Nonlinear Constrained Adaptive Backstepping Tracking Control for a Hypersonic Vehicle with Uncertainty

Adaptive control for hypersonic vehicle with input saturation and state constraints

Robust adaptive integral backstepping control for opto-electronic tracking system based on modified LuGre friction model

A novel low-complexity adaptive control method, capable of guaranteeing the transient and steady-state tracking performance in the presence of unknown nonlinearities and actuator saturation, is investigated for the longitudinal dynamics of a generic hypersonic flight vehicle. In order to attenuate the negative effects of classical predefined performance function for unknown initial tracking errors, a modified predefined performance function with time-varying design parameters is presented. Under the newly developed predefined performance function, two novel adaptive controllers with low-complexity computation are proposed for velocity and altitude subsystems of the hypersonic flight vehicle, respectively. Wherein, different from neural network-based approximation, a least square support vector machine with only two design parameters is utilized to approximate the unknown hypersonic dynamics. And the relevant ideal weights are obtained by solving a linear system without resorting to specialized optimization algorithms. Based on the approximation by least square support vector machine, only two adaptive scalars are required to be updated online in the parameter projection method. Besides, a new finite-time-convergent differentiator, with a quite simple structure, is proposed to estimate the unknown generated state variables in the newly established normal output-feedback formulation of altitude subsystem. Moreover, it is also employed to obtain accurate estimations for the derivatives of virtual controllers in a recursive design. This avoids the inherent drawback of backstepping — “explosion of terms” and makes the proposed control method achievable for the hypersonic flight vehicle. Further, the compensation design is employed when the saturations of the actuator occur. Finally, the numerical simulations validate the efficiency of the proposed finite-time-convergent differentiator and control method.

In the paper, a nonlinear adaptive speed controller for a permanent magnet synchronous motor based on a newly developed adaptive backstepping approach (PMSM) is presented. The exact input-output feedback linearisation control law is first introduced without any uncertainties in the system. However, in real applications, the parameter uncertainties such as the stator resistance and viscous constant and load torque disturbance have to be considered. In this case, the exact input-output feedback linearisation approach is not very effective, because it is based on the exact cancellation of the nonlinearity. To compensate the uncertainties and the load torque disturbance, the input-output feedback linearisation approach is first used to compensate the nonlinearities in the nominal system. Then, a nonlinear adaptive backstepping control law and parameter uncertainty, and load torque disturbance adaptation laws, are derived systematically by using adaptive backstepping technique. Simulation and experimental results clearly show that the proposed adaptive scheme can track the speed reference signal generated by a reference model, successfully, and that the scheme is robust to the parameter uncertainties and load torque disturbance.

The article investigates the discrete-time controller for the longitudinal dynamics of the hypersonic flight vehicle. Based on the analysis of the control inputs, the dynamics model can be decomposed into the altitude subsystem and the velocity subsystem. Using the first-order Taylor expansion, the altitude subsystem can be transformed into discrete-time model, and then the strict-feedback form can be obtained. The controller is designed via back-stepping method. During this progress, neural networks are employed to approximate the mismatched uncertainties. Neural networks are used on the denominator of the controller as well as on the numerator of the controller to approximate the whole uncertainty (including the nominal value). The dual neural network controller via back-stepping is able to track system instructions accurately. Stability analysis proves that the errors of all the signals in the system are of uniform ultimate bound-ness. The simulation results show the effectiveness of the proposed controller.

Globally stable direct adaptive backstepping NN control for uncertain nonlinear strict-feedback systems

Based on non-affine models of hypersonic space vehicles, the tracking control problem of hypersonic vehicles is studied and analyzed in this article using funnel robust model-free control mechanism considering parametric uncertainty and external disturbances. First, the control system is decomposed into altitude subsystem and velocity subsystem. For altitude subsystem, we propose a concise funnel robust model-free control mechanism based on error driving, and a novel model transformation approach is applied to the controller design. The new model-free controller only contains a Hurwitz stable term and a filtering term, and does not need precise motion model and too much calculation, so it can improve the calculation speed of the system. For velocity subsystem, only a concise proportional-integral controller is needed to meet the tracking requirements. Moreover, the devised controller is capable of guaranteeing funnel performance on the altitude and velocity tracking errors. Finally, numerical simulation results are presented to verify the efficiency of the design.

This paper design an output tracking controller for a class of uncertain nonlinear systems to address the input quantization and output tracking funnel control problem. By the traditional adaptive control and backstepping technique, an adaptive quantized funnel control scheme is designed. It is shown that the presented controller can ensure that arbitrary closed-loop signals are globally uniformly bounded, and the output tracking error is bounded, and held in the prescribed funnel performance functions. The presented control scheme is checked through the simulation result.

The development of a nonlinear adaptive controller with reconfiguration capabilities for a generic 6-DOF model of an air-breathing hypersonic vehicle is considered in this paper. The proposed controller is endowed with a modular structure (comprised of an inner-loop adaptive attitude controller, a robust nonlinear outer-loop controller and a dynamic control allocation scheme) that avoids the complexity typical of solutions based on adaptive back-stepping. An observer-based algorithm achieves decoupling between adaptation and constrained actuator dynamics. A case study is presented to illustrate the capability of the proposed methodology to deal with the simultaneous occurrence of significant model uncertainty and actuator faults.

This paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed via dynamic inversion method using neural network. To deal with input constraints, the additional compensation system is employed to help engine recover from input saturation rapidly. The highlight is that high order integral chained differentiator is used to estimate the newly defined variables and an adaptive neural controller is designed for the altitude subsystem where only one neural network is employed to approximate the lumped uncertain nonlinearity. The altitude subsystem controller is considerably simpler than the ones based on backstepping. It is proved using Lyapunov stability theory that the proposed control law can ensure that all the tracking error converges to an arbitrarily small neighborhood around zero. Numerical simulation study demonstrates the effectiveness of the proposed strategy, during the morphing process, in spite of some uncertain system nonlinearity.

Nowadays, there is a great interest in using active control methods to increase the compressor working range. The advantage of this controlling method is that the performance point can be located in the vicinity of maximum pressure and efficiency. However, most of the existing controllers require an awareness of compressor characteristic, disturbance upper bound, throttle gain, and throttle valve feature; this is why they are limited in engineering applications. In order to overcome the weakness of the existing controllers, this research employs a novel combined controlling method based on robust adaptive control, which is designed using backstepping technique because the compressor behavior is nonlinear. The increased efficiency and improved operational area for the compressor are provided by this controller without requiring any knowledge or information regarding the compressor characteristic, disturbance upper bound, throttle gain, and throttle valve feature. The adaptive controller has been used to compensate for uncertainties of the compressor characteristic and throttle valve as well as the un-modeled dynamics. Also, the controller robustness is a barrier against the time-varying disturbances in flow and pressure applied to the system. Finally, simulation results showed that the designed controller, in addition to assure the system stability, developed the compressor working range, and the convergence of system states was achieved after applying disturbance in flow and pressure.

In this paper, we develop a robust controller for parallel DC-DC converter system by combining the adaptive backstepping technique and decentralized control. The voltages and currents of all converters are coupled with each other. The parallel DC-DC converter system is modeled as a large scaled state-space system with interconnection between subsystems. Each local controller, designed simply based on the local model of each subsystem by using adaptive backstepping technique, only employs local information to generate control signals. Each of them is robust against the interactions, such that each local controller does not need full knowledge of the overall network and operates as a single power converter providing power to the load. The robustness of decentralized adaptive controllers is established. It is shown that the designed decentralized adaptive backstepping controllers can globally stabilize the overall interconnected system asymptotically, such as to drive the system to a reference point and eliminate the disturbance. The load is properly shared between the power converters. The L 2 norm of the system outputs is also established as functions of design parameters. This implies that the transient system performance can be adjusted by choosing suitable design parameters.

This paper studies the problem of adaptive fuzzy control for a category of single-input single-output nonlinear networked control systems with network-induced delay and data loss based on adaptive backstepping control approach. Fuzzy logic systems are used to approximate the unknown nonlinear characteristics existing in the system, while Pade approximation is introduced to handle network-induced delay. Data loss occurs intermittently and stochastically in the data transmitting process, which is regarded as the delay in the controller design. In the framework of adaptive fuzzy backstepping technique, a novel state-feedback adaptive controller is constructed to ensure all signals in the resulting closed-loop system to be bounded and the state variables can be regulated to the origin. Finally, two examples are given to show the validity of the proposed results.

Power electronic converter loads are widespread in DC distributed power system. These tightly controlled converter loads behave as constant power loads (CPL) and may cause stability issue when fed by a source converter. This paper proposes a nonlinear control method to stabilize DC/DC boost converter feeding CPL. The proposed controller is based on adaptive backstepping and nonlinear disturbance observer techniques. Following the step-by-step backstepping algorithm, the designed controller achieves global stabilization of the system. With the load variation estimated by nonlinear disturbance observer (NDO), DC bus voltage can be accurately tracked with fast transient response. The proposed controller not only ensures large signal stability under large disturbance of CPL, but also features high performance at wide operating range. Simulations in Matlab/Simulink are conducted to verify the proposed strategy.

A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach.