A5053178444- Adaptive Control of Nonlinear Systems
- Guidance and Control Systems
- Adaptive Dynamic Programming Control
- Advanced Control Systems Optimization
- Iterative Learning Control Systems
- Stability and Control of Uncertain Systems
- Fault Detection and Control Systems
- Distributed Control Multi-Agent Systems
- Inertial Sensor and Navigation
- Geophysics and Sensor Technology
- Advanced Control Systems Design
- Robotic Path Planning Algorithms
- Space Satellite Systems and Control
- Structural Health Monitoring Techniques
- Advanced Sensor and Control Systems
- Hydraulic and Pneumatic Systems
- Underwater Vehicles and Communication Systems
- Topology Optimization in Engineering
- Aerospace and Aviation Technology
- Control and Dynamics of Mobile Robots
- Machine Learning and ELM
- Computational Fluid Dynamics and Aerodynamics
- Neural Networks and Applications
- Anomaly Detection Techniques and Applications
- Video Surveillance and Tracking Methods
Northwestern Polytechnical University
2016-2025
East China Normal University
2024
Yunnan University
2024
Tianjin University of Science and Technology
2024
Nanjing University of Aeronautics and Astronautics
2008-2022
Murdoch University
2022
Southeast University
2022
Harry Butler Institute
2022
Zhejiang University of Technology
2022
Tsinghua University
2006-2021
For parameter identifications of robot systems, most existing works have focused on the estimation veracity, but few literature are concerned with convergence speed. In this paper, we developed a control/identification scheme to identify unknown kinematic and dynamic parameters enhanced rate. Superior traditional methods, information error was properly integrated into proposed identification algorithm, such that performance achieved. Besides, Newton-Euler (NE) method used build model, where...
This paper studies the composite adaptive tracking control for a class of uncertain nonlinear systems in strict-feedback form. Dynamic surface technique is incorporated into radial-basis-function neural networks (NNs)-based framework to eliminate problem explosion complexity. To avoid analytic computation, command filter employed produce signals and their derivatives. Different from directly toward asymptotic tracking, accuracy identified models taken consideration. The prediction error...
This paper studies both indirect and direct global neural control of strict-feedback systems in the presence unknown dynamics, using dynamic surface (DSC) technique a novel manner. A new switching mechanism is designed to combine an adaptive controller approximation domain, together with robust that pulls transient states back into domain from outside. In comparison conventional techniques, which could only achieve semiglobally uniformly ultimately bounded stability, proposed scheme...
Abstract In this paper, the robust adaptive controller is investigated for longitudinal dynamics of a generic hypersonic flight vehicle. The proposed methodology addresses issue design and stability analysis with respect to parametric model uncertainty input saturations control‐oriented model. velocity attitude subsystems are transformed into linearly parameterized form. Based on parameter projection estimation, dynamic inverse control via back‐stepping scheme. order avoid problem “explosion...
This paper investigates the disturbance observer (DOB)-based neural adaptive control on longitudinal dynamics of a flexible hypersonic flight vehicle (HFV) in presence wind effects. The coupling effect between states and rigid body, accessional angle attack (AOA) due to wind, is modeled as unknown disturbance, where nonlinear DOB constructed using approximation. For weight update networks (NNs), novel algorithm proposed with additional prediction error derived from serial-parallel estimation...
This paper investigates a fault-tolerant control of the hypersonic flight vehicle using back-stepping and composite learning. With consideration angle attack (AOA) constraint caused by scramjet, laws are designed based on barrier Lyapunov function. To deal with unknown actuator faults, robust adaptive allocation law is proposed to provide compensation. Meanwhile, obtain good system uncertainty approximation, learning for update neural weights constructing serial–parallel estimation model...
This paper addresses the dynamic surface control of uncertain nonlinear systems on basis composite intelligent learning and disturbance observer in presence unknown system nonlinearity time-varying disturbance. The serial-parallel estimation model with approximation is built to obtain prediction error this way law for weights updating constructed. developed using information while guaranteed converge a bounded compact set. highlight that different from previous work directly toward...
This paper investigates the disturbance observer-based composite fuzzy control of a class uncertain nonlinear systems with unknown dead zone. With logic system approximating nonlinearities, learning is constructed on basis serial–parallel identifier. By introducing intermediate signal, observer developed to provide efficient compounded which includes effect time-varying disturbance, approximation error, and Based estimation approximation, adaptive controller synthesized novel updating law....
Reducing interface nonradiative recombination is important for realizing highly efficient perovskite solar cells. In this work, we develop a synergistic bimolecular interlayer (SBI) strategy via 4-methoxyphenylphosphonic acid (MPA) and 2-phenylethylammonium iodide (PEAI) to functionalize the interface. MPA induces an in-situ chemical reaction at surface forming strong P-O-Pb covalent bonds that diminish defect density upshift Fermi level. PEAI further creates additional negative dipole so...
Linear discriminant analysis (LDA) is well known as a powerful tool for analysis. In the case of small training data set, however, it cannot directly be applied to high-dimensional data. This so-called small-sample-size or undersampled problem. this paper, we propose an exponential (EDA) technique overcome The advantages EDA are that, compared with principal component (PCA) + LDA, method can extract most information that was contained in null space within-class scatter matrix, and another...
Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher order statistics of data and offer potentially significant performance improvement over their linear counterparts especially non Gaussian signal processing machine learning. In this paper, we propose a new measure called the risk-sensitive loss (KRSL), provide some important properties. We apply KRSL to adaptive filtering investigate robustness, then develop MKRSL algorithm analyze mean square...
In this article, the adaptive neural controller in discrete time is investigated for longitudinal dynamics of a generic hypersonic flight vehicle. The are decomposed into altitude subsystem and velocity subsystem. transformed strict-feedback form from which discrete-time model derived by first-order Taylor expansion. virtual control designed with nominal feedback network (NN) approximation via back-stepping. Meanwhile, one NN To avoid circular construction problem practical control, design...
This paper investigates the dynamic surface control of nonlinear transport aircraft model during process continuous heavy cargo airdrop in case disturbance and actuator saturation. For process, effects moving parameters include mass items upon flight dynamics is with dramatic change. The related technique designed for attitude subsystem unmatched unknown so that each step virtual carefully considered using observer while auxiliary signal constructed closed-loop stability established via...
This paper addresses two composite learning controller designs of quadrotor dynamics with unknown and time-varying disturbances using the terminal sliding mode. For system dynamics, single-hidden-layer feedforward network is employed for approximation which provides information disturbance observer. Based on neural estimation, mode control (TSMC) synthesized to obtain finite-time convergence performance. To overcome singularity problem, nonsingular TSMC proposed. The closed-loop stability...
The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilising derivative-free higher-order approximation by approximating Gaussian distribution rather than function. Applying UT Kalman filter type estimator leads well-known (UKF). Although UKF works very well in noises, its performance may deteriorate significantly when noises are non-Gaussian, especially system disturbed some heavy-tailed impulsive noises. To...
This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using backstepping framework. In each step virtual design, network (NN) is employed for uncertainty approximation. previous works, most designs are directly toward system stability ignoring fact how NN working as an approximator. this paper, to enhance learning ability, a novel prediction error signal constructed provide additional correction information weight update data....
This paper studies the compound learning control of disturbed uncertain strict-feedback systems. The design is using dynamic surface equipped with a novel scheme. integrates recently developed online recorded data-based neural nonlinear disturbance observer (DOB) to achieve good "understanding" system uncertainty including unknown dynamics and time-varying disturbance. With proposed method show how networks DOB are cooperating each other, one indicator constructed included into update law....
Summary In this paper, the adaptive back‐stepping controller is investigated for a class of strict‐feedback systems using command filter technique. Adaptive laws are designed updating parameters when both plant and actuator‐failure unknown. Furthermore, auxiliary dynamics developed to deal with input constraints. Closed‐loop stability asymptotic‐state tracking ensured. The method applied longitudinal generic hypersonic aircraft in presence actuator faults Based on parameter estimation,...
This paper investigates the singular perturbation (SP) theory-based composite learning control of a flexible-link manipulator using neural networks (NNs) and disturbance observer (DOB). For dynamics, system states are separated into fast slow variables in terms time scale. multi-input-multi-output intelligent is designed where NNs used for uncertainty approximation DOB compound estimation. The main contribution that novel controller NN constructed to deal with unknown dynamics time-varying...
The actuator failure compensation control problem of robotic systems possessing dynamic uncertainties has been investigated in this paper. Control design against partial loss effectiveness (PLOE) and total (TLOE) the are considered described, respectively, a disturbance observer (DO) using neural networks is constructed to attenuate influence unknown disturbance. Regarding prescribed error bounds as time-varying constraints, method based on barrier Lyapunov function (BLF) used strictly...