Basis Function Neural Network Control Research Articles

Fault-Tolerant Control for Carrier-Based Aircraft Automatic Landing Subject to Multiple Disturbances and Actuator Faults

This paper introduces a fault-tolerant control scheme for the automatic carrier landing of carrier-based aircraft using direct lift control. The scheme combines radial basis function neural network and active disturbance rejection control (RBF-ADRC) to overcome the impact of actuator failures and external disturbances. First, the carrier-based aircraft model, the carrier air-wake model, and the actuator fault model were established. Secondly, ADRC is designed to estimate and compensate for actuator faults and disturbances in real time. RBFNN adjusts the ADRC controller parameters based on the system state. Then, the Lyapunov function is constructed to prove the stability of the closed-loop system. The controller is applied to the direct lift control channel, auxiliary attitude channel, and approach power compensation system. The direct lift control improves the performance of fixed-wing aircraft. Finally, comparative simulations were conducted under various actuator failures. The results demonstrate the remarkable fault tolerance of the RBF-ADRC scheme, enabling precise tracking of the desired glide path by the shipboard aircraft even in the presence of actuator failures.

Adaptive Sliding Mode Control Based on a Radial Neural Model Applied for an Electric Drive with an Elastic Shaft

External disturbances, uncertainties, and nonlinear behavior are problems that are commonly encountered by control system designers. In order to save on energy and materials, mechanical structures have become lighter and more flexible, which only exacerbates the control problem. To resolve this issue, robust and adaptive control strategies have been proposed and have recently gained a lot of interest in modern scientific literature. This article proposes a combination of both approaches: a sliding mode—radial basis function neural network controller applied to an electrical drive with a sophisticated mechanical structure. The proposed sliding surface provides robustness against parameter uncertainties, while the neural network adjusts itself to the current state of the drive and mitigates the oscillations resulting from the elastic connection with the load machine. This article proves the stability of the proposed control algorithm in the sense of Lyapunov, provides an in-depth numerical analysis, and compares those results with the experimental tests. The algorithm was implemented in a 1103 dSPACE fast-prototyping card and was used to control a 0.5 kW DC motor connected to the load machine by a long (thin) steel shaft.

Fault-Tolerant Event-Triggrred Control for Multiple UAVs with Predefined Tracking Performance

This paper proposes an event-triggered fault-tolerant time-varying formation control method dedicated to multiple unmanned aerial vehicles (UAVs). We meticulously design a formation-tracking controller with a predefined tracking performance to accommodate the presence of actuator faults and external disturbances. Firstly, the formation-tracking controller acquires the desired heading using the line-of-sight algorithm. Secondly, in the presence of actuator faults and external disturbances, we introduce the radial basis function neural network (RBFNN) and adaptive law tracking control to effectively compensate for their effects. Additionally, we design adaptive tracking controllers and event-triggering conditions to increase the computational frequency. The predefined tracking performance, implemented via a Lyapunov function, ensures the convergence of the tracking error over time. Finally, we conduct a thorough analysis of the system’s stability, successfully eliminating the possibility of Zeno behavior. The simulation results thoroughly validate the effectiveness of the theoretical analysis.

Research on a Pressure Control Method for a Liquid Supply System Based on Online Updating of a Radial Basis Function Neural Network

In order to solve the problem of frequent pressure fluctuations caused by fluid quantity variation in hydraulic support liquid supply systems and the pressure response lag caused by long-distance pipelines, an online updated radial basis function neural network (RBF neural network) control method was proposed for the long-distance liquid supply system. Based on the analysis of the measured pressure fluctuations of the mining face and the process of the stable pressure liquid supply system, the influencing factors of the stable pressure liquid supply flow demand were obtained. The flow set of the stable pressure liquid supply system was established and fitted in the SimulationX–Simulink co-simulation model and the online correction was carried out by using the characteristics of the repeated action of the hydraulic support. Finally, the online updating RBF neural network regulator was established to realize the pressure regulator control of the pumping station, and the experimental platform was set up for verification. The results show that this method can effectively reduce the pressure fluctuations caused by the change in the flow demand of the mining face, and can adjust the flow rate of the mining face, reduce the pressure impact, and improve the efficiency of the machine.

Adaptive and intelligent control of a dual-arm space robot for target manipulation during the post-capture phase

Dual-arm space robots provide a promising solution for efficiently and flexibly completing complex on-orbit servicing tasks. However, their effectiveness is often hindered by strong dynamic coupling between the robotic arms and the satellite base in the microgravity environment. In this paper, both robotic arms are used as task arms, and a system model that considers the closed-loop constraint formed after a dual-arm space robot captures a target is established. On this basis, two types of control strategies, namely self-tuning control strategy (STC) and radial basis function neural network control strategy (RBF) are proposed for trajectory tracking of the target transferred or detumbled by the robot arms in the presence of system uncertainty or external disturbances. The control methods are verified and compared against the traditional computed torque control strategy (CTC) in terms of control accuracy and system robustness through simulation. The results demonstrate the excellent robustness of both RBF and STC strategies. RBF outperforms the other strategies in terms of control accuracy, while STC proves to be the most energy-efficient.

Radial basis function neural network controller for power control of molten salt breeder reactor of nuclear power plant

The molten salt breeder reactor (MSBR) basically uses molten salt as fuel. Due to molten salt, it is difficult to control power of the MSBR core. To solve this problem, a neural network control scheme is adopted for the same system. Firstly, a nonlinear MSBR model is taken and after linearizing a single input single output (SISO) system is constructed out of it. Radial basis function neural network (RBFNN) has strong tolerance to input noise, online learning ability and good generalization. Therefore, RBFNN controller is used for controlling the core power of the MSBR. The conventional RBFNN dominates in the neural active region. Compact RBF networks has been designed by using newly developed algorithm. By using Lyapunov stability criterion, it has proved that the variation of core power is bounded and it may be comparable to any traditional neural networks or fuzzy interference system. Results of the study reflect the efficiency of RBFNN controller for controlling power of MSBR core.

Improved precise guidewire delivery of a cardiovascular interventional surgery robot based on admittance control.

The development of cardiovascular interventional surgery robots can realize master-slave interventional operations, which will effectively solve the problem of surgeons being injured by X-ray radiation. The delivery accuracy and safety of interventional instruments such as guidewire are the most important issues in the development of robotic systems. Most of the current control methods are position control or force feedback control, which cannot take into account delivery accuracy and safety. A cardiovascular interventional surgery robotic system integrated force sensors is developed. A novel force/position controller, which includes a radial basis function neural networks-based inner loop position controller and a force-based admittance outer loop controller, is proposed. Furthermore, a series of simulations and vascular model experiments are carried out to demonstrate the feasibility and accuracy of the proposed controller. The designed cardiovascular interventional robot is flexible to enter the target vessel branch. Experimental results indicate that the proposed controller can effectively improve the delivery accuracy of the guidewire and reduce the contact force with the vessel wall. The proposed controller based on radial basis function neural network and admittance control is effective in improving delivery accuracy and reducing contact force. The algorithm needs to be further validated in vivo experiments.

Trajectory tracking control for unmanned helicopter based on RBFNN

This paper proposes a nonlinear adaptive radial basis function neural network (RBFNN) control method for Unmanned Helicopter. The control objective is to reduce the trajectory tracking error of the unmanned helicopter in the presence of system uncertainty and external disturbances. Based on the backstepping control technique, the controller of the unmanned helicopter are designed in order of position kinematics, position dynamics, attitude kinematics and attitude dynamics, and the block processing is used in the last three steps above.Then, RBFNN is added to compensate the error caused by external interference. Meanwhile, the derivatives of virtual controls are obtained by second-order command filter, and the stability of the system is analyzed by Lyapunov stability theorems. Finally, the simulation results verify the stability and robustness of the control method.

Spacecraft Attitude Measurement and Control Integration Using a Novel Configuration of Variable Speed Magnetically Suspended Control and Sensing Gyroscope

For the sake of achieving the high-accuracy and high-bandwidth attitude measurement and control integration of flexible spacecraft, an integrated method based on a novel configuration of Variable Speed Magnetically Suspended Control and Sensitive Gyroscope (VSMSCSG) and Radial Basis Function (RBF) neural network control is proposed in this paper. First, the structure and principle of a novel VSMSCSG configuration are given, and its modelling is established. VSMSCSGs are employed as both actuators and attitude-rate sensors. Second, the attitude measurement method based on the configuration is proposed in the case of rotor spin speed and deflection angle changing. Based on the characteristics of the novel configuration, the corresponding RBF neural network attitude control law of spacecraft and the adaptive saturation avoidance steering laws of the VSMSCSGs have been further designed; the rotor can deflect at high frequency and change speed simultaneously to output a high bandwidth attitude control moment which dramatically improves the moment output capability of VSMSCSG configuration. Compared with the traditional method of attitude measurement and control integration, the proposed one not only improves the speed and accuracy of attitude control under high bandwidth disturbance but also greatly improves the accuracy of attitude measurement by fully using the dynamic information of novel configuration. Semiphysical simulation results demonstrate the correctness and superiority of the proposed method.

Adaptive Neural Control of Nonlinear Cyber–Physical Systems Against Randomly Occurring False Data Injection Attacks

In this article, the main contribution is to introduce the adaptive backstepping technique into nonlinear cyber–physical systems against randomly occurring false data injection (ROFDI) attacks. And a new defense strategy based on nonlinear disturbance observer (NDO) is developed, which can not only effectively estimate the external compound disturbance in the presence of the attack, but also improve the robustness of the controlled system. Different from FDI attacks, it is a special case of ROFDI attacks, and the proposed method can deal with ROFDI attacks injected by attackers. Meanwhile, multiple Nussbaum functions are introduced, which overcomes the design difficulty of unknown control directions caused by ROFDI attacks. Furthermore, the approximation of the unknown nonlinear function and the exponential growth problem in the traditional backstepping calculation process are handled by radial basis function neural network and dynamic surface control, respectively. Finally, a new adaptive neural control method based on NDO is proposed to make all signals bounded. Meanwhile, tracking errors and disturbance estimation errors converge on the neighborhood of zero. Numerical and practical examples further illustrate the rationality of the method.

Event-triggered adaptive target tracking control for an underactuated autonomous underwater vehicle with actuator faults

In this paper, the target tracking control problem is investigated for an underactuated autonomous underwater vehicle (AUV) in the presence of actuator faults and external disturbances based on event-triggered mechanism. Firstly, the five degrees-of-freedom kinematic and dynamic models are constructed for an underactuated AUV, where the backstepping method is introduced as the major control framework. Then, radial basis function neural network (RBFNN) and adaptive control method are made full use of estimating and compensating the influences of uncertain information and actuator faults. Besides, the relative threshold event-triggered strategy is integrated into the tracking control to further reduce communication burden from the controller to the actuator. Moreover, through Lyapunov analysis, it is proved that the designed controllers guarantee that the tracking error variables of the underactuated AUV are uniformly ultimately bounded and can converge to a small neighborhood of the origin. Finally, the effectiveness and reasonableness of the designed tracking controllers are illustrated by comparative simulations.

Deterministic learning-based neural network control with adaptive phase compensation

Under the persistent excitation (PE) condition, the real dynamics of the nonlinear system can be obtained through the deterministic learning-based radial basis function neural network (RBFNN) control. However, in this scheme, the learning speed and accuracy are limited by the tradeoff between the PE levels and the approximation capabilities of the neural network (NN). Inspired by the frequency domain phase compensation of linear time-invariant (LTI) systems, this paper presents an adaptive phase compensator employing the pure time delay to improve the performance of the deterministic learning-based adaptive feedforward control with the reference input known a priori. When the adaptive phase compensation is applied to the hidden layer of the RBFNN, the nonlinear approximation capability of the RBFNN is effectively improved such that both the learning performance (learning speed and accuracy) and the control performance of the deterministic learning-based control scheme are improved. Theoretical analysis is conducted to prove the stability of the proposed learning control scheme for a class of systems which are affine in the control. Simulation studies demonstrate the effectiveness of the proposed phase compensation method.

Neural adaptive with impedance learning control for uncertain cooperative multiple robot manipulators

In this paper, an adaptive radial basis function neural network (RBFNN) control scheme is studied for the desired tracking of multiple robot manipulators with carrying a common object in joint-space. First, a non-zero time-varying parameter is introduced into the RBFNN, as well as a novel universal approximation of RBFNN with this non-zero parameter is introduced. Second, a switching control scheme based on this approximation property is designed. When the states of uncertainties terms leave the universal approximation domains, the suggested adaptive laws established by a sliding surface would pull them back, then the uncertain nonlinear functions are approximated by the RBFNNs in the domains. With this new structure of control technique, the limitation of a finite universal approximation can be broken. Third, the cooperative multiple manipulators can track the desired trajectory determined through impedance learning, which is used to modulate the control input in order to optimize the environment robot interaction. Finally, simulation results are shown to demonstrate the effectiveness in terms of expected tracking performance and position tracking errors constraints.

Design of Three-Dimensional Intelligent Guidance Law for Intercepting Highly Maneuvering Target

This paper investigates the three-dimensional guidance and control problem of missile intercepting highly maneuvering target, whose acceleration information is difficult to accurately predict. With the three-dimensional guidance model for intercepting single target established by using the principle of zeroing the rate of line-of-sight (LOS), a novel intelligence guidance law has been designed through backstepping sliding mode control method, radial basis function (RBF) neural network and adaptive control technique. Then, a Lyapunov-based stability analysis demonstrates that all the signals are bounded, and the LOS rates ultimately converge to a neighborhood of the origin. Following advantages are highlighted in this paper: (i) the target information is online estimated and compensated by the RBF neural network, which indicates that the proposed guidance law is easily put into practice only relying on the position information of target. (ii) an adaptive gain term is designed in the control system, which greatly reduces the inherent chattering of sliding mode method. At last, simulations are conducted, and results illustrate the effectiveness and superiority of the designed guidance law.

A Hybrid Adaptive Controller Applied for Oscillating System

In this paper, a hybrid PI radial basis function neural network (RBFNN) controller is used for a plant with significant disturbances related to the mechanical part of the construction. It is represented through a two-mass system. State variables contain additional components—as a result, oscillations affect the precision of control. Classical solutions lead to movements of the poles of the whole control structure. However, proper tuning of the controller needs detailed identification of the object. In this work, the neural network is implemented to improve the classical PI controller’s performance and mitigate the errors generated by oscillations of the mechanical variables and parametric uncertainties. The proposed control strategy also guarantees the closed-loop stability of the system. The mathematical background is firstly presented. Afterward, the simulation results are shown. It can be stated that the results are very promising, and a significant improvement in oscillations damping is achieved. Finally, experimental tests are conducted to substantiate the obtained simulation results. For this purpose, the algorithm was implemented in the dSPACE card. Achieved transients confirm the numerical tests.

Nonlinear control of wind turbine in above rated wind speed region

Abstract Advanced control of variable speed horizontal wind turbine was considered in the high wind speed range. The aims of control in this region are to limit and stabilize the rotor speed and electrical power to their nominal values, while reducing the fatigue loads acting on the structure. A new nonlinear technique based on combination between sliding mode control and radial basis function neural network control was investigated. The proposed hybrid controller was implemented via MATLAB on a simplified two masses numerical model of wind turbine. By applying the Lyapunov approach, this controller was shown to ensure stability. It was found also to be robust and able to reject the uncertainties associated to system nonlinearities. The obtained results were compared with those provided by an existing controller.

Adaptive fault-tolerant control for stratospheric airships with full-state constraints, input saturation, and external disturbances

This study addresses the trajectory tracking problem of a stratospheric airship subject to actuator faults, full-state constraints, input saturation, and unknown external disturbances. Improved airship state-space equations are obtained by simultaneously considering actuator faults, input saturation, and external disturbances. An adaptive fault-tolerant controller with a neural network (AFTCNN) was proposed with backstepping as the main framework. A tan-type barrier Lyapunov function (TBLF) and auxiliary systems are combined to address the full-state constraints and input saturation. Adaptive fault-tolerant and radial basis function neural network (RBFNN) control laws are proposed to estimate the actuator faults, and to approximate the disturbances, respectively. Stability analysis indicates that the tracking error converges to a neighborhood of zero; the error estimation of actuator faults and disturbances is bounded, and the state constraints are never violated. The standard backstepping method with and without RBFNN, and an adaptive fault-tolerant controller with and without RBFNN are compared in the numerical simulation. The results verify that the proposed method is superior to other methods in terms of trajectory-tracking performance and estimation of actuator faults and external disturbances, with smaller forces and torques to track the trajectory.

Combined RBFN based MPPT and d-axis stator current control for permanent magnet synchronous generators

This paper proposes a new radial basis function neural network maximum power point tracking controller based on a differential evolution algorithm for machine side converter of permanent magnet synchronous generator wind turbine under variable wind speed. Direct axis stator current control methods of permanent magnet synchronous machine are reviewed shortly. A combined radial basis function neural network-based network maximum power point tracking method and d axis stator current control techniques including zero d axis stator current, unity power factor, and constant stator flux-linkage have been implemented to control the machine side converter of permanent magnet synchronous generator wind turbine. The dynamic performance of the proposed approach is assessed under different operating conditions through a simulation model based on MATLAB. It has been seen that the radial basis function neural network controller can not only track well the maximum power point but also can be reduced costly.

Observer-based adaptive neural network control for PEMFC air-feed subsystem

Oxygen excess ratio (OER) tracking of polymer electrolyte membrane fuel cell (PEMFC) air-feed subsystem is an interesting topic to obtain a high energy conversion ratio. The control purpose is challenging due to the unknown nonlinear function, time-varying external disturbance and unmeasured variable. To deal with the above problems, a neural network control scheme based on tracking error observer and disturbance observer is proposed. Firstly, a canonical form of the PEMFC air-feed subsystem is developed based on the input–output feedback linearization method. Meanwhile, a tracking error observer which only uses the system output is developed for the reconstruction of the unmeasured variable. Secondly, the neural network based on a novel adaptive learning law is proposed to approximate the unknown nonlinear function. An identification model with a second-order low-pass filter is designed such that the adaptive learning law is adjusted by the observer error and modeling error, simultaneously. Finally, to reduce the effect caused by compound disturbance including the external disturbance and neural network approximation error, a nonlinear disturbance observer is proposed. The main contribution of this study is not only avoiding the prior knowledge of system uncertainty, external disturbance and the unmeasured variable but also improving the OER tracking performance under the measurement noise. Lyapunov theory analysis shows that the system tracking error is uniformly ultimately bounded. Numerical simulations and hardware-in-loop (HIL) experiments show that the proposed neural network control algorithm can maintain OER at the target value even under external disturbance. The experimental results show that the performance indexes including the mean absolute error (MAE), the root mean square error (RMSE) and the standard deviation (SD) of the proposed controller during the whole process are 0.0213, 0.0547 and 0.0538, respectively. Compared with the proportional–integral–derivative (PID) controller (MAE 0.0466, RMSE 0.0762 and SD 0.0745), robust adaptive radial basis function neural network (RBFNN-H∞) controller (MAE 0.0841, RMSE 0.0839 and SD 0.0813) and hybrid robust adaptive radial basis function neural network (HARBFNN-1st) controller (MAE 0.0255, RMSE 0.0613 and SD 0.0603), the proposed controller performs a better performance.

Frequency tracking control of the WPT system based on fuzzy RBF neural network

With the application of electrical equipment, magnetically coupled resonant (MCR) wireless power transfer (WPT) technology has become an effective means to improve equipment intelligence. The MCR-WPT system is a loosely coupled system, and the resonant frequency may be split or detuned due to the changes of load or transferring distance, resulting in the system transfer efficiency (TE) greatly reduced. To solve the problems of limited speed and accuracy in the existing frequency tracking methods, this paper analyzes the relation between the detuning rate and the system TE, proposing an adaptive frequency tracking control method based on fuzzy radial basis function neural network control. The neural network outputs proportion–integration–differentiation parameters to adjust the inverter drive circuit, and the frequency of inverter drive circuit is adjusted nonlinearly in real time to ensure the accurate frequency tracking of the MCR-WPT system. The simulation and experimental results show that the proposed method can enhance the tracking ability of the resonant frequency, and effectively improve the system TE.