Neural-approximation-based robust adaptive control of flexible air-breathing hypersonic vehicles with parametric uncertainties and control input constraints

Hypersonic tracking control under actuator saturations via readjusting prescribed performance functions

Prescribed performance control (PPC) has been shown to be an effective tool in pursuing prescribed transient and steady-state specifications. Unfortunately, the existing PPC is incapable of handling the peaking of errors caused by actuator saturations, which is due to the short of the ability of readjusting the prescribed performance functions. In this article, we propose a novel PPC scheme, namely the readjusting-performance-function-based approach, for hypersonic flight vehicles subject to actuator saturations. A new sort of performance functions containing readjusting terms are developed to impose prescribed constraints on the velocity tracking error and the altitude tracking error. More specially, the prescribed performance functions can be adaptively readjusted to guarantee that tracking errors are always within them. This eliminates the singular problem that is usually encountered by traditional PPC. To deal with the actuator saturation problem, a novel compensated system (CS) is exploited for the velocity dynamics. Then, the CS is further extended to the altitude subsystem by reforming it as a high-order formulation. Besides the aforementioned baseline controllers, optimal control protocols are also addressed based on adaptive dynamic programming. Finally, comparison simulation results are given to verify the advantages.

Nonlinear control of a class of non-affine variable-speed variable-pitch wind turbines with radial-basis function neural networks

Due to complicated dynamics, wind turbines' governing equations are subject to uncertainties and unknown disturbance sources. Despite uncertainties and disturbance sources, the paper's focus is to design an adaptive controller that enables trajectory-tracking with a zero-converging tracking error. As the main result of a zero tracking error, the turbine can operate at maximum power efficiency. In addition, novel Lyapunov functions are proposed introducing auxiliary adaptive terms to result in closed-loop asymptotic stability in the presence of a non-affine controller input, uncertainties, and unknown disturbance sources. Considering the turbine dynamics, one can divide the wind turbine control problem into torque and pitch control phases. For addressing the nonlinearities and uncertainties of the dynamics in each phase, RBF neural networks are utilized to develop novel control and adaptive laws. To address the non-affine dynamics stemming from the pitch angle, a neural network alongside the implicit function and mean-value theorems are utilized to transform the dynamics into the control affine form. Several auxiliary adaptive variables are proposed in the transformation procedure, leading to closed-loop asymptotic stability. Moreover, using the Lyapunov stability analysis, closed-loop asymptotic stability is obtained for each phase. In the end, simulation results are presented to verify the analytical results where the proposed controller's performance is compared to that of an existing method in different scenarios. The proposed controller's simulation results suggest dramatic improvement over those of the existing method in both trajectory-tracking and required control action.

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.

Fuzzy adaptive fault diagnosis and compensation for variable structure hypersonic vehicle with multiple faults

Based on the type-II fuzzy logic, this paper proposes a robust adaptive fault diagnosis and fault-tolerant control (FTC) scheme for multisensor faults in the variable structure hypersonic vehicles with parameter uncertainties. Type-II fuzzy method approximates the original models while eliminating the parameter uncertainties. Hence the sensor faults are detected and isolated by the multiple output residuals and thresholds considering nonlinear approximation errors and disturbance. Based on the fuzzy adaptive augmented observer, the faults and disturbance are all estimated accurately by an improved proportional-differential part. Then a variable structure FTC scheme repairs the faults by the estimation, the fast-varying disturbance is considered in FTC scheme and is compensated by the control parameters designed based on its derivative function, thereby enhancing the output robust tracking accuracy of the variable structure hypersonic vehicles. The Lyapunov theory proves the system robust stability, semi-physical simulation verifies the validity of the proposed method and the superiority compared with the traditional method.

Novel anti-saturation robust controller for flexible air-breathing hypersonic vehicle with actuator constraints

A novel anti-saturation robust control algorithm (NARC) is presented for flexible air-breathing hypersonic vehicle (FAHV) with actuator saturation, including two controllers designed for velocity and height subsystem respectively. Firstly, an anti-saturation finite-time dynamic inversion controller is designed for velocity subsystem, in which an anti-saturation fixed-time compensator (ASFC) is proposed to ensure the stability of saturated system and make it exit saturated region faster. Compared with conventional anti-saturation compensator, the auxiliary variable of ASFC can converge with faster speed and higher precision when actuator is not saturated, which avoids the impact on original system. Secondly, an anti-saturation robust command filtered backstepping controller is designed for height subsystem, combining backstepping control, ASFC and a novel fixed-time filter (FTF). Compared with low pass filter, the FTF proposed can track input signal with faster response speed and higher precision without the need to select a smaller time constant, so as to avoid introducing high-frequency noise. Meanwhile, convergence domain of height subsystem can be reduced as well. Ultimately, simulations on FAHV with actuator constraints, parametric uncertainties and external disturbances are performed using the NARC and conventional anti-saturation controller respectively to demonstrate the superiority of NARC.

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.

Extended state observer based control for generic hypersonic vehicles with nonaffine-in-control character

This paper investigates the flight control problem of generic hypersonic vehicles subject to nonaffine-in-control character. Considering the large uncertainties and external disturbance, the disturbance observer based control strategy is incorporated in the control scheme. Firstly, an extended state observer is used to estimate the system states and the total disturbance. Then, based on the output of the extended state observer, we follow the backstepping design procedure. The dynamic inversion method is involved in the last step of backstepping to solve the nonaffine-in-control problem. The proposed control scheme ensures that the hypersonic vehicle tracks the command signal with almost no aerodynamic knowledge. Rigorous stability proof is given based on the separated time-scale structure of the extended state observer and the dynamic inversion method. At last, numerical simulations are presented in different conditions to demonstrate the effectiveness and good tracking performance of the proposed control scheme.

Tracking control of air-breathing hypersonic vehicles with non-affine dynamics via improved neural back-stepping design

This study considers the design of a new back-stepping control approach for air-breathing hypersonic vehicle (AHV) non-affine models via neural approximation. The AHV's non-affine dynamics is decomposed into velocity subsystem and altitude subsystem to be controlled separately, and robust adaptive tracking control laws are developed using improved back-stepping designs. Neural networks are applied to estimate the unknown non-affine dynamics, which guarantees the addressed controllers with satisfactory robustness against uncertainties. In comparison with the existing control methodologies, the special contributions are that the non-affine issue is handled by constructing two low-pass filters based on model transformations, and virtual controllers are treated as intermediate variables such that they aren't needed for back-stepping designs any more. Lyapunov techniques are employed to show the uniformly ultimately boundedness of all closed-loop signals. Finally, simulation results are presented to verify the tracking performance and superiorities of the investigated control strategy.

Adaptive fuzzy control of a class of nonaffine nonlinear system with input saturation based on passivity theorem

In this paper, based on the passivity theorem, an adaptive fuzzy controller is designed for a class of unknown nonaffine nonlinear systems with arbitrary relative degree and saturation input nonlinearity to track the desired trajectory. The system equations are in normal form and its unforced dynamic may be unstable. As relative degree one is a structural obstacle in system passivation approach, in this paper, backstepping method is used to circumvent this obstacle and passivate the system step by step. Because of the existence of uncertainty and disturbance in the system, exact passivation and reference tracking cannot be tackled, so the approximate passivation or passivation with respect to a set is obtained to hold the tracking error in a neighborhood around zero. Furthermore, in order to overcome the non-smoothness of the saturation input nonlinearity, a parametric smooth nonlinear function with arbitrary approximation error is used to approximate the input saturation. Finally, the simulation results for the theoretical and practical examples are given to validate the proposed controller.