Narrow Results

In order to control the humanoid manipulator, the Chinese medicine massage force can be automatically adjusted according to human demand, and ensure that the massage force is close to the masseur’s manual treatment effect, an automatic control method of Chinese medicine massage force of humanoid manipulator based on fuzzy controller is designed. This method constructs the dynamic model of the massage robot, analyzes the relationship between the motor voltage and the rotation frequency of the manipulator, and believes that the voltage affects the rotation frequency of the motor, and then affects the massage force. The PVDF sensor is installed on the manipulator structure to collect the manipulator motor voltage signal that can represent the massage force. According to the collected signals, a fuzzy logic controller is designed with force error and force error change rate as the input, and the manipulator motor output voltage as the control output. The controller uses a dragonfly optimization algorithm to optimize and adjust the fuzzy control parameters, so as to realize the automatic control of Chinese massage force of humanoid manipulator. The experimental results show that under the control of this method, the change of Chinese massage force of the humanoid manipulator is similar to that of human hand massage, and it can automatically enter the steady state massage program according to the set intensity.

A novel high speed linear tunable transconductor suitable for analog and mixed-signal fuzzy circuits operating in current mode is proposed. Using this OTA, we construct a high speed fuzzifier and implement trapezoidal/triangular functions with all parameters (slope, width, and position) independently and continuously tunable, and excellent for low voltage applications. Computer simulations verify the performance of this circuit, showing high speed (up to 100 MHz) and a high support range (up to 2.5 V).

The proposed system provides an energy management method for various types of an energy storage system including cascade utilization battery. The method is used to receive, store and manage the relevant operating data from the energy storage battery and also randomly determine the energy distribution coefficient of the energy storage battery. According to the adaptive energy distribution method, the power value of the total distributed energy storage power to the cascade utilization energy is calculated and also the energy distribution coefficient of the energy storage battery in real time is adjusted. Finally, the corrected command value of the energy storage battery power is obtained as an output. The system can not only prevent overcharging and over-discharging of the energy storage system, but also maintain the good performance of the energy storage system. To realize the coordinated control and energy management of the battery power plant, we use multiple types, including conventional battery and cascade utilization power battery control purpose. The performance metrics, namely, real-time energy management, computational time and operating cost are employed for the experimental purpose. The simulation results show the superior performance of the proposed energy management system over other state-of-the-art methods.

An important and difficult issue in designing a Fuzzy Inference System (FIS) is the specification of fuzzy sets and fuzzy rules. In this paper, two useful qualitative properties of the FIS model, i.e., the monotonicity and sub-additivity properties, are studied. The monotonic sufficient conditions of the FIS model with Gaussian membership functions are further analyzed. The aim is to incorporate the sufficient conditions into the FIS modeling process, which serves as a simple (which can be easily understood by domain users), easy-to-use (which can be easily applied to or can be a part of the FIS model), and yet reliable (which has a sound mathematical foundation) method to preserve the monotonicity property of the FIS model. Another aim of this paper is to demonstrate how these additional qualitative information can be exploited and extended to be part of the FIS designing procedure (i.e., for fuzzy sets and fuzzy rules design) via the sufficient conditions (which act as a set of useful governing equations for designing the FIS model). The proposed approach is able to avoid the "trial and error" procedure in obtaining a monotonic FIS model. To assess the applicability of the proposed approach, two practical problems are examined. The first is an FIS-based model for water level control, while the second is an FIS-based Risk Priority Number (RPN) model in Failure Mode and Effect Analysis (FMEA). To further illustrate the importance of the sufficient conditions as the governing equations, an analysis on the consequences of violating the sufficient conditions of the FIS-based RPN model is presented.

In this article, we consider environmental and demographic fuzziness when the varying uncertainties are modelled by Fuzzy Set Theory. In the first case the uncertainties are considered in the parameters of the model, frequently described by differential equations. In the second the state variables and their variation rates are linguistic and related through fuzzy rules. Here we proposed a methodology to study stability of equilibriums for systems whose direction field is partially known and given by a fuzzy rules. From the results it is possible to obtain much relevant information that can help in the study of classic models.

However this methodology can be applied to more complex epidemiological mathematical models. With the goal of making comparisons with the results of the classic model, we apply these ideas in the formulation of the SIS model (susceptible-infectedsusceptible), which is the model that describes a disease in which individual recovers but does not develop any kind of immunity. The basic reproduction values (R0) for the fuzzy model and the classic model are compared. For the SIS model described by a system based on fuzzy rules, instead of differential equations, the stability of the equilibrium point is analyzed using the Lyapunov function. Also, parameter estimation from fuzzy modelling is considered.

This study investigates a fuzzy controller technique for autonomous robot navigation in both the static and dynamic environmental conditions and an excessive number of pathways to the destination. The design and implementation of a novel obstacle avoidance technique for autonomous robots are developed using the fuzzy controller-based multi-agent system. This method allows the Robot to identify dynamic or static unidentified objects while directing the Robot to prevent collisions and advance toward the objective. The Robot is capable of moving in a variety of environments. The Robot may communicate and travel in dynamic space by perceiving its surroundings and pursuing a free-collision route. This study covers creating a multi-agent system that includes fuzzy logic to regulate the robotic movements along a path reactive for effective Navigation. This project aims to develop an algorithm that allows the Robot to do distinct tasks to accomplish a unified objective, autonomous Navigation in a slightly unfamiliar environment. Under such a situation, the usage of a multi-agent system is advantageous. As a result, we created a framework made up of four agents responsible for sensing, Navigation, dynamic, and static obstacle avoidance. These agents communicate with one another via a coordinating mechanism.

In this paper, we notice the fact that a human learning process is characterized by a process under a natural language environment, and discuss an approach of learning based on indirect linguistic instructions. An instruction is interpreted through some meaning elements and each trend. Fuzzy evaluation rules are constructed for the searched meaning elements of the given instruction, and the performance of a system to be learned is improved by the evaluation rules. In this paper, we propose a framework of learning based on indirect linguistic instruction based learning using fuzzy theory: FULLINS(FUzzy-Learning based on Linguistic INStruction). The validity of FULLINS is shown by applying it to two control examples: truck backer-upper control and helicopter flight control problem.

In this paper, a Fuzzy controller model has been converted into a time-dependent nonlinear model and then quadratic Riccati differential equation was analyzed to satisfy the solution of the nonlinear active noise behavior of Fuzzy controller. Further, the approximate solutions of this equation using non-perturbation methods i.e., adomian decomposition method (ADM), variational iterational method (VIM) and homotopy perturbation method (HPM) were investigated. A comparison of these methods has also been given with tabular and graphical presentations. Our results reveal that VIM provides the closest approximate solution and fast convergence for the proposed model as compare to ADM and HPM.

As improving system reliability in a basic system has been always one of the important concerns in reliability engineering; many studies have been developed in this regard. In this paper, a novel intelligent PSO (PSO-IM) is proposed. In suggested approach two different types of mutation operator, which controlled by Fuzzy controller, are applied to standard PSO for finding the best solution of reliability-redundancy allocation problems (RRAP). Also a heuristic inertia weight equation is introduced in our proposed PSO-IM. The main objective of our solution is to achieve maximum reliability in a basic system with acceptable redundancy allocation subject to cost, weight, volume. The proposed method (PSO-IM) significantly improves the search ability of basic PSO and finds the maximum reliability in comparison with previous works.

The performance of on telescope depend strongly on its operating conditions. During pointing, the telescope can move at a relatively high velocity, and the system can tolerate trajectory position errors higher than during tracking. On the contrary, during tracking, Alt-Az telescopes generally move slower but still in a large dynamic range. In this case, the position errors must be as close to zero as possible. Tracking is one of the essential factors that affects the quality of astronomical observations. In this paper, a hybrid Genetic-Fuzzy approach to control the movement of a two-link direct-drive Celestron telescope is introduced. The proposed controller uses the Genetic algorithm (GA) for optimizing a fuzzy logic controller (FLC) to improve the tracking of the 14-inch Celestron telescope of the Kottamia Astronomical Observatory (KAO). The fuzzy logic input is a vector of the position error and its rate of change, and the output is a torque. The GA objective function used here is the Integral Time Absolute Error (ITAE). The proposed method is compared with a conventional Proportional-Differential (PD) controller, an optimized PD controller with a GA, and a Fuzzy controller. The results show the effectiveness of the proposed controller to improve the dynamic response of the overall system.

This paper presents a generalized linguistic variable which can be viewed as an extension of the (ordinary) linguistic variable proposed by Zadeh. After analyzing the fuzzy sets FScom developed by Pan, we discover that the FScom is at least possessed with several shortcomings: 1) give any fuzzy set A, the medium negative fuzzy set of A is non-normal; 2) in FScom, the parameter λ is non-trivial, namely, the value of λ is not easy to be determined. In order to sketch the essential and intrinsic relationships between fuzzy knowledge and its different negation forms, we define a novel type of generalized fuzzy sets with contradictory, opposite and medium negation GFScom, and further explore several basic algebraic operations, properties and convexity and concavity with respect to GFScom. Moreover, we apply the generalized linguistic variable (or GFScom) to the Mamdani controller and suggest a novel form of fuzzy controller by considering three kinds of negation in a fuzzy system. A simple demonstration in a fuzzy system shows that the generalized linguistic variable (or GFScom) makes the fuzzy reasoning capability of fuzzy system much richer.

This chapter presents a new practical control system that can apply conventional PID controllers to nonlinear field by using fuzzy reasoning. The proposed system is a hierarchical one consisting of two components: (a) a Fuzzy-PID controller, and (b) a supervisor for setting the control target of this controller. The fuzzy controller in the Fuzzy-PID controller compensates the output error of the conventional PID controller. The supervisor calculates the control target by fuzzy reasoning. This hierarchical control system is applied to the temperature control in a petroleum plant. The parameters in fuzzy controller are tuned on-line in the actual plant and the system can control the temperature effectively in the transient state, such as feed property changing or operation mode changing, as well as in the steady state