Search Results (5)

This research proposes a fractional-order adaptive neural control scheme using an optimized backstepping (OB) approach to address strict-feedback nonlinear systems with uncertain control directions and predefined performance requirements. The OB framework integrates both fractional-order virtual and actual controllers to achieve global optimization, while a Nussbaum-type function is introduced to handle unknown control paths. To ensure convergence to desired accuracy within a prescribed time, a fractional-order dynamic-switching mechanism and a quartic-barrier Lyapunov function are employed. An input-to-state practically stable (ISpS) auxiliary signal is designed to mitigate unmodeled dynamics, leveraging classical lemmas adapted to fractional-order systems. The study further investigates a decentralized control scenario for large-scale stochastic nonlinear systems with uncertain dynamics, undefined control directions, and unmeasurable states. Fuzzy logic systems are employed to approximate unknown nonlinearities, while a fuzzy-phase observer is designed to estimate inaccessible states. The use of Nussbaum-type functions in decentralized architectures addresses uncertainties in control directions. A key novelty of this work lies in the combination of fractional-order adaptive control, fuzzy logic estimation, and Nussbaum-based decentralized backstepping to guarantee that all closed-loop signals remain bounded in probability. The proposed method ensures that system outputs converge to a small neighborhood around the origin, even under stochastic disturbances. The simulation results confirm the effectiveness and robustness of the proposed control strategy. Full article

In this paper, the velocity and altitude control problem of hypersonic vehicles is studied. Aiming at the nonlinear parameter uncertainties, external disturbances and coupling of the hypersonic vehicle system, a control method combining backstepping control with linear active disturbance rejection control is proposed. The backstepping control solves the coupling of the system and transforms the longitudinal dynamic model of the hypersonic vehicle into the form of strict feedback, which is divided into the altitude subsystem and velocity subsystem. The linear extension state observer (LESO) can observe parameter uncertainty disturbance and external disturbance. At the same time, the stability of the system is proved by Lyapunov theory. Finally, the effectiveness of the designed controller is verified by numerical simulation and comparison with classical PID control. Full article

Due to the widespread presence of disturbances in practical engineering and widespread applications of high-order systems, this paper first pays attention to a class of high-order strict-feedback nonlinear systems subject to bounded disturbance and investigates the prescribed performance tracking control and anti-disturbance control problems. A novel composite control protocol using the technique of a disturbance observer—prescribed performance control—is designed using the back-stepping method. The disturbance observer is introduced for estimating and compensating for unknown disturbances in each step, and the prescribed performance specifications guarantee both transient and steady-state performance of the tracking error to improve the control performance and result in better disturbance rejection. Moreover, the technique of adding a power integrator is modified to tackle controller design problems for the high-order systems. The Lyapunov function method is utilized for rigorous stability analysis. It is revealed that while the control performance completely remains in the prescribed bound, all states in the closed-loop system are input-to-state stable, and the tracking error and the disturbances estimating error asymptotically converge to zero simultaneously. Then, the feasibility and effectiveness of the proposed control protocol are verified by a simulation result. Full article

An integrated guidance and control (IGC) scheme considering the field-of-view (FOV) constraint is proposed in this paper for hypersonic skid-to-turn (STT) missiles with a strapdown seeker intercepting a high-speed maneuvering target, which is based on the backstepping control (BC), barrier Lyapunov function (BLF), sliding mode control (SMC), dynamic surface control (DSC), and reduced-order extended state observer (ESO). First, a fifth-order strict feedback IGC model considering the rudder delay dynamics is derived, which also considers the drag effect on the axial velocity. Second, the missile guidance control system based on the BC consists of seeker, guidance, angle-of-attack, attitude, and rudder subsystems. The seeker subsystem was designed based on the BLF, and the other four subsystems were designed based on the SMC. The system-lumped disturbances, including unknown target maneuvers, unmodeled parts, perturbations caused by aerodynamic parameter variations, and external disturbances, were estimated and compensated for using the reduced-order ESO. The DSC prevented the “differential explosion” caused by virtual control commands introduced by the BC. Subsequently, the stability of the closed-loop system was strictly proven using the Lyapunov theory, and the boundedness of the FOV angle was strictly derived. Finally, the simulation results demonstrated the effectiveness and robustness of the proposed IGC scheme. Full article

Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and motor speeds. The proposed method has three main steps. In the first step, the strict-feedback backstepping digital twin is designed for acoustic emission signal modeling and estimation. After that, the acoustic emission residual signal is generated. Finally, a support vector machine is recommended for crack type/size classification. The proposed digital twin is presented in two steps, (a) AE signal modeling and (b) AE signal estimation. The AE signal in normal conditions is modeled using an autoregressive technique, the Laguerre algorithm, a support vector regression technique and a Gaussian process regression procedure. To design the proposed digital twin, a strict-feedback backstepping observer, an integral term, a support vector regression and a fuzzy logic algorithm are suggested for AE signal estimation. The Ulsan Industrial Artificial Intelligence (UIAI) Lab’s bearing dataset was used to test the efficiency of the combined strict-feedback backstepping digital twin and machine learning technique for bearing crack type/size diagnosis. The average accuracies of the crack type diagnosis and crack size diagnosis of acoustic emission signals for the bearings used in the proposed algorithm were 97.13% and 96.9%, respectively. Full article