Reconfigurable Flight Control System Design Using Adaptive Neural Networks

With the development of economic science and technology, the development of computer vision has undergone rapid changes, and various products relying on computer vision are also more and more, such as smart home, robot technology, and so on. At present, robot technology has become a very important part of the development of human science and technology, and in the field of industrial robots, the most rapid development is the robot with robot arm adaptive motion. It is very necessary to study the adaptive motion control of the manipulator based on machine learning. The robot with the adaptive motion of the manipulator can carry out logistics express sorting, operate in the doors and windows outside the building, and pick fruits in the orchard, which can ensure the effective implementation of hard work. Therefore, this paper proposes a mechanical adaptive control method based on a neural network. According to the motion model of the manipulator, the RBF neural network model is used to judge the stability of the system according to the Lyapunov function. The related algorithms of machine learning and multi-degree of freedom manipulator are studied and improved. The RBF neural network model approximates the unknown function infinitely and then establishes the complex motion model. Aiming at the adaptive neural network of a manipulator, a network adaptive terminal control method is proposed. Firstly, a stable manipulator motion system is designed by using a neural network, and then the terminal synovial controller is designed by using backstepping control technology. The stability of the method is proved by using the approximation virtual control technology of the neural network. The adaptive control is realized by using the learning and self-adaptability of the neural network; thus, the stability analysis of the closed-loop system is realized.

The aircraft flight control problem is addressed via a nonlinear adaptive backstepping control. A reconfigurable flight controller is designed that achieves robust, high accuracy command angle tracking in spite of uncertain aerodynamic coefficients. The backstepping controller makes use of a recursive procedure that breaks down the control problem for the full system into a sequence of designs for lower order systems. The angular rates of the aircraft are considered as virtual inputs for slow dynamics, which includes velocity roll angle, angle of attack and sideslip angle, to track the given commands. It is assumed that the aerodynamic coefficients include uncertainties, and a gradient projection algorithm is used in an adaptive controller to estimate the uncertain aerodynamic coefficients. Stability of the proposed adaptive backstepping controller is shown and discussed. Finally, these results are applied to a nonlinear six-degree-of-freedom aircraft model. Simulation results are presented to demonstrate that the stability and high accuracy tracking performance is accomplishedin spite of uncertainties in the model.

A force/position hybrid adaptive control method based on radial basis function(RBF) neural network is proposed to solve the problem of difficulties to precisely describe the compliance and friction for robot terminal during trajectory tracking process.RBF neural network is an efficient feed-forward neural network with non-linear approximation and global optimization characteristics,which is not provided by other feed-forward networks,which is simple in network structure,and rapid in training speed.An adaptive controller is designed that relies on the nonlinear approximation ability of the RBF neural network to estimate the uncertainty factors in the models,the update rules for the weights of the controller neural network is provided and its finally uniform boundedness of the errors of the controller output force and position is proved.The controller is applied to a duct cleaning robot for simulation experiments.Simulation results shows that the adaptive controller demonstrates superior tracking precision and robustness compared with traditional adaptive controller.

Hydraulic system is widely used in industry where large actuation forces are needed such as electro-hydraulic positioning system, active suspension control, and so on. One of the main problem in hydraulic system is the nontriviality, for instance, the deadbands in valve operation, frictions of links, saturations, pressure dynamics, etc. both system unknown nonlinearities and external disturbances should be considered in controller design. For underwater hydraulic manipulator, because of the environment of sea and the large nonlinearities of the hydraulic manipulator system, both system unknown nonlinearities and external disturbances should be considered in controller design. A lot of control methods have been proposed to solve the problem properly, such as sliding mode control, adaptive control, fuzzy adaptive control, adaptive robust control, ect. However, due to limited knowledge about hydraulic manipulator system, it is hard to precisely estimate the nonlinearities for a possible higher performance. In this paper, a neural network adaptive robust control method is proposed to help control an underwater hydraulic manipulator. In order to achieve better performance using adaptive robust control strategy, unknown nonlinearities in the system are approximated by the outputs of radial basis function neural network (RBFNNs). A projection law has been used to bound the NN weights when they are tuning on-line to avoid possible adaptive divergence, and the tracking transient performance is guaranteed by robust control law. Simulation results illustrate that manipulator has a good tracking performance.

Modeling and switching adaptive control for nonlinear morphing aircraft considering actuator dynamics

A novel adaptive backstepping control scheme based on invariant manifolds for unmanned aerial vehicles in the presence of some uncertainties in the aerodynamic coefficients is presented in this article. This scheme is used for command tracking of the angle of attack, the sideslip angle, and the bank angle of the aircraft. The control law has a modular structure, which consists of a control module and a recently developed non-linear estimator. The estimator is based on invariant manifolds, which allows for prescribed dynamics to be assigned to the estimation error. The adaptive backstepping control law combined with the estimator covers the entire flight envelope and does not require accurate aerodynamic parameters. The stability of the whole closed-loop system is analyzed using the Lyapunov stability theory. The full six-degree-of-freedom non-linear model of a small unmanned aerial vehicle is used to demonstrate the effectiveness of the proposed control law. The numerical simulation result shows that this method can yield satisfying command tracking despite some unknown aerodynamic parameters.

In this paper we propose architecture of a processor for vector operations involved in on-line learning of neural networks. We target to implement on-line learning on a Radial Basis Function Neural Network (RBFNN) based Face Recognition (FR) system that has pseudo inverse computation as an essential component during training. Synaptic weights of RBFNN output layer need to be updated whenever the FR system comes across a new face to be learnt. For real-time on-line learning, update of synaptic weights is done using an existing Incremental Pseudo Inverse (IPI) algorithm in the place of compute intensive pseudo inverse algorithm. We design a custom data-path for vector operations appearing in IPI algorithm. The custom data-path along with configuration and memory access mechanisms forms a processing unit, termed Processor for Vector Operations (VOP). We simulate and synthesize VOP to target Virtex-6 FPGA using the Xilinx ISE. Apart from on-line learning, the VOPs can be used in acceleration of several applications involving predominant vector-matrix operations.

Active front steering (AFS) can enhance the vehicle yaw stability. However, the control of vehicle yaw rate is very challenging due to (1) the unmodelled nonlinearity and uncertainties in vehicle dynamics; (2) timely response in control scheme. These two issues can be simultaneously alleviated through a random projection neural network (RPNN) for its high model generalisation and fast computational speed. However, typical RPNN cannot be directly applied to adaptive control applications. Therefore, a new RPNN-based adaptive neural control method is proposed, which is equipped with a newly designed adaptation law based on the theorem of Lyapunov stability. To test the performance of the proposed control method, simulations were carried out using a validated vehicle model. The simulation results show that, compared to conventional backpropagation neural network (BPNN) based controller, the proposed RPNN-based adaptive controller can reduce the response time and attenuate oscillatory steering in the case of cornering manoeuvre under fast variant vehicle speed. The results also demonstrate that the proposed RPNN-based adaptive controller outperforms the state-of-the-art fuzzy logic controller and the error feedback controller in multiple aspects including tracking nominal vehicle yaw rate, desired sideslip angle and intended path, showing its significance in vehicle yaw stability control.

Currently, there is a need to improve the systems and control of pumping equipment in the oil and gas production and oil and gas transport industries. Therefore, an adaptive neural network control system for an electric drive of a production well was developed. The task of expanding the functional capabilities of asynchronous electric motors control of the oil and gas production system using the methods of neural networks is solved. We have developed software modules of the well drive control system based on the neural network, an identification system, and a scheme to adapt the control processes to changing load parameters, that is, to dynamic load, to implement the entire system for real-time control of the highspeed process. In this paper, based on a model of an identification block that includes a multilayered neural network of direct propagation, the control of the well system was implemented. The neural network of the proposed system was trained on the basis of the error back-propagation algorithm, and the identification unit works as a forecaster of system operation modes based on the error prediction. In the initial stage of the model adaptation, some fluctuations of the torque are observed at the output of the neural network, which is associated with new operating conditions and underestimated level of learning. However, the identification object and control system is able to maintain an error at minimum values and adapt the control system to a new conditions, which confirms the reliability of the proposed scheme.

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.

This paper presents a design method of adaptive fuzzy controllers using dual-fuzzy neural networks (DFNNs). For the convenience of adaptive control, the structure of the two-fuzzy neural-network controller is divided into two parts. Each part is a fuzzy neural-network (FNN). The adaptive controller uses two FNNs. One FNN is used to identify a fuzzy model of controlled object. The fuzzy control rule is designed with the linguistic rules of the fuzzy model and is incorporated into the other FNN. Simulations using a controlled object with an non-monotonic input-output characteristics are done to verify the proposed design method.

Global adaptive neural network control of nonlinear time-varying systems with unknown control coefficients and model uncertainties

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

SummaryThe concept of linear parameter‐varying (LPV) model has been developed as a convenient framework to describe a special class of uncertain LPV flight aircraft systems. In this paper, an adaptive control method for a class of uncertain LPV systems whose state‐space matrix elements are unknown affine functions of a set of measurable scalar parameters is presented. Firstly, the scalar parameters are separated from the state matrices such that the LPV model is rewritten as general unknown parameter model, then state feedback adaptive control laws, in both cases: the matched uncertainty and the unmatched uncertainty, are designed with the aim of controlling the system state to follow a desired trajectory. The sufficient condition of stability is derived using a Lyapunov equation, not a parameterized Lyapunov equation. Simulation tests based on a simple example and a nonlinear model of a transport aircraft are given to illustrate the effectiveness of the control algorithm and to demonstrate that the adaptive controller satisfies the performance requirement for an aircraft control system. Copyright © 2016 John Wiley & Sons, Ltd.

Adaptive flight control systems hold tremendous promise for maintaining the safety of a damaged aircraft and its passengers. However, most currently proposed adaptive control methodologies rely on online learning neural networks (OLNNs), which necessarily have the property that the controller is changing during the flight. These changes tend to be highly nonlinear, and difficult or impossible to analyze using standard techniques. In this paper, we approach the problem with a variant of compositional verification. The overall system is broken into components. Undesirable behavior is fed backwards through the system. Components which can be solved using formal methods techniques explicitly for the ranges of safe and unsafe input bounds are treated as white box components. The remaining black box components are analyzed with heuristic techniques that try to predict a range of component inputs that may lead to unsafe behavior. The composition of these component inputs throughout the system leads to overall system test vectors that may elucidate the undesirable behavior