Fuzzy Neural Network Control Scheme Research Articles

Direct yaw-moment control of vehicles based on phase plane analysis

In view of the problems related to vehicle-handling stability and the real-time correction of the heading direction, nonlinear analysis of a vehicle steering system was carried out based on phase plane theory. Subsequently, direct yaw-moment control (DYC) of the vehicle was performed. A four-wheel, seven-degree-of-freedom nonlinear dynamic model that included the nonlinear characteristics of the tire was established. The stable and unstable regions of the vehicle phase plane were divided, and the stable boundary model was established by analyzing the side slip angle–yaw rate ([Formula: see text]) and side slip angle–side slip angle rate [Formula: see text] phase planes as functions of the vehicle state variables. In the unstable region of the phase plane, taking the instability degree as the control target, a fuzzy neural network control strategy was utilized to determine the additional yawing moment of the vehicle required for stability restoration, which pulled the vehicle back from an unstable state to the stable region. In the stable region of the phase plane, a fuzzy control strategy was utilized to determine the additional yawing moment so that the actual state variables followed the ideal state variables. In this way, the vehicle responded rapidly and accurately to the steering motion of the driver. A simulation platform was established in MATLAB/Simulink and three working condition was tested, that is, step, sine with dwell, and sine amplification signals. The results showed that the vehicle handling stability and the instantaneous heading-direction adjustment ability were both improved due to the control strategy.

Adaptive Type-2 Fuzzy Neural-Network Control for Teleoperation Systems With Delay and Uncertainties

Interacting with human operators, remote environment, and communication networks, teleoperation systems are considerably suffering from complexities and uncertainties. Managing these is of paramount importance for safe and smooth performance of teleoperation systems. Among the countless solutions developed by researchers, type-2 fuzzy (T2F) algorithms have shown an outstanding performance in modeling complex systems and tackling uncertainties. Moreover, artificial neural networks (NNs) are well known for their adaptive learning potentials. This article proposes an adaptive interval type-2 fuzzy neural-network control scheme for teleoperation systems with time-varying delays and uncertainties. The T2F models are developed based on the experimental data collected from a teleoperation setup over a local computer network. However, the resulted controller is evaluated on an intercontinental communication network through the Internet between Australia and Scotland. Moreover, the slave robot and the remote workspace are completely different and unforeseen. Stability and performance of the proposed control is analyzed by Lyapunov–Krasovskii method. Comprehensive comparative studies demonstrate that the proposed controller outperforms traditional techniques in experimental evaluations.

Research on the driving control strategy for ISG hybrid electric vehicles

ABSTRACT To overcome such Hybrid Electric Vehicle (HEV) engine problems as high fuel consumption and poor economical efficiency when working in low-efficiency areas, this paper intends to establish the ISG light hybrid vehicle structure-based mathematical models of engines, electric motors, batteries and the whole vehicle power output first; to analyse the relationship between the engine load rate and the optimal engine fuel consumption characteristics, and then to take into consideration the performance characteristics of the motor battery output efficiency, so as to come up with the switching pattern of the ISG parallel hybrid vehicle operating mode. According to the switching rules, the primary influence parameters of the engine load rate as well as the State of Charge (SOC) were extracted and a fuzzy neural network control strategy for torque management was developed. Under the Matlab/Advisor joint simulation platform, a simulation model was established and the effectiveness of the fuzzy control strategy was verified under the typical state of cyclic operation NEDC. The simulation results indicate that the adoption of fuzzy neural network control strategy for torque management can lead to considerable advancements in engine operating efficiency–the fuel economical efficiency can be improved by about 12%.

The fuzzy neural network control scheme with H tracking characteristic of space robot system with dual-arm after capturing a spin spacecraft

In this paper, the dynamic evolution for a dual-arm space robot capturing a spacecraft is studied, the impact effect and the coordinated stabilization control problem for post-impact closed chain system are discussed. At first, the pre-impact dynamic equations of open chain dual-arm space robot are established by Lagrangian approach, and the dynamic equations of a spacecraft are obtained by Newton-Euler method. Based on the results, with the process of integral and simplify, the response of the dual-arm space robot impacted by the spacecraft is analyzed by momentum conservation law and force transfer law. The closed chain system is formed in the post-impact phase. Closed chain constraint equations are obtained by the constraints of closed-loop geometry and kinematics. With the closed chain constraint equations, the composite system dynamic equations are derived. Secondly, the recurrent fuzzy neural network control scheme is designed for calm motion of unstable closed chain system with uncertain system parameter. In order to overcome the effects of uncertain system inertial parameters, the recurrent fuzzy neural network is used to approximate the unknown part, the control method with H&#x221E tracking characteristic. According to the Lyapunov theory, the global stability is demonstrated. Meanwhile, the weighted minimum-norm theory is introduced to distribute torques guarantee that cooperative operation between manipulators. At last, numerical examples simulate the response of the collision, and the efficiency of the control scheme is verified by the simulation results.

Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation

In this paper, a fuzzy neural network controller for regulating demand-side thermostatically controlled loads (TCLs) is designed with the aim of stabilizing the frequency of the smart grid. Specifically, the balance between power supply and demand is achieved by tracking the automatic generation control (AGC) signal in an electric power system. The particle swarm optimization (PSO) and error back propagation (BP) algorithms are used to optimize the control parameters and consequently reduce the tracking errors. The fuzzy neural network can be applied to solve load control problems in power systems, since its self-learning and associative storage functions can deal with the highly nonlinear relationship between input and output. Simulation results show the advantage of the fuzzy neural network control scheme in terms of frequency regulation error and consumer comfort.

Data-driven-based adaptive fuzzy neural network control for the antimony flotation plant

Due to the unknown system structure of the froth flotation process and frequent fluctuations in production conditions, design of control strategy is a challenging problem. As a result, manual operation is still widely applied in practice by observing froth image features. However, since the manual observation is subjective and the production conditions are time-varying, the manual operation cannot make decisions quickly and accurately. In this paper, a data-driven-based adaptive fuzzy neural network control strategy is developed to implement the automatic control of the antimony flotation process. The strategy is composed of fuzzy neural network (FNN) controllers, a data-driven model, and an on-line adaptive algorithm. The FNN is constructed to derive the control laws of the reagent dosages. The parameters of the FNN controllers are tuned by gradient descent algorithm. To obtain the real-time error feedback information, the data-driven model is established, which integrates the long short term memory (LSTM) network and radial basis function neural network (RBFNN). The LSTM network is utilized as a primary model, and the RBFNN is used as an error compensation model. To handle the challenges of the frequent fluctuations in the production conditions, the on-line adaptive algorithm is proposed to tune the parameters of the FNN controllers. Simulations and experiments are carried out in a real-world antimony flotation plant in China. The results demonstrate that the proposed adaptive fuzzy neural network control strategy produces better control performance than the other two existing methods.

Main steam temperature control based on GA-BP optimised fuzzy neural network

The high inertia and long time-delay characteristics of main steam temperature control system in a thermal power plant will reduce the system control performance. In order to solve this problem, a genetic algorithm-back propagation (GA-BP) optimised fuzzy neural network control strategy is proposed in this paper. Gauss function is chosen as membership function and fuzzy neural network is designed. GA combined with BP algorithm is chosen for the offline parameters optimisation of fuzzy neural network, and then BP algorithm is used for online parameters optimisation. GA-BP optimisation algorithm overcomes the shortcomings of GA algorithm or BP algorithm which is used to adjust the parameters of fuzzy neural network controller. The simulation experiment compared with cascade PID and fuzzy neural network is carried out. Simulation results show that the controller based on GA-BP optimised fuzzy neural network has faster response speed, smaller overshoot and error, better tracking performance, and reduces the lag effect of the control system under different load, working conditions and membership functions.

Performance Simulation on the Secondary Hydraulic Lifting System of the Excavator Bucket

The flow coupled secondary hydraulic lifting system of the excavator bucket was presented, also its structure and working principle. The fuzzy-neural network control strategy which was combination of fuzzy control and neural network has been adopted, and the math model of the lifting system is established, then the simulations of the performance of the lifting system has been conducted. The flow coupled hydraulic system with secondary regulation can regenerate gravitational energy of load. By controlling the hydraulic accumulator, the process controlling of the energy recycling and reusing has been realized. The system pressure range was expanded, and the energy reusing effect and efficiency were improved.

Fuzzy Neural Network Control Strategy of a Wind Generation and Flywheel Energy Storage Sensor Combined System

Fuzzy Neural Network Control Strategy of a Wind Generation and Flywheel Energy Storage Sensor Combined System

Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller

This paper presents a fuzzy neural network (FNN) control system to automatically manage the hemodynamic variables of patients with hypertension and congestive heart failure (CHF) via simultaneous infusion of cardiac drugs such as vasodilators and inotropic agents. The developed system includes two FNN sub-controllers for regulating cardiac output (CO) and mean arterial pressure (MAP) by cardiac drugs, considering interactive pharmacological effects. The adaptive FNN controller was tested and evaluated on a cardiovascular model. Six short-term therapy conditions of hypertension and CHF are presented under different sensitivities of a vasodilator drug. The results of the automated system showed that root mean square errors were ≤ 5.56 mmHg and ≤ 0.22 L min-1 for regulating MAP and CO, respectively, providing short settling time responses of MAP (≤ 10.9 min) and CO (≤ 8.22 min) in all therapy conditions. The proposed FNN control scheme can significantly improve the performance of cardiac drug infusion System.

Synchronization of uncertain chaotic systems based on adaptive type-2 fuzzy sliding mode control

A novel direct adaptive interval type-2 fuzzy neural network (FNN) controller in which linguistic fuzzy control rules can be directly incorporated into the controller is developed to synchronize chaotic systems with training data corrupted by noise or rule uncertainties involving external disturbances, in this paper. By incorporating direct adaptive interval type-2 FNN control scheme and sliding mode approach, two non-identical chaotic systems can be synchronized based on Lyapunov stability criterion. Moreover, the chattering phenomena of the control efforts can be reduced and the external disturbance on the synchronization error can be attenuated. The stability of the proposed overall adaptive control scheme will be guaranteed in the sense that all the states and signals are uniformly bounded. From the simulation example, to synchronize two non-identical Chua’s chaotic circuits, it has been shown that type-2 FNN controllers have the potential to overcome the limitations of tpe-1 FNN controllers when training data is corrupted by high levels of uncertainty.

Intelligent Control of Dynamic Performance of Vehicle Electric Power Steering and Active Suspension Integrated System

Aiming at complexity, uncertainty and non-linearity of electric power steering and active suspension integrated system, the grey prediction fuzzy neural network control strategy of dynamic performance of integrated system is proposed based on the grey prediction theory, fuzzy control theory and neural network control theory. The dynamic response of integrated system is studied. The integrated system controller with chip microprocessor LPC2138 as kernel is designed. Based on the simulation, the real vehicle road test is done. The results show that the grey prediction fuzzy neural network control strategy can carry out real time coordinated control of the integrated system of electric power steering and active suspension, the contradiction between the ride comfort and the handling stability is alleviated and the comprehensive performance is improved.

GA-based control to swing up an acrobot with limited torque

To swing up an acrobot, a two-link under-actuated robotic system, a fuzzy neural network (FNN) controller with limited torque based on genetic algorithm (GA) is presented and tested. The acrobot is composed of two links rotating in a same plane around two joints, only the second of which is actuated. The upswing process is a complex problem to rotate the two links from the low to the top balance position by single torque on the actuated joint. In a real application system, motor output is often limited. Therefore, an FNN controller is proposed to generate limited torque on the actuated joint according to different states of the two links. The antecedents and consequences of the FNN controller are determined with an improved GA. Simulation results on the acrobot system verify the feasibility and generalization of the proposed GA-based FNN control scheme.

Intelligent actuation strategy for a three-DOF four-wire type optical pickup

This study is dedicated to design a fuzzy neural network (FNN) control scheme for performing precision positioning of a particularly-designed four-wire-suspended lens actuator (often called “pickup”), which is capable of moving not only in the conventional translational degrees of freedom (DOFs)—focusing and tracking, but also in the rotational DOF of tilting. This additional tilting DOF provides the capability of the pickup to suppress the non-zero tiling angle of the pickup, which is caused by uneven magnetic fields and other factors of manufacturing/assembling tolerance. The FNN scheme aims to successfully control the aforementioned three-DOFs of the pickup simultaneously for achieving fast focusing, tracking and suppressing non-zero-tilting simultaneously. The design of this FNN control scheme is started with an establishment of a electro-dynamic model of the pickup with considerations of dimensional/assembling errors as parametric uncertainty and unbalanced radial vibrations as disturbance. Three independent FNN schemes are synthesized next to accomplish fast tracking, focusing and zero-tilting simultaneously. Numerical simulations with considering practical system parameters for four different cases with/without the tilting degree and/or uncertainty and disturbance are conducted to verify the effectiveness of the FNN schemes designed. Simulated results show that the designed FNN control scheme is capable of achieving fast zero-tilting with favorable tracking/focusing performances and also robust to unbalanced radial vibrations and assumed plant uncertainty.