Adaptive Fuzzy Neural Network Control Research Articles

A Kind of Electro-hydraulic Servo System Cooperative Control Simulation: An Experimental Research

Background: The single position control or force control of the electro-hydraulic servo system has long been unable to meet the actual needs of production and life. To achieve a good control effect and solve various problems in real life, people no longer only consider a single position control or force control. Objective: The proposed method provides technical support for multi-objective collaborative control of the electro-hydraulic servo system, completes the requirements of multitasking operation, improves the positioning accuracy and response velocity of the electro-hydraulic servo system, and realizes the synergy between the position and force. Methods: A control method of the outer loop control of force as feedforward compensation is adopted to realize the coordinated control of force and position based on position control. The position control part adopts the PID control algorithm to ensure real-time response accuracy and positioning accuracy. The outer force loop uses an adaptive fuzzy neural network control algorithm to reduce vibration and shock caused by system instability and nonlinear factors during the force control process. Results: The control effect of the designed position/force collaborative controller is verified by simulation and experiment. It can effectively reduce the vibration and impact caused by the contact force during the working process of the system, at the same time greatly improving the accuracy of position control and improving the stability of the system. Conclusion:: The designed force/position collaborative control system has a good overall control effect.

An Adaptive Fuzzy Neural Network Based On Progressive Gaussian Approximate Filter with Variable Step Size

The nonlinear filtering problem is a hot spot in robot navigation research. As an advanced method that can effectively improve the robustness and accuracy of the system, the progressive Gaussian approximate filter with variable step size (PGAFVS) still has some shortcomings, how to resolve the nonlinear filtering problem in the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy, which becomes the purpose of this paper. This paper formulates the processing of trajectory tracking measurement noise problem as a Kalman filtering procedure and the measurement noise covariance matrix in controller, is jointly estimated based on the progressive Gaussian approximate filter (PGAF), after that, PGAFVS can be deduced. Then we proposed an adaptive fuzzy and backpropagation neural network controller based on PGAFVS (AFNPGA-VS) that can improve the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy. The simulation results show that the proposed algorithm outperforms the state-of-the-art methods.

Observer-based self-organizing adaptive fuzzy neural network control for non-linear, non-affine systems with unknown sign of control gain and dead zone: A case study of pneumatic actuators

In this paper, an observer-based self-organizing adaptive fuzzy neural network (OSOAFNN) control for non-linear, non-affine systems with the unknown sign of control gain and dead zone is presented. First, a reverse dead-zone compensator scheme to cope with the impact of dead-zone phenomenon existing in control input is investigated. Then, an observed-based control approach to address immeasurable states of the system is proposed, utilizing this approach all states of the system are not needed to be available. A self-organizing fuzzy neural network (SOFNN) technique is presented to approximate the non-linear and unknown function of the observer error dynamics. The proposed fuzzy neural network (FNN) model benefits from two main advantages: (1) the number of rules is automatically generated or pruned and (2) the parameters of antecedent and consequent part of SOFNN are updated through the hybrid tuning, simultaneously. Furthermore, the control law contains a Nussbaum function which deals with the unknown sign of control gain. As the system states are immeasurable, the strictly positive real (SPR) Lyapunov function to guarantee the closed-loop system stability, and tracking error convergence to zero is employed, as well as the boundedness of control parameters are assured through a projection law merged with adaptive law. Finally, the controller is practically implemented on a non-linear, non-affine pneumatic system with unknown dead zone which exploits just a sensor for output measuring. Experimental results show that the proposed controller has satisfactory performance in tracking different trajectories, and tracking error for desired signal case I and case II is limited to [−1.5, 1.5] and [−1,1]mm, respectively.

Novel Adaptive Fuzzy Feedback Neural Network Controller for Narrowband Active Noise Control System

With the development of science and technology in recent years, many operating machines have become sources of noise affecting quality of life. Hence, the topic of noise diminution using an active noise control (ANC) system has attracted many researchers. This paper develops a new adaptive fuzzy feedback neural network controller (AFFNNC) to improve the performance for narrowband active noise control (NANC) systems. The proposed controller combines fuzzy inference and adaptive feedback neural network controllers that are based on the filtered-s least mean square (FSLMS) algorithm. The AFFNNC comprises five network layers, in which the output layer of the controller uses an adaptive algorithm to tune directly the parameters of filters without prior training. The computational complexity, convergence and stability of the AFFNNC are analyzed. Evaluations are performed on both linear and nonlinear NANC systems with a recorded noise signal that was obtained from a transformer. Numerical simulations confirm the efficiency of the proposed controller compared with other ANC controllers.

Neural Network Identification and Direct Adaptive Fuzzy Neural Network (DAFNN) Controller for Unknown Nonlinear Non-affine Pneumatic Servo System

In this paper, a direct adaptive fuzzy neural network (DAFNN) controller for trajectory tracking control of the non-linear non-affine pneumatic servo system is presented. First, using a neural network identifier, the non-linear dynamics of a real pneumatic servo system is simulated. By comparing the output of the neural network and the output of the experimental setup, it is observed that the non-linear pneumatic actuator system is well-identified using neural networks. By incorporating the Lyapunov stability theorem, the adaptive laws for the parameters of the controller are obtained, parameter boundedness and stability of the closed-loop system are guaranteed. Finally, practical results are successfully implemented for trajectory tracking control of the pneumatic servo system, in which the effect of the simultaneous updating of the antecedent and consequent’s parameters of the fuzzy neural network controller has been investigated. The tracking error ±1.3mm and ±1 mm for proposed updating method compared to ±2.5mm and ±3.5mm, for a case that the weigh parameters are merely adjusted, are obtained. The results indicate the proposed adjustment method improves the performance of the controller in the presence of unknown nonlinearities and dynamics uncertainty.

Adaptive fuzzy neural network control for a space manipulator in the presence of output constraints and input nonlinearities

Space manipulator is considered as one of the most promising technologies for future space activities owing to its important role in various on-orbit serving missions. In this paper, a novel adaptive fuzzy neural network (FNN) control scheme is proposed for the trajectory tracking control of an attitude-controlled free-flying space manipulator in the presence of output constraints and input nonlinearities. The parametric uncertainties and external disturbances are also taken into the consideration. First, a model-based controller is designed by using the barrier Lyapunov function (BLF) to prevent the position tracking errors from violating the predefined output constraints. Then, an adaptive FNN controller is designed by using two FNNs to compensate for the lumped uncertainties and input nonlinearities, respectively. Rigorous theoretical analysis for the semiglobal uniform ultimate boundedness of the whole closed-loop system is provided. The proposed adaptive FNN controller can guarantee the position and velocity tracking errors converge to the small neighborhoods about zero, while ensuring the position tracking errors within the output constraints even in the presence of input nonlinearities. To the best of the authors’ knowledge, there are relatively few existing controllers can achieve such excellent control performance in the same conditions. Numerical simulations illustrate the effectiveness and superiority of the proposed control scheme.

Adaptive Type-2 Fuzzy Neural Network Inherited Terminal Sliding Mode Control for Power Quality Improvement

This article proposes an adaptive type-2 fuzzy neural network control system to enhance the performance of power quality improvement. First, the dynamic model of APF with lumped uncertainties caused by parameter perturbation of ac inductor and dc capacitor is briefly introduced. Then, an integral-type terminal sliding mode control (TSMC) is developed for the finite-time reference signal tracking. Meanwhile, in terms of the considered chattering problem, saturation function is utilized in the proposed TSMC. Moreover, an adaptive type-2 fuzzy neural network (T2RFSFNN) is derived to achieve the model-free design, by applying the recurrent feature selection algorithm in the type-2 fuzzy neural network. To enhance the capacity to represent the uncertainties, adaptive learning mechanisms for updating the parameters of T2RFSFNN are derived by the Lyapunov theorem. Furthermore, a robust compensator with an adaptive uncertainty estimation law is investigated to relax the requirement for lumped uncertainties. Finally, the control performance using the developed T2RFSFNN is evaluated by some comparative experimental results.

Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo algorithm

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law; which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques (i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem.

Research on Position / Velocity Synergistic Control of Electro Hydraulic Servo System

Background: In some applications, the requirements of electro-hydraulic servo system are not only precise positioning, but also the speediness capability at which the actuator is operated. Objective: In order to enable the system to achieve rapid start and stop during the work process, reduce the vibration and impact caused by the change of the velocity, at the same time improve the positioning accuracy, and further strengthen the stability and the work efficiency of the system, it is necessary to perform the synergistic control between the position and the velocity of the electrohydraulic servo system. Methods: In order to achieve synergistic control between the position and the velocity, a control method of velocity feed-forward and position feedback is adopted. That is, based on the position control, the speed feed-forward is added to the outer loop as compensation. The position control adopts the PID controller, and the velocity control adopts the adaptive fuzzy neural network controller. At the same time, the position and velocity sensors are used for feedback, and the deviation signals between the position and the velocity obtained by superimposing the feedback are used as the final input of the control object, thereby controlling the whole system. Results: The control effect of the designed position / velocity synergistic controller is verified by simulation and experiment. The results show that the designed controller can effectively reduce the vibration and impact caused by the change of the velocity, and greatly improve the response velocity and the position accuracy of the system. Conclusion: The proposed method provides technical support for multi-objective synergistic control of the electro-hydraulic servo system, completes the requirements of multi-task operation, improves the positioning accuracy and response velocity of the electro-hydraulic servo system, and realizes the synergy between the position and the velocity. In this article, various patents have been discussed.

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.

Sliding-Mode-Control-Theory-Based Adaptive General Type-2 Fuzzy Neural Network Control for Power-line Inspection Robots

In this paper, adaptive general type-2 fuzzy neural network control for motion balance adjusting of a power-line inspection robot is developed. It is used to enhance the anti-interference ability of the controlled plant. General type-2 fuzzy system is adopted because of its ability to more effectively handle uncertainties which may exist as external disturbances and parameter perturbations. The structure of general type-2 fuzzy system is designed by mimicking the neural network. The adaptive laws can be obtained based on the sliding mode control theory. This provides a kind of dynamic general type-2 fuzzy system whose membership functions and consequent parts are changing adaptively. The proposed controller is used to control an under-actuated non-linear power-line inspection (PLI) robot. Different simulation conditions are considered to test the anti-interference ability of the proposed controller. Simulation results indicate that the proposed method can strengthen the PLI robot’s anti-interference ability in a better way as compared to its interval type-2 fuzzy counterpart and PD controller.

Intelligent Rate-Dependent Hysteresis Control Compensator Design With Bouc-Wen Model Based on RMSO for Piezoelectric Actuator

Piezoelectric actuators (PAs) require high precision positioning for the applications of micro electrical mechanical systems, but it exhibits hysteresis nonlinearity which deteriorates positioning accuracy if no proper compensation is given. Hysteresis nonlinear modeling of PAs is a prime choice for hysteresis compensation. This paper proposes a novel intelligent positioning control algorithm based on Bouc-Wen (BW) model for the compensation of a bi-morph type piezoelectric actuator (PA) suffering rate-dependent hysteresis. A region based mixed-species swarm optimization (RMSO) algorithm is proposed for BW modeling to capture the dynamic nonlinearity of a piezoelectric actuator which exhibits rate-dependent hysteresis. Results of numerical simulations have been disclosed to illustrate the performance enhancement of RMSO over classical algorithm while they are applied to the parameter fitting problem of BW model for experimentally acquired datasets. An model based adaptive Fuzzy neural network (Fuzzy-NN) controller of PA is utilized to compensate the hysteresis for the positioning tracking control. Experimental results also illustrate the good performance of the proposed RMSO-BW based control scheme for the hysteresis compensation control of the PA.

Adaptive Backstepping Fuzzy Neural Controller Based on Fuzzy Sliding Mode of Active Power Filter

An adaptive backstepping fuzzy neural network (FNN) controller using a fuzzy sliding mode controller is designed to suppress the harmonics and improve the performance of a shunt active power filter (APF). A backstepping method transforms the APF system into a series of subsystems and uses the virtual control to simplify the controller design. A FNN controller is utilized to approach the nonlinear APF system. An adaptive fuzzy system is employed to adjust the sliding gain to compensate the approximation error of neural network and diminish the chattering. The weights of the fuzzy neural network and fuzzy system parameters are adjusted in real-time to guarantee the asymptotic stability of the designed control system. Simulation and experiemental studies indicate the validitiy of the proposed control method, demonstrating the good compensation performance and strong robustness.

Commercial Vehicle-Based Robust Control of Seated Whole-Body Vibration Using Adaptive Indirect Type-2 Fuzzy Neural Network

Drivers of commercial vehicles are invariably subject to chronic diseases such as back pain as a result of exposure to severe cabin excitations. In this paper, a six degrees of freedom (6-DOF) coupled human-body and seat suspension system is modeled in order to reduce the vibrations transmitted to the head, seat, and the relative seat and cabin floor displacement. The contributions of the present paper are: 1) two significant but inherently conflicting control objectives are employed, namely the seat acceleration and the relative displacement between seat and cabin floor to account for the effect of seat endstops, 2) A novel learning rate gradient descent based neural network approximator algorithm coupled to an adaptive indirect type-2 fuzzy neural network (T2FNN) controller to converge the controller to the ideal parameters of the uncertain model. 3) The controller model takes into account the seat suspension nonlinearities due to the nonlinear asymmetric piecewise damper and the cubic hardening of the suspension spring. The proposed controller employs the principle of type-2 fuzzy systems with interval membership function and unknown specifications. The effectiveness of the closed-loop system is validated regarding the uncertainties compared to observer-based sliding mode controller (SMC) and a high-fidelity virtual lab MSC.ADAMS-Simulink platform to validate the results in practical scenarios.

Design of a robust adaptive sliding mode control using recurrent fuzzy wavelet functional link neural networks for industrial robot manipulator with dead zone

This paper addresses the problem of trajectory tracking control for industrial robot manipulators (IRMs) in the presence of external disturbances and uncertain dynamics. A novel robust adaptive recurrent fuzzy wavelet functional link neural network (RFWFLNN) controller based on dead-zone compensator is proposed in order to improve the position tracking performance. To handle the unknown dynamics of the IRMs, the robust adaptive RFWFLNNs are applied to approximate the unknown dynamics. The online learning laws and estimation of the dead zone are determined by using Lyapunov stability theory and the approximation theory. In addition, the robust SMC is applied to eliminate the estimation errors and disturbances of the IRM control system. Therefore, the RFWFLNN controller for IRMs can guarantee not only the robustness and stability but also the position tracking performance. Some simulations and experiments performed on three-link IRMs are provided to prove the robustness and effectiveness of the RFWFLNNs. The superiority of the RFWFLNN controller is also demonstrated based on comparisons with fuzzy wavelet neural networks and PID controllers.

Optimization Control of CVT Clutch Engagement Based on MPC

As an important part of continuously variable transmission (CVT) vehicle power transmission system, drive, neutral and reverse (DNR) wet clutch has the function of transmitting or interrupting vehicle power. However, due to the complex and variable working conditions of the clutch, it is difficult to achieve precise control of the clutch by the traditional control strategy. To solve this problem, a clutch control optimization algorithm based on model predictive control (MPC) is proposed. In order to identify and track the driver's launching intentions, a driver's launching intentions recognition system based on fuzzy neural network (FNN) is designed. The impact degree and friction work are taken as the evaluation standard of clutch control. The clutch controller is designed by using MPC control strategy, and the control effect is compared with the adaptive fuzzy neural network (AFNN) strategy. Finally, the validity of the control strategy is verified by simulation model and vehicle test. The results show that compared with the AFNN control strategy, the MPC control strategy can effectively control the clutch engagement and improve the vehicle launching quality.

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.

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.

Fuzzy Tracking Control for a Class of Uncertain MIMO Nonlinear Systems With State Constraints

In this paper, an adaptive fuzzy neural network (FNN) control scheme is developed for a class of multiple-input and multiple-output (MIMO) nonlinear systems subject to unknown dynamics and state constraints. FNNs are used to approximate the unknown dynamics that comprises the effects of uncertain parameters and functions. Also, integral Lyapunov functions are introduced to address state constraints. A neural-network-based observer is designed to estimate the unmeasurable states. With state-feedback and output feedback tracking control, the stability of closed-loop system is guaranteed via Lyapunov’s stability theory. Two cases of simulations for MIMO systems with state constraints are conducted to verify the effectiveness of the proposed control.

Heading Control of Unmanned Marine Vehicles Based on an Improved Robust Adaptive Fuzzy Neural Network Control Algorithm

A robust adaptive fuzzy neural network control (RAFNNC) algorithm is proposed based on a generalized dynamic fuzzy neural network (GDFNN), proportion-integral-differential (PID), and improved bacterial foraging optimization (BFO) algorithm, for heading the control of the unmanned marine vehicle (UMV) in the presence of a complex environment disturbance. First, the inverse dynamic model of the motion control of UMV is established based on the GDFNN for the uncertain disturbance caused by the complex environment disturbance. Then, the adaptive rate of the fuzzy neural network is designed based on the error between the real UMV heading angle and designed reference heading angle, so as to further adjust the weight parameter of the GDFNN, and then, the output control value of the neural network is obtained. In order to further reduce the computation amount and computation time of the RAFNNC, the parameters of the PID control algorithm were optimized in advance by using the improved BFO algorithm. The fractal dimension step size and the intelligent probe operation are integrated into the BFO algorithm, in order to optimize the operation time and accuracy of the algorithm. Stability of the designed RAFNNC algorithm for the heading control of the UMV in the presence of complex marine environment disturbance is proved by the Lyapunov stability theory, and the effectiveness and accuracy of the control algorithm proposed are verified by semi-physical simulation experiment carried out in our laboratory.