On-line Fuzzy Neural Network Research Articles

Adaptive hierarchical sliding mode control based on fuzzy neural network for an underactuated system

We present an adaptive hierarchical sliding mode control based on fuzzy neural network for a class of underactuated systems to solve the problem of high-precision trajectory tracking. This system is viewed as several subsystems. One subsystem is used to design the first-layer sliding surface, which constructs the second-layer sliding surface with another subsystem. When the top layer includes all the subsystems, the design process is finished. Meanwhile, the equivalent control law and the switching control law are achieved at every layer. Because the hierarchical sliding mode control law relies excessively on the requirement of detailed information of the underactuated dynamic system, and because that method causes an inevitable chattering phenomenon, an online fuzzy neural network system is applied to mimic the hierarchical sliding mode control law. Moreover, the bounds of system uncertainties and modeling error caused by the fuzzy neural network system are estimated online by a robust term. The stability of the closed-loop system is guaranteed based on the Lyapunov theory and Barbalat’s Lemma. Finally, the examples, a single-pendulum-type overhead crane system and an inverted pendulum system, are simulated to verify the effectiveness and robustness of the proposed method compared with some conventional methods.

Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

Robust fuzzy neural network sliding mode control scheme for IPMSM drives

This article proposes a robust fuzzy neural network sliding mode control (FNNSMC) law for interior permanent magnet synchronous motor (IPMSM) drives. The proposed control strategy not only guarantees accurate and fast command speed tracking but also it ensures the robustness to system uncertainties and sudden speed and load changes. The proposed speed controller encompasses three control terms: a decoupling control term which compensates for nonlinear coupling factors using nominal parameters, a fuzzy neural network (FNN) control term which approximates the ideal control components and a sliding mode control (SMC) term which is proposed to compensate for the errors of that approximation. Next, an online FNN training methodology, which is developed using the Lyapunov stability theorem and the gradient descent method, is proposed to enhance the learning capability of the FNN. Moreover, the maximum torque per ampere (MTPA) control is incorporated to maximise the torque generation in the constant torque region and increase the efficiency of the IPMSM drives. To verify the effectiveness of the proposed robust FNNSMC, simulations and experiments are performed by using MATLAB/Simulink platform and a TI TMS320F28335 DSP on a prototype IPMSM drive setup, respectively. Finally, the simulated and experimental results indicate that the proposed design scheme can achieve much better control performances (e.g. more rapid transient response and smaller steady-state error) when compared to the conventional SMC method, especially in the case that there exist system uncertainties.

A Design of Fuzzy Neural Network Control with Decoupling for Ball Mill System

An on-line fuzzy neural network control (FNNC) with decoupling unit is applied for ball mill system of blast furnace. The structure of FNNC is composed of premise and consequence part. Premise parameters reduce fuzzy rules to solve the combination explosion of multi-inputs. Consequence parameters achieve a good transient response. An adaptive step size equation created by a Lyapunov function guarantees convergence of algorithm. Neural network decoupler based on timedomain response curve is used to counter the interaction among channels. Simulation results prove effectiveness of proposed control system.

Enhancing the Autonomy of Teleoperated Redundant Manipulators Through Fusion of Intelligent Control Modules

This paper presents a method for redundancy resolution of an industrial manipulator in a teleoperated force control task. A seven degree-of-freedom (DOF) industrial manipulator manufactured by the Mitsubishi Heavy Industries Ltd. is used for experiments. The task involves obeying a force command sent from a remote computer while autonomously adapting the posture to avoid unexpected obstacles moving toward the manipulator. Redundancy resolution is employed for autonomous adaptation of the configuration to avoid the obstacle while continuing the force control task. This self-adaptive skill on the slave manipulator side is very important because teleoperation is often performed in dangerous or partially unknown environments where unexpected changes such as moving obstacles can well be expected. In such situations, the control ability of the master side is very limited due to the practical limitations of vision sensors to capture a comprehensive view of the environment and the limitations of the degrees of freedom on the master manipulator. The proposed method relies on two modules of an intelligent controller on the slave side. The first is an on-line fuzzy neural network (FNN) for intelligent force control, and the second is a configuration controller that works in harmony with the first to exploit redundancy to react to avoid moving obstacles such that the latter does not inhibit the progress of the former. The second controller generates joint velocity commands in null space of the hand Jacobian, so that its activation does not affect the force controller. Here we show that the proposed method can skillfully avoid a moving obstacle without stopping the force control task. This skillful adaptation ability can significantly improve the efficiency and safety of teleoperated force control tasks with less burden on the master side. This paper presents some promising experimental results to demonstrate the effectiveness of the proposed method.

On-line tool wear estimation in CNC turning operations using fuzzy neural network model

In recent past, several neural network models which employ cutting forces and AErms or their derivatives for estimation as well as classification of flank wear have been developed. However, a significant variation in mean cutting forces and AErms at the start of cutting operation for similar new tools can result in estimation and classification error. In order to deal with this problem, a new on-line fuzzy neural network (FNN) model is presented in this paper. This model has four parts. The first part of the model is developed to classify tool wear by using fuzzy logic. The second part of this model is designed for normalizing the inputs for the next part. The third part consisting of modified least-square backpropagation neural network is built to estimate flank and crater wear. The development of forth part was done in order to adjust the results of the third part. Several basic and derived parameters including forces, AErms, skew and kurtosis of force bands, as well as the total energy of forces were employed as inputs in order to enhance the accuracy of tool wear prediction. The experimental results indicate that the proposed on-line FNN model has a high accuracy for estimating progressive flank and crater wear with small computational time.