Minimal-learning-parameter based simplified adaptive neural back-stepping control of flexible air-breathing hypersonic vehicles without virtual controllers

Neural-Network-Based Constrained Output-Feedback Control for MEMS Gyroscopes Considering Scarce Transmission Bandwidth

In this article, a neural-network-based constrained output-feedback control is considered for microelectromechanical system (MEMS) gyroscopes subject to scarce transmission bandwidth and lumped disturbances resulting from model uncertainties, dynamic coupling, and environmental disturbances. First, a hybrid quantizer capable of achieving an adjustable communication rate and quantization density is proposed to convert continuous control signals into discrete values, allowing for reduced chattering behavior even when control actions vary within large regions and enhanced tracking accuracy can be ensured. Subsequently, by applying two types of nonlinear mapping, all state variables of MEMS gyroscopes are restrained within the predefined time-varying asymmetric functions without imposing stringent feasibility conditions on virtual control laws. Furthermore, an echo-state network-based minimal learning parameter neural observer is developed to simultaneously recover the unmeasurable velocity-state variables, matched as well as unmatched disturbances in constrained MEMS gyroscopes dynamics, enabling an output-feedback control solution with a decreased online learning complexity. It is shown via the Lyapunov stability and nonsmooth analysis that all signals in the closed-loop system remain ultimately uniformly bounded even with discontinuous control actions. Comparison simulations are produced to certify the effectiveness of the presented controller.

Adaptive Sliding Mode Control via Backstepping for an Air-Breathing Hypersonic Vehicle Using a Double Power Reaching Law

This paper presents a backstepping-based adaptive sliding mode control scheme using a new double power reaching law for an air-breathing hypersonic vehicle (AHV) with uncertainties. A novel double power reaching law is proposed to speed up the state stabilization. A backstepping control scheme is proposed for a class of high-order nonlinear system with uncertainties. Then, a novel sliding mode controller using the new double power reaching law is developed to maintain the high tracking performance of the AHV. In order to further attenuate the influence of uncertainties, new adaptive laws are employed. Lastly, simulation studies show that the novel double power reaching law can guarantee that the state of the system converges to zero equilibrium in fixed time, and the controller proposed can effectively reduce the influence of uncertainties on the AHV and achieve good tracking performance.

Finite-time controller design with adaptive fixed-time anti-saturation compensator for hypersonic vehicle

An adaptive anti-saturation robust finite-time control algorithm (AARFTC) is designed for flexible air-breathing hypersonic vehicle (FAHV) under actuator saturations. Firstly, an adaptive fixed-time anti-saturation compensator (AFAC) is presented to drive system to faster leave the saturated region Compared to traditional anti-saturation compensators, the auxiliary variable of AFAC is able to realize faster and more accurate convergence when saturation disappears, which avoids the influence on convergent characteristics of tracking error. In addition, the novel adaptive law in AFAC can further shorten the duration of saturation and improve the convergent speed of tracking error via adjusting gain in AFAC according to saturation of actuator. Then, dynamic inversion control is combined with AFAC to establish anti-saturation controller for velocity subsystem. Secondly, differentiator-based backstepping control is combined with AFAC for height subsystem. Two recursive fixed settling time differentiators are utilized to approximate derivatives of virtual control signals exactly in fixed time, which avoids the complex computational burden residing in traditional backstepping control and improves convergent accuracy compared to command filtered backstepping control. Meanwhile, AFAC is utilized to suppress the influence of elevator saturation. Ultimately, multiple sets of simulations on FAHV subject to external disturbances, parametric uncertainties and actuator saturations are carried out to show the superiorities of AFAC and AARFTC.

Robust Adaptive Tracking Control for Hypersonic Vehicle Based on Interval Type-2 Fuzzy Logic System and Small-Gain Approach

This paper presents a novel robust adaptive tracking control method for a hypersonic vehicle in a cruise flight stage based on interval type-2 fuzzy-logic system (IT2-FLS) and small-gain approach. After the input-output linearization, the vehicle model can be decomposed into two uncertain subsystems by considering matching disturbances and parametric uncertainties. For each subsystem, an interval type-2 Takagi-Sugeno-Kang fuzzy logic system (IT2-TSK-FLS) is then employed to approximate the unavailable model information. Following the idea of a small-gain approach, a composite feedback form for each subsystem is constructed, based on which the final robust adaptive tracking control law is developed. Rigorous stability analysis shows that all signals in the derived closed-loop system are kept uniformly ultimately bounded (UUB). The main contribution of this paper is that the proposed control law for the hypersonic vehicle is with only two adaptive parameters in total which can greatly alleviate the computation and storage burden in practice; meanwhile its superiority over the conventional minimal-learning-parameter (MLP)-based one is specifically illustrated. Comparative numerical simulations of three cases demonstrate the effectiveness of our proposed control method with respect to complicated uncertainties.

Model Identification and Trajectory Tracking Control for Vector Propulsion Unmanned Surface Vehicles

To promote the development of military and civilian applications for marine technology, more and more scientific research around the world has begun to develop unmanned surface vehicles (USVs) technology with advanced control capabilities. This paper establishes and identifies the model of vector propulsion USV, which is widely used at present. After analyzing its actuator distribution, we consider that the more realistic vessel model should be an incomplete underactuated system. For this system, a virtual control point method is adopted and an adaptive sliding mode trajectory tracking controller with neural network minimum learning parameter (NNMLP) theory is designed. Finally, in the simulation experiment, the thruster speed and propulsion angle are used as the inputs of the controller, and the linear and circular trajectory tracking tests are carried out considering the delay effect of the actuator, system uncertainty, and external disturbance. The results show that the proposed tracking control framework is reasonable.

Robust fixed-time sliding mode controller for flexible air-breathing hypersonic vehicle

An improved robust fixed-time sliding mode controller (RFSMC) is presented for flexible air-breathing hypersonic vehicle (FAHV) with actuator faults, composing of a novel fast fixed-time integral sliding surface (FFIS), a continuous fixed-time super-twisting-like reaching law (CFSTL) and a uniformly convergent observer. Firstly, the nonlinear control-oriented model of FAHV is processed via input/output feedback linearization with flexible effects and actuator faults modeling as matched Lipschitz disturbances. Secondly, a novel non-singular FFIS is established based on a fast fixed-time high-order regulator (FFTR), which is improved with two gains incorporating into standard fixed-time high-order regulator via dilation rescaling. The FFTR proposed can accelerate respond speed of system by tuning values of two gains simply without complicated parameters selection and the stability is proved strictly via Lyapunov criteria. Thirdly, a CFSTL is utilized to ensure high-precision convergence of sliding mode vector and its derivative in fixed time. Afterwards, a uniformly convergent observer is applied to estimate lumped disturbances accurately in fixed time. With the estimated values compensated into controller, RFSMC can enhance fault-tolerant performance and attenuate chattering efficiently. Finally, simulations on FAHV are performed to verify the effectiveness and superiority of the method proposed.

Finite-time predictor line-of-sight–based adaptive neural network path following for unmanned surface vessels with unknown dynamics and input saturation

In the presence of unknown dynamics and input saturation, a finite-time predictor line-of-sight–based adaptive neural network scheme is presented for the path following of unmanned surface vessels. The proposed scheme merges with the guidance and the control subsystem of unmanned surface vessels together. A finite-time predictor–based line-of-sight guidance law is developed to ensure unmanned surface vessels effectively converging to and following the referenced path. Then, the path-following control laws are designed by combining neural network-based minimal learning parameter technique with backstepping method, where minimal learning parameter is applied to account for system nonparametric uncertainties. The key features of this scheme, first, the finite-time predictor errors are guaranteed; second, designed controllers are independent of the system model; and third, only required two parameters update online for each control law. The rigorous theory analysis verifies that all signals in the path-following guidance-control system are semi-globally uniformly ultimately bounded via Lyapunov stability theory. Simulation results illustrate the effectiveness and performance of the presented scheme.

Neural network–based nonaffine control of air-breathing hypersonic vehicles with prescribed performance

This article investigates a novel nonaffine control strategy using neural networks for an air-breathing hypersonic vehicle. Actual actuators are regarded as additional state variables and virtual control inputs are derived from low-computational cost neural approximations, while a new altitude control design independent of affine models is addressed for air-breathing hypersonic vehicles. To further reduce the computational load, an advanced regulation algorithm is applied to devise adaptive laws for neural estimations. Moreover, a new prescribed performance mechanism is exploited, which imposes preselected bounds on the transient and steady-state tracking error performance via developing new performance functions, capable of guaranteeing altitude and velocity tracking errors with small overshoots. Unlike some existing neural control methodologies, the proposed prescribed performance-based nonaffine control approach can ensure tracking errors with preselected transient and steady-state performance. Meanwhile, the complex design procedure of backstepping is also avoided. Finally, simulation results are presented to validate the design.