Fuzzy system approach for task planning and control of micro wall climbing robots

Purpose To become a sustainable lean manufacturing (LM) system, an organization must be first distinctly cognizant of the terms “lean” and “sustainability” as they relate to the state of affairs of their particular industry and business. Next, the organization must identify and acquire the necessary qualities it needs to become sustainable in lean philosophy and its practices in the organization. The LM paradigm has been a top priority for many businesses; thus, this article is based on actual research done in Indian small and medium scale organizations to see how widely it is understood and implemented. Design/methodology/approach A framework was developed based on literature review and academic research. A preliminary analysis of a small number of small and medium-sized enterprises (SMEs) that, conceptually, summarizes and demonstrates the concerted efforts that a company may undertake to increase its leanness. This conceptual model was employed to create a questionnaire that was administered to survey the SMEs of India. The information gathered through this questionnaire was analyzed using the model developed by the researchers. Then fuzzy logic and systems approach were used to find out the effectiveness index (EI) of the organization. Findings The EI for system leanness at different organizational levels within an organization is determined using fuzzy logic and systems approach for 48 SMEs in different clusters. The average EI of the system was found to be 0.336 on a scale of 0–1 which indicates that the current state of lean implementation and its sustainability is very low and poor in Indian SMEs. This article outlines the key model components and describes how they were applied to analyze the data gathered from an industry study. Research limitations/implications The research aims to make lean continuously sustainable by surfacing and eliminating the wastes in the Indian SMEs whenever it appears rather than using it as a cleaning tool. The present study was focused on India’s southern industrial areas and it was difficult to gather the information around the country due to its diverse industrial culture and geography. Hence, more research and the comparative study of the same that takes into account the various regions of the nation’s industrial lean behavior can be conducted. Practical implications The generalized sustainable lean framework analyzed using fuzzy logic and systems approach gives the current effectiveness of the leanness in SMEs of south India. This model can be effectively implemented in other areas of the nation to identify the scenario of lean and its sustainability and a final comprehensive model can be developed. Originality/value There is a dearth of comprehensive studies on the assessment of sustainability of the lean philosophy in Indian SMEs. With the help of combined fuzzy logic and systems approach, the model developed in this study evaluates the sustainability of the lean methodology using the EI used in SMEs by taking into account both the lean and sustainability factors as well as enablers like customer satisfaction, ethics, innovation and technology.

Presents a paradigm for task scheduling and action planning/control of a robotic system. Based on the proposed max-plus algebra model, a robotic task involving both discrete and continuous actions can be scheduled, planned and controlled in a perceptive reference frame. Therefore, task scheduling, which usually deals with discrete type of events, as well as action planning, which usually deals with continuous events, can be treated systematically in a unified framework. More importantly, the unique feature of this approach is that interactions between discrete and continuous events can be considered in the same framework. As a result the efficiency, robustness and reliability of the task schedule and action plan can increase significantly. A typical assembly task in a dual-robot manufacturing work-cell is used to illustrate the proposed approach. The experimental results clearly demonstrate the advantages of the proposed approach.

Neuro-Fuzzy network based impulse noise filtering for gray scale images is presented. The proposed method is constructed by hybrid technique of Mamdani and Sugeno based fuzzy interference system approach. As demonstrated by experimental results, Peak signal-to-noise ratio(PSNR) and Root Mean Square Error(RMSE) possess better performance. The proposed filter has some advantages over its competitors. The hybrid rule is applied to reduce the error of the optimization. In hybrid based fuzzy interference system approach, the system develops a fuzzy logic based scheme to filter a noisy signal. This can be applied in various source to reduce noise. A one input and output Mamdani fuzzy interference system is designed for the filter where the input is a noisy signal and output is a filtered output. Fuzzy rules have been used to obtain the filtered output.

In this paper, an adaptive fuzzy output feedback control approach is investigated for a class of stochastic nonlinear strict-feedback systems without the requirement of states measurement. The stochastic nonlinear system addressed in this paper is assumed to possess unstructured uncertainties (unknown nonlinear functions) and, in the presence of unmodeled dynamics, dynamics disturbances. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By combining the backstepping design technique with the stochastic small-gain approach, a new adaptive fuzzy output feedback control approach is developed. It is proved that the proposed control approach can guarantee that the closed-loop system is input-state-practically stability (ISpS) in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters. Simulation results are included to indicate that the proposed adaptive fuzzy control approach has a satisfactory control performance. In addition, the simulation comparisons with the previous methods show that the proposed adaptive fuzzy control approach has robustness to the dynamical uncertainties.

Robust fuzzy model predictive control of discrete nonlinear systems is investigated in this paper. A recently developed Takagi–Sugeno (T–S) fuzzy approach which uses nonlinear local models is adopted to approximate the nonlinear systems. A critical issue that restricts the practical application of classical model predictive control is the online computational cost. For model predictive control of T–S fuzzy systems, the online computational burden is even worse. Especially for complex systems with severe nonlinearities, parametric uncertainties, and disturbances, existing model predictive control of T–S fuzzy systems usually leads to a very conservative solution or even no solution in some occasions. However, more relaxed results can be achieved by the proposed fuzzy model predictive control approach which adopts T–S systems with nonlinear local models. Another advantage is that online computational cost of the optimization problem through solving matrix inequalities can be significantly reduced at the same time. Simulations on a numerical example and a two-tank system are presented to verify the effectiveness and advantages of the proposed method. Comparisons among several T–S fuzzy approaches are illustrated and show that the best settling time is achieved via the proposed method.

This paper presents an implementation of a Fuzzy Motion Controller (FMC) to endow the mobile robot Robucar with capability to achieve the action behavior allowing smooth motion generation with intelligence in real-time. The robot state space (velocity and distances) is modeled in discrete intervals leading to linguistic variables. The fuzzy motion control rules are derived and used in a fuzzy inference mechanism to give the final control command to the robot actuators. Simulation and experimental results show FMC capabilities in generating smooth motions, illustrating then its adaptivity and intelligence.

One of the most important physical properties of the soil is its mechanical strength. Increasing soil mechanical strength will lead to increments in the draft, fuel consumption, work duration and equipment wear, but will follow the reduction in the root growth. Awareness of the soil cone index (as a criterion of the arable soil compaction) in order to production management in connection with the soil physical properties have high importance, especially in precision farming. On the other hand, finding the methods and models that be able to create the best function or model to estimate the soil cone index at the least cost and use of available data are crucial for researchers. The aim of this study was predicting arable soils cone index values by effective parameters on the soil cone index, including bulk density, soil moisture content and soil electrical conductivity by using Fuzzy and neuro-fuzzy systems. In this study, for measurement and determination of the influencing factors on the soil cone index value, the experimental design was the factorial experiment based on a randomized complete block design with five replications. The experimental field had three types of loam, sandy loam and loamy sand soils. The modeling of soil cone index was performed by using effective parameters such as bulk density, moisture content and soil electrical conductivity in the fuzzy and adaptive neuro-fuzzy inference system (ANFIS) systems. For fuzzification of input and output parameters, the linguistic variables, including very low (VL), low (L), medium (M), high (H) and very high (VH) were used. Since determining the type and number of membership functions was conducted experimentally, the triangular membership function for both input and output variables was used due to the high accuracy and convenience in system design. In ANFIS model, 80% and 20% of total data were considered as training and test data, respectively. The numbers of membership functions were selected 5 for each input parameters. ANFIS training was done by the hybrid method. The average of coefficients of determination (R2) were obtained 80.1% and 97.9% for the fuzzy and ANFIS models, respectively. The obtained model through ANFIS presented the high accuracy of 2.54% rather than the fuzzy models with the accuracy of 9.68%. Also in comparison with regression models, ANFIS model has high accuracy and can be used to estimate the soil cone index in agricultural land.

Average local image entropy can be seen as an effective measure of a training environments potential toward producing highly fit robots. As seen in Fig.6 the measure is sensitive to different window values but in our application a wide range of values could have been used. Our selection of neighborhood size was a reasonable as only very low values (around 10) show poor discriminancy. Fig. 7 shows how the average fitness obtained during testing is clearly dependent on the diversity of the training room used. A training environment with too much environmental diversity is as unsuitable as one with not enough diversity. Higher motivation diversity (H ( M)) caused lower average score values. This is possibly due to the different and conflicting (e.g. orthogonal) requirements of the different motivations upon the robotic neural brain. These scores are also negatively impacted by the fitness function used (4) given that it includes a motivation satisfaction product (mn × f n ) in each rule that further penalizes lower behavior fitnesses ( f n ). Even though obtained fitness was generally lower with more diverse motivations, the obtained behaviors demonstrated very good capability (e.g. room exploration, battery usage, etc) and were in agreement with the specified motivations. Because of this, motivation diversity results are somewhat counter intuitive in that by diversifying motivation values one could naively expect higher overall fitness but this is clearly not the case due to system constraints.

The evolution of bionic machines into intelligent robots to adapt to real scenarios is inseparable from positioning sensors. However, traditional positioning methods such as camera arrays, ultrasound, or GPS are limited in narrow concealed spaces, harsh temperatures, or dynamic light fields, which hinder the practical application of special robots. Here, we report a flexible sensor inspired by Gnathonemus petersii that enables robots to achieve contactless and high-precision spatial localization independent of the unstructured features of the environment. Sensors are obtained from low-cost materials (carbon nanotubes and polyimides) and simple structures (fibers) and preparation processes (spin-coating). Experiments and simulations confirmed the high resolution (<1 mm) of the sensor over a large distance detection range (>150 mm) and high bandwidth (0-520 MPa) of contact force. Moreover, the sensing capability is still feasible when the sensor is bent to various curvatures and not affected under harsh conditions such as ultralow temperatures (below -78 °C), ultrahigh temperatures (over 250 °C), darkness, or brightness. We demonstrate the practical potential of the proposed sensors for a biomimetic hyper-redundant continuum robot to locate and avoid collisions in unstructured environments.

Due to the increased environmental and economic challenges, in recent years, renewable based distribution generation has been developed. More penetrations from the side of consumers caused a new concept called microgrids which are able to stand with or without connection to the bulk power system. Control of microgrids in islanded mode is very crucial for decreasing the amplitude of frequency deviations as well as damping speed. This chapter aims to propose an optimal combination of FOPD and fuzzy pre-compensated FOPI approach for load-frequency control of microgrids in islanded mode. The optimization parameter of the control scheme is designed by the differential evolution (DE) algorithm which has been improved by a fuzzy approach. In the optimization, control effort is considered as a constraint. Due to the robustness and flexibility of the proposed method, the simulation results have been improved substantially. Robust performance of the proposed control method is examined through sensitivity analysis.

An output-feedback fuzzy approach to guaranteed cost control of vehicle lateral motion

Intrinsically soft and fully recyclable robotic sensors with quadruple sensing functions for reliable human-robot interactions.

This paper presents a fuzzy sliding-mode cooperative consensus formation control method for networked heterogeneous Mecanum-wheeled multi-robots with dynamic effects. The formation system is composed of three kinds of uncertain Mecanum-wheeled mobile robots (MWMRs) and the communication network is modeled as a directed graph. The dynamic behavior of each kind of uncertain MWMR is modelled by an uncertain second-order dynamic model in a unified framework. By using the Lyapunov stability theory and fuzzy sliding-mode control approach, a fuzzy sliding-mode leader-follower consensus formation control law is presented to carry out formation control in presence of model and parameter uncertainties. Two computer simulations are performed to illustrate the effectiveness and merits of the proposed method.

Abstract.This study involved implementing a fuzzy controller approach to create a ground-based control model for an autonomous robot in a stable environment. In addition, a classical control method was developed and tested within the results. With the fuzzy controller approach, it was studied that the robot moves easily in the test environment and goes to desired targets. An autonomous mobile robot aims to minimize the energy consumption of the robot with a fuzzy controller algorithm. The energy consumption for processing time and data error between the coordinates where the robot should go and the coordinates it goes after the operations are completed are calculated separately. By utilizing a fuzzy controller algorithm, the mobile robot is capable of operating in a flexible manner. It was observed that the fuzzy approach performs 10 times faster and more accurately compared to the traditional method. In terms of being open to development and modular, the flying mobile robot fulfilled the functions envisaged in this work. In the future, the direction of a flying robot can be determined using a digital electronic compass to reach longer distances with external sensors.

Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow