspection and transportation of supplies to disaster areas. However, ensuring stable and highly accurate automatic flights has become a significant challenge. Trajectory tracking control methods that combine adaptive output feedback control based on almost strictly positive real characteristics and backstepping strategy have been proposed for a quadrotor control system with nonlinearities. These methods have simple structures and are robust. However, existing adaptive control methods have the problem of singularities, owing to the use of Euler angles in the modeling of quadrotors. In this paper, we propose an adaptive control method that enables a wider range of attitude changes using quaternions. The effectiveness of the proposed method is validated through numerical simulations.

Adaptive output feedback control of a bidirectional AC/DC totem‐pole PFC converter for wireless charging of electric vehicles

AbstractThis paper deals with the control problem of a single‐phase bidirectional alternating current/direct current (AC/DC) totem‐pole power factor correction (PFC) converter intended to be used in wireless power transfer (WPT) chargers for electric vehicles (EVs). The converter is operating as a bridgeless boost rectifier with the PFC functionality during grid to vehicle (G2V) mode and as a PFC inverter during vehicle to grid (V2G) mode. Firstly, the operating principle is presented. Secondly, an overall mathematical model governing all the operating modes of the converter is elaborated. Thirdly, due to the nonlinearity of the studied system, a nonlinear controller–based Lyapunov technique is designed to achieve the following control objectives: (i) unity power factor (UPF) during both power transfer modes G2V and V2G, (ii) regulation of the DC‐link voltage, and (iii) asymptotic stability of the closed‐loop system. Further, as the developed control law requires the measurement of all voltages and currents, an adaptive observer–based Kalman‐like technique is designed to estimate all the required signals and parameters. The performances of the proposed output feedback control technique are validated through several simulation scenarios showing that all control objectives are achieved. It should be noted that the use of the Kalman observer reduces the complexity of the systems as it requires only two measurements (grid voltage and current) making it an advantageous solution over existing solutions where three measurements are generally used. Furthermore, due to the low‐pass filtering behavior of the observer and its susceptibility to estimate the converter's parameters, the robustness of the controller and its insensitivity to the parameter uncertainties are greatly improved.

Adaptive Neural Control for Hysteretic Nonlinear Systems With Hysteresis Neural Direct Inverse Compensator and Its Application.

Aiming at high precision control for a class of hysteretic nonlinear systems, a new hysteresis direct inverse compensator-based adaptive output feedback control scheme is designed in this article. First, a novel long short-term memory neural network (LSTMNN)-based hysteresis inverse compensator is established to compensate the asymmetric hysteresis nonlinearity, where the LSTMNN is used as the prediction mechanism for model operator weights, rather than the overall mapping of hysteresis input and output. Second, by designing the modified high-gain K-Filter states observer and the error transformed function, the unmeasurable states are estimated with arbitrarily small estimation error and the prespecified tracking performance is achieved. Lastly, the biconical dielectric elastomer actuator (DEA) motion platform is constructed. Then, the effectiveness of the proposed LSTMNN-based hysteresis inverse compensator and control scheme are verified on the experimental platform. The experimental results illustrate the effectiveness and advantages of proposed control scheme.

Output Feedback Adaptive Optimal Control of Multiple Unmanned Marine Vehicles with Unknown External Disturbance

This paper presents an optimal output-feedback tracking control problem for multiple unmanned marine vehicles (UMVs) to track a desired trajectory. To guarantee the control objective in an optimal manner, adaptive dynamic programming (ADP) with optimal compensation terms is adopted. A neural velocity observer is designed based on a neural network (NN) to estimate the unmeasured system states and the unknown system dynamics. Furthermore, a disturbance observer (DO) is proposed to go against the effect of the unknown external disturbance of the sea environment. It is proved that the proposed controller can guarantee that all signals in the closed-loop system are bounded. Simulation results are given to demonstrate the effectiveness of the proposed control algorithm.

Disturbance observer-based nonsingular fixed-time fuzzy adaptive event-triggered output feedback control of uncertain nonlinear systems

This article examines the fuzzy adaptive event-triggered output feedback control issue of eliminating fixed-time singularity for a type of uncertain nonlinear systems with disturbance observer structures. By introducing novel bounded time-varying functions (TVFs), a set of more general intermediate-variable-based disturbance observers (IVBDOs) is constructed. Combining the corresponding auxiliary reduced-power function and the quartic Lyapunov method, a co-design scheme for a fuzzy approximation high-order controller and an improved event-triggered mechanism with a judgment indicator threshold is proposed. Especially, the developed framework not only removes the singularity during the fixed-time design process but also further reduces the communication resources. By means of theoretical analysis, the devised solution achieves fixed-time stable control (FTSC) of the system and the tracking error converges to the desired neighborhood around the origin. After verifying the absence of Zeno behavior, the efficacy of the presented methodology is supported by two simulation examples.

Sufficient conditions on input-to-stabilisation of time-varying semi-linear systems with disturbances in Hilbert spaces

In this paper, we investigate the problem of input-to-state stabilisation of time-varying semi-linear systems with disturbance in Hilbert spaces. An original adaptive input-to-state stabilisation strategy to derive this exceptional problem under the nonlinearities modelling is proposed using the Lyapunov method. Since all the states are not measurable, a Luenberger observer is designed to estimate unmeasurable states for input-to-state stabilisation. Thus, in the design procedure, first, a Luenberger observer is designed, and then the adaptive output feedback controller is constructed via the estimated states and adaptation law. Moreover, we give a sufficient condition, in terms of a perturbation term, to guarantee the input-to-state stability of the time-varying semi-linear systems under a stabilising nonlinear feedback law. An example of a controlled reaction-diffusion equation is given.

Neural Adaptive Intermittent Output Feedback Control for Autonomous Underwater Vehicles With Full-State Quantitative Designs.

In this article, a neural adaptive intermittent output feedback control is investigated for autonomous underwater vehicles (AUVs) with full-state quantitative designs (FSQDs). To achieve the prespecified tracking performance determined by quantitative indices (e.g., overshoot, convergence time, steady-state accuracy, and maximum deviation) at both kinematic and kinetic levels, FSQDs are designed by transforming constrained AUV model into an unconstrained model via one-sided hyperbolic cosecant boundaries and nonlinear mapping functions. An intermittent sampling-based neural estimator (ISNE) is devised to reconstruct the matched and mismatched lumped disturbances as well as immeasurable velocity states of transformed AUV model, where only system outputs after intermittent sampling are required. Using the estimations of ISNE and the system outputs after triggering, an intermittent output feedback control law incorporated with hybrid threshold event-triggered mechanism (HTETM) is designed to achieve ultimately uniformly bounded (UUB) results. Simulation results are provided and analyzed to validate the effectiveness of the studied control strategy with application to an omnidirectional intelligent navigator (ODIN).

An output-feedback stabilisation scheme with exponential convergence for nonlinear systems with unknown polynomial-of-output growth rate

ABSTRACT This paper seeks adaptive output-feedback control which enables exponential stabilisation for nonlinear systems with unknown polynomial-of-output growth rate and dynamic uncertainties. The existence of uncertainties naturally requires the introduction of adaptive compensation mechanisms, while, convergence rate, as an important performance specification, is usually hard to attain in the context of adaptive control. For this, we are compelled to pursue a comprehensive and competent adaptive compensation mechanism which possesses dual capabilities of compensating uncertainties and affording convergence rate. Crucially, a subtle dynamic gain is designed with its dynamic updating law incorporating exponential-type time-varying information. Notably, the gain would not only deal with the unknown polynomial-of-output growth rate but particularly ensure exponential convergence with the convergence rate online adjusting in conformity to the dynamic uncertainties. Correspondingly, an adaptive output-feedback controller is constructed based on a dynamic-gain observer, which can render exponential convergence for system and observer states.