Fault-tolerant synthesis controller design for a flight-tracking system

Fault diagnosis for wireless sensor is very important to ensure signal acquisition precision. Radial basis function (RBF) neural network has strong classification ability. However, the selection of the connection weights, the hidden centers and the widths has an important influence on the classification performance of the RBF neural network in the learning process of RBF neural network. Thus, ant colony optimization is employ to gain the parameters of radial basis function neural network. Therefore, a novel method for fault diagnosis of wireless sensor based on RBF neural network and ant colony optimization is presented. The results of computational experiments show that ACO- RBF neural network has a great higher than RBF neural network.

This paper presents an adaptive backstepping neural controller design for aircraft under control surface failures. The control scheme uses radial basis function neural networks in an adaptive backstepping architecture with a full state measurement for trajectory following.13; The requirement for stability is separated from the network-learning part. This allows us to use any function approximation scheme (including neural networks) for learning. For the radial basis function neural networks, a learning scheme in which the network starts with no eurons and adds new neurons based on the trajectory error is developed. Stable tuning rules are derived for the update of the centers, widths, and weights of the radial basis function neural networks. Using Lyapunov's theory, a proof of stability in the ultimate bounded sense is presented for the resulting controller. The fault-tolerant controller design is illustrated for an unstable high performance aircraft in the terminal-landing phase subjected to multiple control surface failures of hard over type and severe winds. The design uses the full 6-degree-of-freedom nonlinear aircraft model, and the simulation studies show that the above controller is able to successfully stabilize and land the aircraft within tight touchdown dispersions.

In the process of moisture content's measurement with microwave resonant cavity based on RBF (radial basis function) neural network, the relationship among resonant frequency, quality factor and environmental temperature (the inputs of network) and moisture content (the output of network) is multiple non-linear. The performance of multiple non-linear regression algorithm is the major factor that determines the accuracy of measurement. A regression algorithm based on a RBF neural network is put forward to improve the measurement result in this paper. The RBF neural network regression algorithm can effectively avoid the BP algorithm disadvantages such as getting into local minimum and the low efficiency, and has the advantages such as high generalization precision and rapid convergence. Consequently, the measurement accuracy is enhanced. In this paper, experimental results show that the high-precision measurement value of moisture content can be obtained with the multiple non-linear regression algorithm based on RBF neural network.

This paper uses a radial basis function (RBF) neural network for star identification. A feature vector consisting of distance and angle cosine values is constructed as a library of recognition feature stars and input to the neural network. Then, the network’s output is the number of the corresponding primary star. Comparing the training efficiency and robustness of the grid algorithm with the BP neural network and radial basis function neural network for star identification, the experimental results show that the radial basis function neural network has a strong anti-interference ability against noise and pseudo-star interference. The following conclusions are drawn from the simulation tests: the sample recognition rate of radial basis function neural network algorithm can reach 96% for star position and star addition noise; when the proportion of pseudo-star reaches 20%, the sample recognition rate of radial basis function neural network algorithm is still higher than grid algorithm and BP neural network, which indicates that the radial basis function neural network algorithm has the solid anti-interference ability to both noise and pseudo-star. Therefore, the radial basis function neural network algorithm based on star Identification has specific application prospects.

Finite Time Fault Tolerant Control Design for UAV Attitude Control Systems with Actuator Fault and Actuator Saturation

In this paper, we are going to propose an online radial basis function (RBF) neural network algorithm without any preprocessing step. Then a kernel principal component analysis (KPCA) is coupled with the proposed online RBF neural network algorithm. Indeed, the KPCA method is used as a preprocessing step to reduce the feature dimension which fed to the RBF neural network. Reducing memory requirements of the models makes RBF neural network training efficient and fast. These two proposed algorithms are applied, with success, for identification of a mobile robot position. The simulation results present that the used sigmoid function as a kernel, compared to other kernel functions, which gives an excellent model and a minimum mean square error.

Dissolved oxygen (DO) as one of the most fundamental parameters of water quality plays a vital role in aquatic life. This study was conducted to predict DO, biological oxygen demand (BOD), and chemical oxygen demand (COD) in an intensive rainbow trout rearing system with different biomass (B). The multilayer perceptron (MLP) and the radial basis function (RBF) neural networks were employed for evaluating the impacts of food parameters (crude protein (CP), consumed feed (CF)), fish parameters (different values of B, and weight gain (WG)), and water quality parameters including temperature (T) and flow rate (Q) on variation of DO, BOD, and COD concentrations. This study's results showed that although both MLP and RBF neural networks are capable to estimate DO, BOD, and COD concentrations, RBF neural network showed better performance compared to MLP neural network. The results of sensitivity analysis indicated that the parameter CF has the highest effect on DO concentration estimation. Independent variables CF, CP, WG, and B showed the highest to the lowest rank of impacts on BOD estimation, respectively. The results also illustrated a decreasing trend of the effects on the estimation error of COD changes simulation by all independent variables, including B, T, WG, CF, CP, and Q, respectively. RBF neural network based on better stability and generalization ability with average root mean square error (RMSE) and mean absolute percentage error (MAPE) values of less than 0.12 and 3% was superior to MLP in DO, BOD, and COD concentration prediction. Moreover, CF was identified as the most effective factor in estima12tion process. Based on the present study results, there are direct relationships between DO, BOD, and COD concentrations and water quality parameters, fish parameters, and food parameters. Food parameters relative to fish and water quality parameters imposed the greatest effects. Improvement in feeding process such as application of intelligence feeding methods and change in fish diet and feeding time can considerably reduce losses in production system. Graphical abstract.

An adaptive feedback control scheme and a switching strategy for the output tracking stabilization of switched nonlinear systems with time-delay are presented by using RBF (radial basis function) neural network The RBF neural networks is used to compensate adaptively for the unknown nonlinear parts of switched nonlinear systems, and the approximation errors of RBF neural networks are introduced to the adaptive law in order to improve the tracking quality of the systems. The controller and switching strategy can guarantee asymptotic stability of tracking error for the overall system

This article introduces the design and implementation of a fishery forecasting system based on Radial Basis Function (RBF) neural network. The system was developed using the CLient/Server architecture, the C# programming language in the environment of Visual Studio 2008 on the Windows7 platform. It draws knowledge from RBF neural network theory, the production historical data of pelagic fishery and the marine environment data. The system uses the Object-Oriented analysis and design method. It can quickly obtain the forecast results available to users through inputting marine environment data information and the RBF neural network model. The forecasting system includes three major functional modules, namely preprocessing fishery production data, matching production data and environmental data, training RBF neural network and making predictions. Experiments have shown that this forecasting system can generate accurate and effective pelagic fishery knowledge.

A wind power system (WPS) involving a flywheel energy storage (FES) unit is proposed to reduce the fluctuation of output power due to the intermittent nature of wind power. The control of the FES unit is based on a wind speed forecasting technique by the RBF neural network. A new control strategy adopting different reference DC bus voltages under different forecasting wind speeds is presented to improve the output power regulation capability of the FES unit. The wind speeds in the next time array can be accurately forecasted by the radical basis function (RBF) neural network. The mathematical model of a stand-alone wind power generation system including a FES unit is established and simulated by Matlab/Simulink. The simulation results verify the correctness of the system model and the effectiveness of the control method.

A evaluation model based on the integration of analytic hierarchy process (AHP)-rough set theory (RS) and radial basic function (RBF) neural network is put forward for grasping the hydropower project financing risk. Firstly, the evaluation indicator system is constructed by AHP, then the evaluation indicators are discretized by RS neural network. And then, RBF neural network is used to evaluate the hydropower project financing risk. In order to grasp this evaluation model better, finally, the paper provides an example to demonstrate the application of this evaluation model.

A new method based on the integration of principal component analysis (PCA) and radial basic function (RBF) neural network is put forward for selecting the real estate project. Firstly, principal component analysis (PCA) is used to reduce the evaluation index dimensions. And then, radial basic function (RBF) neural network is used to evaluate the real estate projects. In order to grasp this method better, finally, the paper provides a case to demonstrate the application of this method in selecting the real estate project. The case has shown that the method applied to select the real estate project is feasible and reliable.

To improve the accuracy of tracking unmanned vehicles on known trajectories, two optimised model predictive control (MPC) trajectory tracking control systems are designed based on the adaptive compensation and robust control of a radial basis function (RBF) neural network. Based on the traditional MPC trajectory tracking controller and the local approximation characteristics of the RBF neural network, the proposed RBF compensation–MPC control system is designed to compensate for the inaccuracy in the MPC prediction model arising from modelling errors. The results show that this method can achieve a root mean square error of less than 0.3703 m for the lateral position. Subsequently, to suppress the error generated by the RBF neural network and reduce the degree of vehicle sideslip, the error is considered to be external interference, and the anti-interference characteristic of the RBF robust control is incorporated into the RBF robust-MPC control system. Following the re-optimisation of the RBF robust control, the root mean square error of the lateral position is set within 0.2352 m. The results of a MATLAB/Carsim joint simulation show that using the RBF robust control can improve the tracking accuracy of the traditional MPC controller compared with RBF compensation control, while simultaneously improving the driving stability of the vehicle.

Model reference adaptive sliding mode control (MRASMC) using radical basis function (RBF) neural network (NN) is proposed to control the single-phase active power filter (APF). The RBF NN is utilized to approximate nonlinear function and eliminate the modeling error. AC side model reference adaptive current controller not only guarantees the globally stability of the APF system but also generate the compensating current to track the harmonic current accurately. Moreover, a sliding mode controller based on exponential approach is designed to improve the tracking performance of DC side voltage. Simulation results demonstrate that MRASMC using RBF NN can improve the adaptability and robustness of the APF system and track the given instructional signal quickly.

The method of Self-Organizing Configuration Design (SOCD) for vehicle leaf-spring can lessen the complexity of process and improve the accuracy of calculation. In order to realize a rapid parameter solving in SOCD for vehicle leaf-spring, a method of parameter configuration is put forward based upon RBF (Radial Basis Function) Neural Network. Starting with problem description of parameter configuration, the feasibility of RBF used for parameter configuration is analyzed and the possible structure of RBF is described. Taking uniform cross section leaf-spring as an example, parameter configuration model based on RBF Neural Network is built by the train of collected samples and the final simulation results show that the model can achieve a comparatively accurate solving of parameter configuration to testify the method in the paper.