Fuzzy Neural Network based Sliding Mode Control for Hypersonic Vehicles

A new force tracking control algorithm of partial actuated lower limb exoskeleton suit, which is designed for enhancing human motion is presented in this paper. Firstly, a mathematical model of the electro-hydraulic servo system was created, and equations for the frictions in the hydraulic valve and actuator were obtained. Secondly, the appropriate observer based on the estimated functions and the measurement error equations are presented for the sliding mode control algorithm. Thirdly, a sliding mode controller with applicable surface coefficient has been designed for force tracking control of the servo system. Fourthly, so as to reduce the error caused by the unchangeable surface of the sliding mode control, a radial basis functions (RBF) neural network control algorithm has been introduced to offset the disadvantage of the sliding mode control by moving the sliding surface effectively. Finally, the simulation results under conditions of different frequencies and the trial results based on the human motion of sliding mode control and the RBF based sliding control are presented, which indicate that RBF based sliding control provides a better performance than regular sliding mode control.

In this paper, an adaptive chattering free neural network‐based sliding mode control (ACFN‐SMC) method is proposed for tracking trajectories of redundant parallel manipulators. ACFN‐SMC combines adaptive chattering free radial basis function neural networks (RBFN), sliding mode control with online updating the robust term parameters, and a nonlinear compensation item for reducing tracking errors. The stability of the closed‐loop system with modeling uncertainties, frictional uncertainties, and external disturbances is ensured by using the Lyapunov method. The proposed controller has a simple structure and little computation time while securing dynamic performance with expected quality in tracking trajectories of redundant parallel manipulators. In addition, the ACFN‐SMC strategy does not need to know the upper bound of any uncertainties. From the simulation results, it is evident that the proposed control strategy not only has significantly higher robustness capability for uncertainties but also can achieve better chattering elimination when compared with those using existing intelligent control schemes.

In this paper, an attitude control method combining radial basis (RBF) neural network with integral sliding mode control is proposed for the re-entry stage of hypersonic vehicle with uncertain aerodynamic parameters and atmospheric density. Firstly, the Time-scale separation model of nonlinear equations for aircraft is established. Meanwhile, the feedback linearization method is used to linearize the time scale separation model. For fast and slow control subloops, a global sliding mode variable structure control was designed, and the stability of the closed-loop system was verified by Lyapunov theory. Finally, RBF neural network online regulation law is designed to adjust the controller parameters online to reduce chattering. The simulation results show that the controller can maintain good dynamic characteristics even when the aerodynamic data are greatly deviated.

Despite advantages of air-breathing hypersonic vehicles (AHVs) over turbojet-powered or rocket-powered vehicles, the control system design is still a challenge. In particular, the existing control methods lack an effective mechanism to handle time-varying uncertain parameters in the control-oriented AHV model. This study proposes a longitudinal control strategy for AHVs focusing on time-varying uncertain parameters, fast tracking and low computational complexity. The adaptive law is designed to estimate the norm bound of the vector of uncertain parameters, in which case parametric variations are accommodated with a large reduction on the dimension of the resulting control algorithm. By utilizing sliding mode exact differentiators, the analytic differential computation on virtual controls is avoided in the adaptive back-stepping design for altitude tracking. It is shown that both velocity and altitude tracking errors converge into the prescribed sets in finite time. A simulation study on the high-fidelity AHV model is provided to verify the proposed control strategy.

In this paper, a multi-input/multi-output (MIMO) adaptive sliding mode control system based on immersion and invariance (I&I) methodology is developed and analyzed for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle whose model is multivariable, nonlinear, and includes parameter uncertainty. Sliding mode controllers combined with on-line parameter estimators based on adaptive I&I theory are synthesized to suppress the significant aerodynamic parameter uncertainties. The stability of the closed-loop control system is proved by the Lyapunov stability theory. Simulation experiments are conducted to evaluate the robustness of the controller system with respect to parametric uncertainty, which meanwhile show the effectiveness and high performance of the controller system in terms of aggressive maneuvering control inputs.

In this paper, a controller combining backstepping and adaptive supertwisting sliding mode control method is proposed for altitude and velocity tracking control of air-breathing hypersonic vehicles (AHVs). Firstly, the nonlinear longitudinal model of AHV is introduced and transformed into a strict feedback form, to which the backstepping method can be applied. Considering the longitudinal trajectory tracking control problem (altitude control and velocity control), the altitude tracking control system is decomposed to several one-order subsystems based on the backstepping method, and an adaptive supertwisting sliding mode controller is designed for each subsystem, in order to obtain the virtual control variables and actual control input. Secondly, the overall stability of the closed-loop system is proved by the Lyapunov stability theory. At last, the simulation is carried out on an AHV model. The results show that the proposed controller has good control performances and good robustness in the parameter perturbation case.

Recently the adaptive mixing control (AMC) approach has been applied to an airbreathing hypersonic flight vehicle (AHFV) model, and simulation results demonstrate that this adaptive scheme may be capable of improving performance when compared to a non-adaptive mixed-μ design with similar objectives. In this note, the analysis of this AMC scheme's stability and robustness properties is presented, establishing that if the unmodeled dynamics satisfy a norm bound condition, then the closed-loop states are bounded and the mean-square regulation error is of the order of the modeling error.

ABS(antilock brake system) is one of the most important active safety technology for modern vehicles which could enhance vehicle active safety. In this paper, an improved sliding mode control method based on reaching law has been proposed to solve the vibration problem in traditional sliding mode control. The ABS control strategy has been designed based on the sliding mode variable structure control. On this basis, the ABS single wheel depending on control strategy has been designed to ensure the braking stability. By hardware-in-loop simulation, the results demonstrate that ABS sliding mode variable structure control could enhance braking stability performance and improve the control effect on high friction and low friction road.

In the control algorithm of digital hydraulic position tracking system, there exists many modeling assumptions and limited robustness. By the combining of the sliding mode variable structure control and model reference adaptive control, the present essay is to design a new sliding mode adaptive controller and do a simulation analysis. The simulation results indicate that the controller has high precision in controlling and fast in responding. It overcomes the problem of the control precision's decreasing when the adaptive control is applied alone and the output of the system is zero and avoids the influences of the uncertain parameters and the external disturbance, thus the robustness of the system.

It is difficult to obtain the cam profile accurately in cam grinding because of the complicated contour, the grinding force fluctuate, the tracking performance of the servo system and other interferential factors. The tracking performance of the servo system has a great influence on the cam profile error and leads to the reduction of the cam profile precision. In order to reduce the profile error of cam and improve the machining accuracy, the tracking performance of each servo axis is improved by using sliding mode variable structure control method. The sliding mode variable structure controller adopting the method of proportional switching, quasi-sliding mode and dynamic switching function are designed respectively. The simulation analysis are carried out, and the results of simulation show that the tracking error can be reduced effectively using the sliding mode variable structure control in cam.

This paper concerns the finite time tracking control problem for the longitudinal model of an airbreathing hypersonic vehicle (AHV) with actuator faults and external disturbances. First, the dynamics of system outputs V and h are given by calculating the differentials. Next, combining external disturbances and actuator additive faults as lumped disturbances, a nonlinear disturbance observer (NDOB) is designed to estimate them. Then, two integral sliding mode manifolds are designed based on tracking errors. Finally, combining NDOB and integral sliding mode control (SMC) techniques, a composite fault tolerant controller is constructed for the longitudinal model of an AHV. This control approach can make the velocity and altitude track the reference signals in finite time and reduce chattering phenomenon caused by SMC. Simulation results are given to show the effectiveness of the proposed control method.

An airbreathing hypersonic vehicle(AHV) generally adopts a scramjet engine as its propulsion, which needs strict conditions of flight dynamic pressure. A new dynamic pressure control system is presented, in which the AHV autopilot directly uses dynamic pressure to track the dynamic pressure command. Firstly, the dynamic pressure model of the AHV is established and the state equations of dynamic pressure control are given. Then, the dynamic pressure control system is implemented using the LQR optimal control theory. The dynamic pressure error is augmented in order to add an integral control to track the dynamic pressure with zero steady error. The structure of the dynamic pressure control system is also obtained. The simulation results show that the dynamic pressure control system has a good performance for guaranteeing the dynamic pressure of the scramjet engine with the disturbance of drag and thrust considered.

Purpose – The air‐breathing hypersonic vehicle (AHV) includes intricate inherent coupling between the propulsion system and the airframe dynamics, which results in an intractable nonlinear system for the controller design. The purpose of this paper is to propose an H∞ control method for AHV based on the online simultaneous policy update algorithm (SPUA).Design/methodology/approach – Initially, the H∞ state feedback control problem of the AHV is converted to the problem of solving the Hamilton‐Jacobi‐Isaacs (HJI) equation, which is notoriously difficult to solve both numerically and analytically. To overcome this difficulty, the online SPUA is introduced to solve the HJI equation without requiring the accurate knowledge of the internal system dynamics. Subsequently, the online SPUA is implemented on the basis of an actor‐critic structure, in which neural network (NN) is employed for approximating the cost function and a least‐square method is used to calculate the NN weight parameters.Findings – Simulation...

Variable Structure System with Sliding Mode Controller

Nonlinear model predictive control, which is conceptually similar to its linear counterpart except that nonlinear dynamic model is used for process prediction and optimization, is a developing control field. The proposed nonlinear model predictive control scheme, which is called nonlinear sliding mode model predictive control, integrates model predictive control and sliding mode variable structure control. A feasible dual-mode control scheme is presented in the paper, sliding mode predictive controller is implemented while the system state is outside the terminal region, besides sliding mode variable structure control designed off-line is used. The resulting control scheme has strong points of the two control methods. By predicting the pre-designed switching function, the predictive control sequence can be found by solving constrained open-loop optimal control problem, and the current control is implemented. At next sampling time the optimizing procedure is repeated. By constraining terminal sliding mode with an inequality, the state is steered into the predesigned sliding mode region, then the closed-loop stability is proved, and furthermore the system performance is analyzed. With some conditions, the method can be extended to a quasi-infinite horizon formulation. Simulation results show that less calculation is needed.