High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators 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.

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.

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.

Fuzzy Self-tuning Tracking Differentiator for Motion Measurement Sensors and Application in Wide-Bandwidth High-accuracy Servo Control

Sensor differential signals are widely used in many systems. The tracking differentiator (TD) is an effective method to obtain signal differentials. Differential calculation is noise-sensitive. There is the characteristics of low-pass filter (LPF) in the TD to suppress the noise, but phase lag is introduced. For LPF, fixed filtering parameters cannot achieve both noise suppression and phase compensation lag compensation. We propose a fuzzy self-tuning tracking differentiator (FSTD) capable of adaptively adjusting parameters, which uses the frequency information of the signal to achieve a trade-off between the phase lag and noise suppression capabilities. Based on the frequency information, the parameters of TD are self-tuning by a fuzzy method, which makes self-tuning designs more flexible. Simulations and experiments using motion measurement sensors show that the proposed method has good filtering performance for low-frequency signals and improves tracking ability for high-frequency signals compared to fixed-parameter differentiator.

Compound fault-tolerant attitude control for hypersonic vehicle with reaction control systems in reentry phase

In this paper, a novel compound fault-tolerant attitude control (FTC) scheme is proposed for reentry hypersonic vehicles with aerodynamic surfaces and reaction control systems (RCS) in the presence of parameter uncertainties, external disturbances and aerodynamic surfaces faults. Aerodynamic surfaces work as the primary actuators and RCS serve as auxiliary actuators. When aerodynamic surfaces cannot provide the required attitude control torque due to low dynamic pressure or faults, RCS are activated to assist aerodynamic surfaces to generate the residual torque. A nonlinear disturbance observer-based sliding mode controller is designed to calculate the required attitude control torque which can handle the parametric uncertainties and external disturbances together. The quadratic programming method is applied to obtain the optimal aerodynamic surfaces deflections from the required control torque. An innovative fuzzy rule-based decision-making system is design to solve the RCS control allocation problem, which is conceptually easy to understand and computationally efficiently compared with existing approaches. Based on quantized control theory, the closed-loop control system stability is rigorously analyzed. Simulation results are given to demonstrate the effectiveness and efficiency of developed FTC scheme.

Backstepping Sliding Mode Control for Radar Seeker Servo System Considering Guidance and Control System

This paper investigates the design of a missile seeker servo system combined with a guidance and control system. Firstly, a complete model containing a missile seeker servo system, missile guidance system, and missile control system (SGCS) was creatively proposed. Secondly, a designed high-order tracking differentiator (HTD) was used to estimate states of systems in real time, which guarantees the feasibility of the designed algorithm. To guarantee tracking precision and robustness, backstepping sliding-mode control was adopted. Aiming at the main problem of projectile motion disturbance, an adaptive radial basis function neural network (RBFNN) was proposed to compensate for disturbance. Adaptive RBFNN especially achieves online adjustment of residual error, which promotes estimation precision and eliminates the “chattering phenomenon”. The boundedness of all signals, including estimation error of high-order tracking differentiator, was especially proved via the Lyapunov stability theory, which is more rigorous. Finally, in considered scenarios, line of sight angle (LOSA)-tracking simulations were carried out to verify the tracking performance, and a Monte Carlo miss-distance simulation is presented to validate the effectiveness of the proposed method.

Robust inertia-free attitude takeover control of postcapture combined spacecraft with guaranteed prescribed performance

In this paper, a robust inertia-free attitude takeover control scheme with guaranteed prescribed performance is investigated for postcapture combined spacecraft with consideration of unmeasurable states, unknown inertial property and external disturbance torque. Firstly, to estimate the unavailable angular velocity of combination accurately, a novel finite-time-convergent tracking differentiator is developed with a quite computationally achievable structure free from the unknown nonlinear dynamics of combined spacecraft. Then, a robust inertia-free prescribed performance control scheme is proposed, wherein, the transient and steady-state performance of combined spacecraft is first quantitatively studied by stabilizing the filtered attitude tracking errors. Compared with the existing works, the prominent advantage is that no parameter identifications and no neural or fuzzy nonlinear approximations are needed, which decreases the complexity of robust controller design dramatically. Moreover, the prescribed performance of combined spacecraft is guaranteed a priori without resorting to repeated regulations of the controller parameters. Finally, four illustrative examples are employed to validate the effectiveness of the proposed control scheme and tracking differentiator.

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.

Analytical design of an industrial two-term controller for optimal regulatory control of open-loop unstable processes under operational constraints

This paper proposes a novel optimization-based approach for the design of an industrial two-term proportional-integral (PI) controller for the optimal regulatory control of unstable processes subjected to three common operational constraints related to the process variable, manipulated variable and its rate of change. To derive analytical design relations, the constrained optimal control problem in the time domain was transformed into an unconstrained optimization problem in a new parameter space via an effective parameterization. The resulting optimal PI controller has been verified to yield optimal performance and stability of an open-loop unstable first-order process under operational constraints. The proposed analytical design method explicitly takes into account the operational constraints in the controller design stage and also provides useful insights into the optimal controller design. Practical procedures for designing optimal PI parameters and a feasible constraint set exclusive of complex optimization steps are also proposed. The proposed controller was compared with several other PI controllers to illustrate its performance. The robustness of the proposed controller against plant-model mismatch has also been investigated.

Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique–based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.