Adaptive Fuzzy Method Research Articles

Novel fuzzy adaptive finite‐time nonsmooth control for uncertain nonlinear systems

SummaryIn this article, a novel fuzzy adaptive finite‐time nonsmooth controller is developed to handle the finite‐time tracking problem for a class of uncertain nonlinear systems. Different from traditional fuzzy adaptive approximation methods, proposed method contains only one adaptive parameter, no matter how many states there are in the system. By constructing a new Lyapunov function with prescribed performance bound, the transient and steady performances of control system can be ensured. Further, based on a criterion of finite‐time semiglobal practical stability and backstepping technology, a novel fuzzy adaptive finite‐time nonsmooth control method is designed. It can be demonstrated that proposed control can effectively ensure tracking error tends to small neighborhood in a finite time. Finally, two examples have been simulated by the proposed control method, and it shows effective tracking performance.

Energy Optimization Control Algorithm of Underwater Vehicle Based on Model Predictive Control

Xin, G.; Zhou, M.; Yang, B., and Ding, X., 2020. Energy optimization control algorithm of underwater vehicle based on model predictive control. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 830–834. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the accuracy of energy optimization control of underwater vehicle and improve the running stability of underwater vehicle, an energy optimization control method of underwater vehicle based on model predictive control is proposed. The underwater dynamics and kinematics model of underwater vehicle is constructed, and the fuzzy feedback error tracking fusion control law of underwater vehicle is constructed with phase offset and inertia torque as constraint parameters. Fuzzy parameter fusion and adaptive parameter adjustment methods are used to optimize the energy control and model prediction of underwater vehicle. The state adaptive correction and energy optimization control design of underwater motion of robot is realized by model parameter prediction and adaptive weighted information processing. The simulation results show that the energy optimization control design of underwater vehicle using this method has good adaptability, eliminates the angle and azimuth error of energy optimization control, reduces the energy cost of the robot, and improves the stability control accuracy of the energy output of the robot.

Multiobjective Overtaking Maneuver Planning for Autonomous Ground Vehicles

Constrained autonomous vehicle overtaking trajectories are usually difficult to generate due to certain practical requirements and complex environmental limitations. This problem becomes more challenging when multiple contradicting objectives are required to be optimized and the on-road objects to be overtaken are irregularly placed. In this article, a novel swarm intelligence-based algorithm is proposed for producing the multiobjective optimal overtaking trajectory of autonomous ground vehicles. The proposed method solves a multiobjective optimal control model in order to optimize the maneuver time duration, the trajectory smoothness, and the vehicle visibility, while taking into account different types of mission-dependent constraints. However, one problem that could have an impact on the optimization process is the selection of algorithm control parameters. To desensitize the negative influence, a novel fuzzy adaptive strategy is proposed and embedded in the algorithm framework. This allows the optimization process to dynamically balance the local exploitation and global exploration, thereby exploring the tradeoff between objectives more effectively. The performance of using the designed fuzzy adaptive multiobjective method is analyzed and validated by executing a number of simulation studies. The results confirm the effectiveness of applying the proposed algorithm to produce multiobjective optimal overtaking trajectories for autonomous ground vehicles. Moreover, the comparison to other state-of-the-art multiobjective optimization schemes shows that the designed strategy tends to be more capable in terms of producing a set of widespread and high-quality Pareto-optimal solutions.

Semi-active vibration control of large-scale flexible structure based on fuzzy adaptive SSDV technique

In order to suppress the vibration of large-scale flexible structures which are applied on the spacecraft, a semi-active control method based on fuzzy adaptive SSDV (synchronized switch damping on voltage) technique is proposed. The value of voltage source at the switching moment is adjusted adaptively by using fuzzy logic, in order to improve the stability and control precision of the semi-active control system. The feasibility of this method is verified by two kinds of experiments with typical disturbance conditions. By comparing different adaptive displacement-voltage curves, the fuzzy adaptive method can achieve higher control precision. For the initial displacement disturbance of 10 mm, the fuzzy adaptive SSDV method can increase the system damping ratio from 0.6% to 5.1%. The control effect decreases with the increase of initial displacement. For the disturbance caused by the mechanism at the tip position, the method can effectively shorten the vibration damping time and reduce the displacement from 0.3 mm to 0.04 mm.

Fuzzy MRAS rotor resistance identification of BL-IM based on reactive power

As for the inverse decoupling control system of a bearingless induction motor (BL-IM), in order to eliminate the influence of rotor resistance variation on its control performance, on the basis of the reactive power calculation of torque system, a novel fuzzy model reference adaptive (MRAS) identification method of rotor resistance is proposed. The reference model and adjustable model of instantaneous reactive power are derived in detail. In order to improve the identification performance of rotor resistance, a fuzzy PI adaptive law based on popov super stability theory is constructed. On this basis, a rotor resistance identifier is constructed, and it is used to on-line correct the rotor resistance parameter in the inverse system mathematical model of a BL-IM system. Based on the inverse decoupling control system of a BL-IM, the simulation experimental analysis and verification are carried out. The simulation experimental results have shown that when the proposed identification method of rotor resistance is used, not only the identification- and tracking-speed of rotor resistance can be effectively improved, but also the identification accuracy of rotor resistance can be improved; as for the BL-IM system, after the rotor resistance parameter is on-line corrected, not only the inverse dynamic decoupling control performance can be effectively improved, but also the robustness of BL-IM system to the variation of rotor resistance parameters can be improved.

A Fuzzy Ensemble Method With Deep Learning for Multi-Robot System

In a multi-robot system, situation assessment evaluates the current situation quantitatively to help decision-makers make the best decision. Conventional situation assessment methods ignore the initiative of each robot, so it often encounters bottlenecks. Collaborative intelligence shows better performance than a single global decision. To address this problem, this work introduces a deep learning-based fuzzy adaptive method (DLFA) to achieve the real-time situation assessment for a multi-robot system. The proposed method employs the shortest path faster algorithm to achieve information sharing between agents. The shortest path faster algorithm ensures that the agent distributes its state information to its teammates in the fastest way. Each agent gets the information from teammates and treats their state as the observation of the scene. Deep neural network maps current observations into a local situation assessment result by combining a large number of nonlinear processing layers. Finally, each local assessment result is regarded as a brick to construct the final situation assessment via a fuzzy ensemble method. Experimental results show that the proposed method outperforms competitors.

Reactive power sharing control for islanded AC microgrid based on fuzzy adaptive compensation method

Due to reactive power irrational sharing issue resulting from the mismatched line impedances and unbalanced local loads of the inverters, the hierarchical coordination control strategy based on fuzzy adaptive compensation is designed to improve the accuracy of the reactive power sharing in the islanded AC microgrid. Firstly, in the local controller, the total active power and the average reactive power are obtained by the initial power sharing stage and the secondary power sharing stage, respectively. Then, in the power balancing stage, the average reactive power as the input of the fuzzy control can be used to adaptively adjust the voltage reference value in real time, so the rational sharing of reactive power may be achieved by using the designed control strategy. Furthermore, based on the classification of load fluctuation, the coordinated operation strategy is designed to deal with reactive power sharing for different load fluctuation conditions. Noteworthy, only the low-bandwidth synchronisation signal from the central controller is used to the hierarchical control strategy, which can maintain characteristic of plug-and-play in the microgrid. Simulation and experiment results are presented to validate the effectiveness of the proposed strategy.

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

In order to maximize the operating flexibility and optimize the system performance of a battery energy storage system (BESS), developing a reliable real-time estimation method for the state of charge (SOC) of a BESS is one of the crucial tasks. In practice, the accuracy of real-time SOC detection can be interfered with by various factors, such as battery’s intrinsic nonlinearities, working current, temperature, and aging level, etc. Considering the feasibility in practical applications, this paper proposes a hybrid real-time SOC estimation scheme for BESSs based on an adaptive network-based fuzzy inference system (ANFIS) and Coulomb counting method, where a commercially available lead-acid battery-based BESS is used as the research target. The ANFIS allows effective learning of the nonlinear characteristics in charging and discharging processes of a battery. In addition, the Coulomb counting method with an efficiency adjusting mechanism is simultaneously used in the proposed scheme to provide a reference SOC for checking the system reliability. The proposed estimating scheme was first simulated in a Matlab software environment and then implemented with an experimental hardware setup, where an industrial-grade digital control system using DS1104 as the control kernel and dSPACE Real-Time Interface (RTI) interface were used. Results from both simulation and experimental tests verify the feasibility and effectiveness of the proposed hybrid SOC estimation algorithm.

Fuzzy adaptive fault tolerant IGC method for STT missiles with time-varying actuator faults and multisource uncertainties

This paper studies the integrated guidance and control (IGC) problem for a class of skid-to-turn (STT) missiles subjected to rapidly changing actuator failures and coupled multisource uncertainties. Meanwhile, during interception, the target is allowed to possess unknown and changeable maneuver accelerations. By analyzing the dominating factors in the IGC system, a nonlinear uncertain strict-feedback system suffering from matched and mismatched uncertainties is utilized to describe the IGC dynamics. Furthermore, for each subsystem, the control gain functions and their boundaries are assumed unknown, which make it difficult to design the controllers. By using a hyperbolic tangent function and estimating the bounds of the lumped disturbances, the time-varying multisource uncertainties can be handled. To compensate the unknown nonlinearities existing in the IGC system, several fuzzy logic systems are established. Furthermore, a number of reciprocal adaptive laws are developed such that unwanted effects caused by the unknown control gain functions and changing actuator gain faults can be surmounted. As a result, a novel fault tolerant IGC structure is constructed and satisfactory hit-to-kill interception performance can be obtained. Finally, the numerical simulations are provided to verify the validity and advantage of the proposed method.

A Fuzzy Adaptive Approach to Decoupled Visual Servoing for a Wheeled Mobile Robot

To address the performance bottleneck for image-based visual servoing (IBVS), it is necessary to have appropriate servoing control laws, increased accuracy for image feature detection, and minimal approximation errors. This article proposes a fuzzy adaptive method for decoupled IBVS that allows the efficient control of a wheeled mobile robot (WMR). To address the under-actuated dynamics of the WMR, a decoupled controller is used and translation and rotation are decoupled by using two independent servoing gains, instead of the single servoing gain that is used for traditional IBVS. To reduce the effect of image noise, this article develops an improved bagging method for the decoupled controller that calculates the inverse kinematics and does not use the Moore–Penrose pseudoinverse method. To improve convergence, improved Q-learning is used to adaptively adjust the mixture parameter for the image Jacobian matrix (IQ-IBVS). This allows the mixture parameter can be adjusted while the robot moves under the influence of servo control. A fuzzy method is used to tune the learning rate for the IQ-IBVS method, which ensures effective learning. The results of simulation and experiments show that the proposed method performs better than other methods, in terms of convergence.

Real-time control and application with self-tuning PID-type fuzzy adaptive controller of an inverted pendulum

PurposeThis paper aims to keep the pendulum on the linear moving car vertically balanced and to bring the car to the equilibrium position with the designed controllers.Design/methodology/approachAs inverted pendulum systems are structurally unstable and nonlinear dynamic systems, they are important mechanisms used in engineering and technological developments to apply control techniques on these systems and to develop control algorithms, thus ensuring that the controllers designed for real-time balancing of these systems have certain performance criteria and the selection of each controller method according to performance criteria in the presence of destructive effects is very helpful in getting information about applying the methods to other systems.FindingsAs a result, the designed controllers are implemented on a real-time and real system, and the performance results of the system are obtained graphically, compared and analyzed.Originality/valueIn this study, motion equations of a linear inverted pendulum system are obtained, and classical and artificial intelligence adaptive control algorithms are designed and implemented for real-time control. Classic proportional-integral-derivative (PID) controller, fuzzy logic controller and PID-type Fuzzy adaptive controller methods are used to control the system. Self-tuning PID-type fuzzy adaptive controller was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of real-time with self-tuning PID-type fuzzy adaptive controller.

Fuzzy Adaptive Regularization Method for Inverse Steady-State Heat Transfer Problem

For the steady-state heat transfer process, a fuzzy adaptive regularization method (FARM) is proposed to estimate the distributed thermal boundary condition in heat transfer system. First, the relationship model between temperatures at measurement points and parameters to be estimated is established based on sensitivity matrix. The regularization term is introduced into the least-squares objective function, and then the distributed thermal boundary condition is estimated by optimizing the new objective function. A fuzzy inference mechanism is developed to ensure the adaptive ability of FARM in which the regularization parameter is updated based on the residual norm between calculated and measured temperatures at measurement points and the norm of inversion parameters. Taking the plate heat conduction system and fluid–solid conjugate heat transfer system as research objects, the effects of the parameter distribution, the number of measurement points, and measurement errors on the inversion results are discussed by numerical experiments, and comparison with the classical regularization method is also conducted. Results indicate that FARM exhibits a good adaptive ability.

A Robust Localization Method for Unmanned Surface Vehicle (USV) Navigation Using Fuzzy Adaptive Kalman Filtering

Recently, multi-sensor navigation has emerged as a viable approach in autonomous vehicles’ development. Kalman filtering has been widely applied in multi-sensor data fusion, and researchers are trialing variants of the Kalman Filter (KF) to improve the operational robustness of vehicles in a range of environments under varying dynamic constraints. This paper proposes a novel sensor data fusion algorithm employing an Unscented Kalman Filter (UKF) for the autonomous navigation of an Unmanned Surface Vehicle (USV). Since the navigational sensors on-board the USV are subject to operational uncertainties caused by equipment limitations and environmental disturbances, an improved UKF algorithm with the capability of adaptive estimation, namely fuzzy adaptive UKF data fusion algorithm, has been proposed to obtain reliable navigational information. The conventional UKF is capable of fusing a number of raw sensor measurements and generating relatively accurate estimations with proper a priori knowledge of system noise. To deal with systems that lack such information, a fuzzy adaptive estimation method is introduced to enhance the performance of the conventional UKF, making the algorithm capable of verifying and correcting the associated sensor noise in real time. The proposed fuzzy adaptive UKF data fusion algorithm has been tested and evaluated in different simulations modeled using practical maritime environments and the results are compared with the conventional UKF. The sensor measurements taken from a practical USV trial have also been applied to the proposed algorithm for further validation.

Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: the case study of Latyan watershed

Abstract Assessment of climate change in future periods is considered necessary, especially with regard to probable changes to water resources. One of the methods for estimating climate change is the use of the simulation outputs of general circulation models (GCMs). However, due to the low resolution of these models, they are not applicable to regional and local studies and downscaling methods should be applied. The purpose of the present study was to use GCM models' outputs for downscaling precipitation measurements at Amameh station in Latyan dam basin. For this purpose, the observation data from the Amameh station during the 1980–2005 period, 26 output variables from two GCM models, namely, HadCM3 and CanESM2 were used. Downscaling was performed by three data-driven methods, namely, artificial neural network (ANN), nonparametric K-nearest neighborhood (KNN) method, and adaptive network-based fuzzy inference system method (ANFIS). Comparison of the monthly results showed the superiority of KNN compared to the other two methods in simulating precipitation. However, all three, ANN, KNN, and ANFIS methods, showed satisfactory results for both HadDCM3 and CanESM2 GCM models in downscaling precipitation in the study area.

Improving the Parameters of the Model of the Wheeled Mobile Robot with Constraints by Adaptive Fuzzy Method

In the process of motion of the ground, the mobile trolley has branches of motion in the direction of the axis of X, Y and Z. However, considering that the mobile robot only has the ability of adjustment of motion on the axis of X and Y, it is said that the mode of the motion of this kind of mobile robots with constraints is not complete. The method of control used in the past is the constraint method of predictive control. The whole mobile robot can be considered as a system including three inputs and three outputs. The motion of the mobile robot in the direction of the axis of Z can be considered as a kind of disturb from the inputs with a certain effect on the axis of X and Y, but the control of the output is converted to the three parts of control used in the motion on the axis of X and Y and the control of the input of disturbance. The three systems with one independent input and one independent output are considered as the predictive control on the system including three inputs and three outputs. In this paper, the parameters of the predictive model are optimized in the way similar to an adaptive fuzzy control and based on errors and change of errors.

Power quality assessment based on combinatorial weight method of variance maximizing

In order to improve the limitation of the existing wind power grid connection quality assessment methods, a more reasonable combinatorial weight method considering the advantages of both subjective and objective is proposed. The objective function is to maximize the variance of the evaluation object under the combined weight, and to optimize the optimal solution of the model as the index weight. On this basis, an adaptive fuzzy neural network is used to evaluate the power quality evaluation method based on the combination weight of the maximization of variance. This method is used to evaluate the 220KV bus power quality of the Weifang power grid. The results not only verify the reasonableness of the proposed variance maximization combination weight weighting, but also verify the effectiveness of the proposed adaptive fuzzy neural network power quality comprehensive evaluation method, which provides a new method for the study of the evaluation method.

SETTING LIQUID LEVEL COUPLED TANK USING FUZZY ADAPTIVE CONTROL

Problem with Coupled Tanks is the appearance of interference in the flow that supplies the tank will make the response unstable, resulting in cross-interaction between each input and output. Fuzzy Logic Control (FLC) has its own advantages that is effective in dealing with complex non-linear systems. Adaptive Fuzzy control Auto tuning has the ability to maintain the steady state response value from interference. Adaptive Fuzzy methodology is attractive choice when formulation in the method proposed for the Coupled tank process. Adaptive Fuzzy control has the ability to maintain the steady state response value from interference. In this study using decoupling in the cross interaction process in each tank. Coupled Tanks on the TITO system can change the transfer function to SISO, so as to minimize the effect of interacting. The right way to design modeling is to pay attention to the state of the plant, so that the desired control model can overcome the non linearity of the TITO system. Adaptive Fuzzy method expected to have percent overshoot and better settling time value. DOI : https://doi.org/10.26905/jeemecs.v1i2.2423

Modeling and prediction of water quality parameters using a hybrid particle swarm optimization–neural fuzzy approach

This work focuses on the correlations among water quality parameters such as total hardness, total dissolved solids, electrical conductivity and total alkalinity with water parameters pH, temperature and the sum of mill equivalents of cations and anions in water reservoir in Kermanshah province (located in the middle of the western part of Iran). The data of water quality of monitoring sites were collected over year 2015. To predict and simulate water quality parameters, two data-driven models, i.e., adaptive neural fuzzy inference system and a hybrid adaptive neural fuzzy inference system structure trained by particle swarm optimization technique, were used. The main advantages of these methods are their high accuracy and very fast computational speed to predict unknown data. The results indicated that implementation of two models is highly satisfactory for predicting inorganic indicators of water quality. However, the flexibility of particle swarm optimization–adaptive neural fuzzy inference system method in modeling is better than adaptive neural fuzzy inference system approach. To prove this, the correlation coefficient, mean absolute error, root mean square error and t statistics were calculated as the error criterion. The overall (training and testing) mean relative error percentage, mean absolute error, root mean square error, correlation coefficient and t statistics obtained by the proposed particle swarm optimization–adaptive neural fuzzy inference system model are less than 3.50, 11.60, 18.90, 0.95 and 0.38%, respectively. The results provide a useful approach that uses water parameters to estimate water quality in water reservoir for water treatment and pollution management.

Soil pore identification with the adaptive fuzzy C-means method based on computed tomography images

The complex geometry and topology of soil is widely recognised as the key driver in many ecological processes. X-ray computed tomography (CT) provides insight into the internal structure of soil pores automatically and accurately. Until recently, there have not been methods to identify soil pore structures. This has restricted the development of soil science, particularly regarding pore geometry and spatial distribution. Through the adoption of the fuzzy clustering theory and the establishment of pore identification rules, a novel pore identification method is described to extract pore structures from CT soil images. The robustness of the adaptive fuzzy C-means method (AFCM), the adaptive threshold method, and Image-Pro Plus tools were compared on soil specimens under different conditions, such as frozen, saturated, and dry situations. The results demonstrate that the AFCM method is suitable for identifying pore clusters, especially tiny pores, under various soil conditions. The method would provide an optional technique for the study of soil micromorphology.

Adaptive Fuzzy Control of Nonlinear Systems With Unknown Dead Zones Based on Command Filtering

Adaptive fuzzy control via command filtering is proposed for uncertain strict-feedback nonlinear systems with unknown nonsymmetric dead-zone input signals in this paper. The command filtering is utilized to cope with the inherent explosion of the complexity problem of the classical backstepping method, and the error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach. In addition, by utilizing the bound information of dead-zone slopes, a new adaptive fuzzy method that does not need to establish the inverse of the dead zone is presented for the unknown nonlinear systems. Compared with existing results, the advantages of the developed scheme are that the compensating signals are designed to eliminate the filtering errors and only one adaptive parameter is required, which will make the proposed control scheme more effective for practical systems. An example of position tracking control for the electromechanical system is given to demonstrate the usefulness and potential of the new design scheme.