Fuzzy Learning Control Research Articles

Fixed-Time Composite Learning Fuzzy Control With Disturbance Rejection for Uncertain Engineering Systems Toward Industry 5.0

Fixed-Time Composite Learning Fuzzy Control With Disturbance Rejection for Uncertain Engineering Systems Toward Industry 5.0

Composite fuzzy voltage-based command-filtered learning control of electrically-driven robots with input delay using disturbance observer

This paper presents an improved composite fuzzy learning control for uncertain electrically-driven robot manipulators with input delay and the external disturbances. In the framework of the backstepping algorithm, fuzzy systems are employed to approximate the unknown terms where the accuracy of fuzzy learning is also considered by defining prediction errors. With the aid of integral technique and the dynamic surface control, a variable is engendered for the system in such a way that the input-delayed robotic system is converted to the non-delayed robotic system. Besides, the command-filtered control is used to cope with the complexity explosion of the backstepping-based design. In order to improve the robust behavior of the control system, the proposed control scheme is equipped with disturbance observers (DOBs). Different from the previous works, the information of the input-delayed, the compensated error surfaces (obtained from the command-filtered approach), the prediction errors and the disturbance estimations (derived from DOBs) are unified to construct the proposed control framework. The stability of the overall system is verified by the Lyapunov theorem. The efficiency of the proposed concept is illustrated using various simulations for an electrically-driven robot manipulator in the presence of uncertainties.

Considering the Characteristics of Traffic Risk Factors and the Method of Establishing a Flexible Traffic System

With the acceleration of urbanization and the development of the automobile industry, the contradiction between the traffic capacity of existing urban roads and the growing traffic demand has become increasingly acute. Traffic congestion is becoming increasingly prominent. The purpose of this article is to consider the characteristics of traffic risk factors and to study the method of establishing a flexible traffic system. It can relieve traffic congestion and provide a smooth and orderly traffic environment using intelligent transportation systems to control and direct traffic flow. Based on the large urban road network, this research uses the theory of coordinated control and learning mechanism, fuzzy control, dynamic reprogramming, and other theories to study phase sequence, balance peripheral load, and overall traffic flow. It also uses on-board sensors to optimize the collection and processing of network information, decompose traffic guidance work, select the optimal route, and autonomously guide the intelligent transportation system. Under the flexible demand scheme, the average load of the trunk road is reduced by a larger degree, which is 4% lower than that of the fixed demand scheme. At the same time, the average load of the branch has increased more, which is 4% higher than that of the fixed demand scheme. It can be seen that under the elastic demand scheme, the distribution of traffic flow in the road network is more balanced and the optimization effect of relieving traffic pressure on trunk roads and improving the utilization rate of branch roads is more significant.

Filtering-Error Constrained Angle Tracking Adaptive Learning Fuzzy Control for Pneumatic Artificial Muscle Systems Under Nonzero Initial Errors

In this paper, a filtering-error constrained adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a pneumatic artificial muscle-actuated mechanism. The adaptive learning controller is designed by a novel barrier Lyapunov function, and the filtering error of pneumatic artificial muscle system is ensured to be constrained during each iteration. The initial position problem of iterative learning control is solved by utilizing time-varying boundary layer method. Fuzzy logic system is applied to approximate the unknown nonparametric uncertainties in the pneumatic artificial muscle system, whose optimal weight is estimated by using difference learning approach. The approximation error of fuzzy logic system is tackled by robust control strategy. Simulation results show the effectiveness of the propose angle tracking adaptive learning fuzzy control scheme.

Iterative Learning Fuzzy Control for Nonlinear Systems with Adaptive Gain and without Resetting Condition

In this paper, we present two ILC schemes. The first one is a PD-type iterative learning control with an initial state algorithm to solve the trajectory tracking problem for nonlinear systems with uncertainties and without satisfying the classical resetting condition. -norm method is used to prove the asymptotic stability of the closed loop system and the simulation results on perturbed nonlinear system have been given. The second approach is a simple P-type iterative learning fuzzy control scheme to solve the trajectory tracking problem for MIMO nonlinear systems. The control design is applicable to deal with a class of nonlinear systems without satisfying the global Lipschitz continuity condition, for which a fuzzy logic term is added to cope with unknown parameters. In addition, the swarm optimization algorithm is used to design the optimum iterative learning fuzzy control (ILFC). Using Lyapunov theory, the asymptotic stability of the closed loop system is guaranteed over the whole finite time. Finally, an illustrative example on two-link manipulator is provided to illustrate the effectiveness of the proposed controller.

Development of a Didactic Educational Tool for Learning Fuzzy Control Systems

This paper presents the development of a virtual didactic tool for students of mechatronic engineering taking an intelligent system course. The objective of the tool is for students to learn the structures for fuzzy control systems. This tool makes it easier for students to understand the behavior of the membership functions of input and output variables, the evaluation of the set of fuzzy rules, and the method of defuzzification, giving the students the possibility of applying a fuzzy controller in industrial processes using a data acquisition board. The proposed tool was developed with the virtual instrumentation software LabVIEW. It has the advantage that students can manipulate the internal structure of the fuzzy logic control system in a unique window where students can analyze the behavior of internal signals by looking at the response graphs. The fuzzy controller can be easily translated to a real application by using LabVIEW compatible hardware. To have feedback from students on the use of the tool and to understand if this tool allows an improvement in their academic performance, a 2-hour workshop on the proposed application was given to a group of 93 students. At the end of the workshop, a knowledge assessment and a perception survey were applied to the participants. The academic performance achieved by students who were given the workshop using the proposed teaching tool was compared with the academic performance of students who witnessed the workshop using Matlab tools. The statistical analysis of the results obtained for the knowledge assessment shows that the students that had taken the workshop using the proposed teaching tool had better compression of the topic compared to the students that had taken the workshop using the Fuzzy Logic Toolbox provided by Matlab MathWorks. The students that had taken the workshop using the proposed teaching tool obtained a mean grade of 89.63/100, while students that had taken the workshop using Matlab’s tools obtained a mean grade of 69.85/100. Also, the students’ perception of the proposed tool was that it allowed the design of fuzzy control systems in a simple and intuitive way.

Adaptive state augmented clustering-based fuzzy learning control of a passive torque simulator

Adaptive state augmented clustering-based fuzzy learning control of a passive torque simulator

Development and control of an intelligent assistive exo-glove via fuzzy controller and emotional learning system

The aim of this article is design, fabricate, and control a conceptual prototype of a flexible wearable robot to increase fingers’ force using a novel cable mechanism. In this robot, force and position are controlled in separate phases by a fuzzy system equipped with emotional learning. A Flexible structure has been chosen for this robot, because flexible gloves are lighter and much more suitable for use in daily tasks compared to rigid structures. Since this system is supposed to be substituted for weak or damaged limb, it should have a behavior similar to the body organs as much as possible. Therefore, a novel mechanism, inspired by the natural movement mechanism of the human hand, has been designed and tried to use a control method similar to the human brain. In order to control the glove, a proportional linear and a fuzzy controller were selected and designed. Then they were implemented through the Simulink environment of MATLAB software and applied to the glove using a real-time interface system. Finally, through some experiments, the performance of the glove and controllers have been evaluated and compared in the case of using linear and emotional learning fuzzy controllers. As expected, using the fuzzy controller with an emotional learning system makes low tracking errors result and represents appropriate performance. Compared to the linear controller, the overshoot decreased up to the one-third, and the settling time declined to one-fifth.

Composite Learning Fuzzy Control of Stochastic Nonlinear Strict-Feedback Systems

This article investigates the composite learning fuzzy control for a class of stochastic nonlinear strict-feedback systems subject to dynamics uncertainty. The fuzzy logic system is built to model the unknown system nonlinearity. The highlight is that different from previous studies using only tracking error for fuzzy weight updating, the accuracy of fuzzy learning is emphasized in this study. The serial-parallel estimation model with fuzzy approximation and gain compensation is constructed to acquire the prediction error such that the composite fuzzy updating law is designed with more accurate feedback information. The stochastic stability analysis ensures the uniformly ultimate boundedness of the system signals in mean square. Through the simulation tests on a numerical example with different stochastic disturbances and one-link manipulator dynamics, it is proved that the proposed composite learning scheme can solve the system uncertainty effectively and make the closed-loop system track the reference command with satisfactory accuracy.

Analysis of vibration high-frequency dynamic characteristics of wheelless rail vehicle system based on fuzzy logic control

Track irregularity is the main source of vibration excitation of vehicle-track weighing system, which has an important impact on the safety, stability and comfort of train operation, and it is also the main factor limiting the speed of train operation. Higher requirements are put forward for track smoothness with the rapid development of China’s railways. Therefore, it has a great theoretical and practical significance to study the relationship between track irregularity and random vibration of vehicle-track cooker-in system and the evaluation method of track smoothness. In this paper, a model construction method is proposed based on machine learning fuzzy logic control and neural network algorithm to analyze the high frequency dynamic characteristics of ballastless track wheel-rail vehicle system. Firstly, a numerical analysis model of high frequency dynamic characteristics of ballastless track wheel-rail vehicle system is established by using linear discrete elastic-yellow damper element connection model; Secondly, the Rough Set Block Neural Network is introduced to optimize the dynamic characteristic analysis model, and the intelligent model of model optimization analysis is established. Finally, the validity of the proposed algorithm is verified by simulation experiments of practical examples.

A cognitive/intelligent resource provisioning for cloud computing services: opportunities and challenges

In cloud computing, resources could be provisioned in a dynamic way on demand for cloud services. Cloud providers seek to realize effective SLA execution mechanisms for avoiding SLA violations by provisioning the resources or applications and timely interacting to environmental changes and failures. Sufficient resource provisioning to cloud’s services relies on the requirements of the workloads to achieve a high performance for quality of service. Therefore, deciding the suitable amount of cloud’s resources for these services to achieve is one of the main works in cloud computing. During the runtime of services, the amount of cloud’s resources can be specified and provisioned based on the actual workloads changes. Determining the correct amount of cloud’s resources needed for running the services on clouds is not easy task, and it depends on the existing workloads of services. Consequently, it is required to predict the future workloads for dynamic provisioning of resources in order to meet the changes in workloads and demands of services in cloud computing environments. In this paper, we study the possibility of using a cognitive/intelligent approach for cloud resource provisioning which is a combination of the autonomic computing concept, deep learning technique and fuzzy logic control. Deep learning technique is a state-of-the-art in the machine learning field. It achieved promising results in many other fields like image classification and speech recognition. For these reasons, deep learning is proposed in this work to tackle the workload prediction in cloud computing. Additionally, we also propose to use a fuzzy logic-based method in order to make a decision in the case of uncertainty of the workload prediction. We study various exiting works on autonomic cloud resource provisioning and show that there is still an opportunity to improve the current methods. We also present the challenges that may exist on this domain.

Composite learning fuzzy synchronization for incommensurate fractional‐order chaotic systems with time‐varying delays

SummaryThis paper presents a composite learning fuzzy control to synchronize two different uncertain incommensurate fractional‐order time‐varying delayed chaotic systems with unknown external disturbances and mismatched parametric uncertainties via the Takagi‐Sugeno fuzzy method. An adaptive controller together with fractional‐order composite learning laws is designed based on both a parallel distributed compensation technology and a fractional Lyapunov criterion. The boundedness of all variables in the closed‐loop system and the Mittag‐Leffler stability of tracking error can be guaranteed. T‐S fuzzy systems are provided to tackle unknown nonlinear functions. The distinctive features of the proposed approach consist in the following: (1) a supervisory control law is designed to compensate the lumped disturbances; (2) both the prediction error and the tracking error are used to estimate the unknown fuzzy system parameters; (3) parameter convergence can be ensured by an interval excitation condition. Finally, the feasibility of the proposed control strategy is demonstrated throughout an illustrative example.

Disturbance Observer Based Composite Learning Fuzzy Control of Nonlinear Systems with Unknown Dead Zone

This paper investigates the disturbance observer-based composite fuzzy control of a class of uncertain nonlinear systems with unknown dead zone. With fuzzy logic system approximating the unknown nonlinearities, composite learning is constructed on the basis of a serial–parallel identifier. By introducing the intermediate signal, the disturbance observer is developed to provide efficient learning of the compounded disturbance which includes the effect of time-varying disturbance, fuzzy approximation error, and unknown dead zone. Based on the disturbance estimation and fuzzy approximation, the adaptive fuzzy controller is synthesized with novel updating law. The stability analysis of the closed-loop system is rigorously established via Lyapunov approach. The performance of the proposed controller is verified via simulation that faster convergence and higher precision are obtained.

Fuzzy Pi Adaptive Learning Controller for Controlling the Angle of Attack of an Aircraft

In this paper, a Fuzzy PI Adaptive Learning controller is proposed for a flight control system to control the angle of attack of an aircraft. The proposed controller tracks the reference angle as desired by the pilot of the aircraft. The performance indices are evaluated and the corresponding value is compared with that for the conventional controllers obtained from Zigler Nichols (ZN), Tyreus Luyben (TL) and Extended Skogestad Internal Model Controller (ESIMC). The performance indices such as Mean Square Error (MSE), Integral Absolute Error (IAE) and Integral Absolute Time Error (IATE) are evaluated to verify superiority of one over another.

Logical Linguistic Controllers

The logical-linguistic, analytical, learning and PID fuzzy controllers are considered, based on fuzzy logics of Zadeh. An overview of the Mamdani-type controllers, controllers based on TS-model and the ANFIS architecture, using neural network structure is provided. The conditions of optimality and stability of control systems with Mamdani fuzzy controllers are analyzed. The Sugeno dynamic models and the ANFIS adaptive models and the methods of learning developed on the basis of fuzzy controllers are considered.

Composite Learning Fuzzy Control of Uncertain Nonlinear Systems

Function approximation accuracy and computational cost are two major concerns in approximation-based adaptive fuzzy control. In this paper, a model reference composite learning fuzzy control strategy is proposed for a class of affine nonlinear systems with functional uncertainties. In the proposed approach, a modified modeling error that utilizes data recorded online is defined as a prediction error, a linear filter is applied to estimate time derivatives of plant states, and both the tracking error and the prediction error are exploited to update parametric estimates. It is proven that the closed-loop system achieves semiglobal practical exponential stability by an interval-excitation condition which is much weaker than a persistent-excitation condition. Compared with a concurrent learning approach that has the same aim as this study, the computational cost of the proposed approach is significantly reduced for the guarantee of accurate function approximation. An illustrative example of aircraft wing rock control has been provided to verify effectiveness of the proposed control strategy.

Online learning fuzzy vibration control of smart truss structure

This paper deals with the active vibration control of smart truss structure. First, the electro-mechanical coupled dynamic model of the smart structure is constructed. Then, the first-order ordinary differential equation of the control system is presented. After that, an online learning fuzzy control (OLFC) algorithm is proposed to control the structure vibrations. The OLFC algorithm is composed of a reward function, a Q learning algorithm, a rule base generator and a conventional fuzzy controller. The OLFC algorithm learns the rule base by interaction with the plant, and changes rule base generate policy via evaluative reward signal to realize the learning goal. The algorithm only needs little information about the plant to design the reward function. In order to prove the effectiveness of the proposed control algorithm, control responses are presented and compared with conventional fuzzy control method.

Comparative Study of Fuzzy PID Control Algorithms for Enhanced Position Control in Laparoscopic Surgery Robot

This paper proposes intelligent fuzzy proportional–integral–derivative (PID) controllers for the position control of a master–slave configuration laparoscopic surgery robot system. For the slave robot controller, two fuzzy PID control algorithms are implemented: a rule-based fuzzy control algorithm for online PID gain tuning and a learning fuzzy controller to tune the rules in the rule buffer automatically online. The two fuzzy controllers are tested using sinusoidal reference motions with various periods and the test results are compared with those of a conventional PID controller in terms of position control performance. Various performance indices are used for the comparison, including root mean square error, steady-state error, integral of average error, integral squared error, integral time-weighted absolute error, and integral of time multiplied by the squared error. The evaluation shows that the learning fuzzy controller yields the best performance among the three algorithms. Furthermore, the relationship between the scaling gains and the performance can be deduced to construct a comparative tuning algorithm, which enables the scaling gains to be optimally tuned with less trial and error. Further refinement of the algorithm for enhancing control performance is under way.

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.

Reinforcement Learning Fuzzy Neural Network Control for Magnetic Shape Memory Alloy Actuator

Magnetic shape memory alloy actuator is a new type of actuator that can offer big travel and high resolution of output displacement, which makes it suitable for driving task. However, its output displacement represents the hysteresis applied to input magnetic field. Hysteresis restricts its application in the high precision positioning. In order to eliminate the hysteresis of magnetic shape memory alloy actuator, a reinforcement learning fuzzy neural network controller is proposed. Network structure and special learning algorithm of reinforcement learning fuzzy neural network controller are introduced in detail. The proposed control system adopts the generalized approximate reasoning-based intelligent control architecture, which is mainly consisted of three parts: Action Selection Network, Action Evaluation Network and Stochastic Action Modifier. Finally, in order to verify the effectiveness of the proposed control method, the simulation experiment is researched. The experimental results show that the proposed control method can obtain the smaller tracking error, and the controller’s maximum tracking error is less than 0.95%, hysteresis loop is less than 2.66%.