Integrated and intelligent control system for a flexible manufacturing cell

It is difficult to archive task sequences in daily life environment for home assistant robots without any faults. In this paper, an integrated robot system toward fault tolerant in task level is presented. In this system, we utilize rule-based, knowledge-based descriptions for error detection and error recovery. Goal States of operators of task planner is used in these descriptions in order to increse reusability of error detection and error recovery rules. The key features of this system are: 1) modular and layered system configuration. 2) error recovery rule and detection rule using state descriptions in task-level. 3) definition of error recovery as list operations to action sequences generated by a task planner.

Reacting to unexpected conditions and recovering from errors is crucial for robots to perform their missions continuously in dynamic environments. This paper introduces a novel framework aiming to handle errors impairing the quality of robot services concerning on-line management of error recovery. The framework is a combination of an execution generation tool, a learning module, and a recovery pipeline for error detection, diagnosis, and recovery in robotic systems. The execution generation tool generates control flow instructions and parameter files, then extracts necessary skills from the skill library based on application descriptions or recovery solution recipes. In the learning module, a Bayesian Network (BN) decision model is interactively and continually trained, incorporating application information structured according to the Failure Mode and Effects Analysis (FMEA) method. The recovery pipeline implements a decision model inferring the cause of an error and choosing recovery solutions. We will provide an experimental evaluation of our framework on a pick-and-place application performed by a robot arm in the physical world.

Intelligent systems often violate fundamental usability principles, such as control and transparency. Explanations have been shown to have a positive effect on transparency in such systems but little research exists as to how it may affect control. We set out to investigate how explanations may impact upon users' perceptions of control in an intelligent system. We conducted an empirical study in which 15 participants carried out a qualitative data analysis task using an intelligent system. Participants were divided into two groups: with and without explanations. Participants could indicate agreement or correct the system. Our results show that participants without explanations display more control-exerting behaviors but that there is no difference between conditions in participants' perception of control. We discuss our findings and implications for future work.

Обоснован перечень задач самообучения интеллектуальных систем управления автономными роботами и мультиагентными робототехническими группировками. Обсуждаются основные проблемы создания интеллектуальных самообучающихся систем управления. Показано, что один из наиболее перспективных подходов к реализации процессов самообучения в интеллектуальных системах управления автономными роботами и многоагентными робототехническими группировками связан с использованием метода автоматического построения деревьев классификации. Приводятся результаты комплексного моделирования.

This paper aims to present an implementation of advanced lighting control system in airport terminal. The lighting requirements of the terminal are fulfilled and regulated by an intelligent control system deployed with i-bus. The intelligent control system adjusts the lighting in terminal at different time according to the real-time requirements. Compared with conventional control system, this intelligent control system can control the lighting in time as long as the real-time requirements are shifted in a remote way. Besides, this new system can also save energy to a great extent.

Ensemble of long short-term memory prediction and variable universe fuzzy control for seismic response reduction using electromagnetic dynamic vibration absorber

When the temperature rise and fall rates of a large-scale aqueduct with traditional water-cooling technology exceeds the standard, it is difficult to avoid the temperature change of aqueduct concrete deviating from its control curve in the process of temperature rise and fall by adjusting the water flow rate or cooling the water temperature of a water pipe only by manual experience. Aiming at such problems, an intelligent temperature control system for a large aqueduct body is developed and applied, which is composed of an information processing and decision-making module, simulation calculation module and information collection and control module, with real-time interactive information and real-time update of the temperature change control curve. The system is applied to the 1:30 Dongjia Village Aqueduct model experiment. The model experiment results show that the system can automatically adjust the water flow velocity of concrete according to the temperature variation control curve, and update the temperature variation control curve according to the changes of environmental parameters and cooling water parameters.

With the progress of computer technology and intelligent control system, it is necessary to introduce the PLC together with the current systems. In this paper, we conduct research on the new intelligent industrial control and communication system based on PLC technique. As Industry 4.0 at the core of intelligent factory, the products made in its basic idea is integrated with dynamic digital memory, perception and communication ability, carrying the whole supply chain and all kinds of information required for life cycle. PLC biggest characteristic is to constitute the traditional relay model computer software technology, formed a unique style in relay ladder graph based image programming language, the PLC control system in the form of program to achieve its control function. In the simulation section, we implement our proposed system in detail and conduct numerical analtsis and simulation. The result is acceptable and accurate.

The main problem faced in supplying power to remote consumers is that there are high logistical costs of delivering fuel and equipment for diesel power plants, poor density of transport infrastructure and, as a consequence, a high cost of fuel. There are also high operating costs at diesel power plants and specific fuel consumption; in addition, there is no monitoring and control automation. In view of a high wind potential of the Arctic territories, energy complexes and systems can be effectively modernized and constructed on the basis of modular wind-diesel power plants with an intelligent control system. A concept of and hardware solutions for an intelligent automatic control system are proposed, the use of which makes it possible to maximize the amount of electricity generated by renewable sources owing to dynamically redistributing the power between the hybrid energy complex components and, as a result, to minimize fuel consumption. An analysis of a controlled wind-diesel power plant has shown that by using an intelligent automatic control system, forecasting the electricity generation by a wind power plant, and arranging storage battery operation in a cyclic mode, it becomes possible to effectively cover the load schedule of an autonomous consumer and increase the extent of diesel fuel substitution by up to 60 % or more.

Distributed Speech Recognition (DSR) systems rely on efficient transmission of speech information from distributed clients to a centralized server. Wireless or network communication channels within DSR systems are typically noisy and bursty. Thus, DSR systems must utilize efficient Error Recovery (ER) schemes during transmission of speech information. Some ER strategies, referred to as forward error control (FEC), aim to create redundancy in the source coded bitstream to overcome the effect of channel errors, while others are designed to create spread or delay in the feature stream in order to overcome the effect of bursty channel errors. Furthermore, ER strategies may be designed as a combination of the previously described techniques. This chapter presents an array of error recovery techniques for remote speech recognition applications. This chapter is organized as follows. First, channel characterization and modeling are discussed. Next, media-specific FEC is presented for packet erasure applications, followed by a discussion on media-independent FEC techniques for bit error applications, including general linear block codes, cyclic codes, and convolutional codes. The application of unequal error protection (UEP) strategies utilizing combinations of the aforementioned FEC methods is also presented. Finally, framebased interleaving is discussed as an alternative to overcoming the effect of bursty channel erasures. The chapter concludes with examples of modern standards for channel coding strategies for distributed speech recognition (DSR). 8.1. Distributed Speech Recognition Systems Throughout this chapter various error recovery and detection techniques are discussed. It is therefore necessary to present an overview of a complete experimental DSR system, including feature extraction, a noisy channel model, and an automatic speech recognition engine at the server end (Fig. 8.1). 2 Bengt J. Borgstrom, Alexis Bernard, and Abeer Alwan Feature Extraction Source Coding Channel Coding Channel Decoding Source Decoding Speech Recognition Client Server Noisy Channel input speech

This report presents a generalized design strategy of intelligent robust control systems based on quantum/soft computing technologies that enhance robustness of fuzzy controllers by supplying a self-organizing capability. It is demonstrated that fuzzy controllers prepared to maintain control object in the prescribed conditions are often fail to control when such a conditions are dramatically changed. We propose the solution of such kind of problems by introducing a generalization of strategies in fuzzy inference from a set of pre-defined fuzzy controllers by new quantum fuzzy inference based systems (prototype of intelligent system of systems engineering). We stress our attention on the robustness features of intelligent control systems.

With the development of network communication, computer technology and artificial intelligence, it has become an inevitable development trend to promote "intelligent buildings" characterized by intelligence, comfort and safety. In order to solve the shortcomings of the existing research on intelligent building control system, this paper discusses the functional equation of CAN algorithm, artificial intelligence fuzzy controller and intelligent building, and aims at the intelligent building control system based on artificial intelligence and CAN algorithm. Hardware settings and parameter settings are briefly introduced. And the work flow design of the intelligent building control system structure based on artificial intelligence and CAN algorithm is discussed, and finally the application of the intelligent building control system based on artificial intelligence and CAN algorithm is experimentally tested. The correct number of switch control by artificial intelligence and CAN algorithm in the intelligent building control system is relatively high. The correct number of switch control in the interference environment is less than that in the humid environment, and the number of switch operations for the control system is the least in the humid environment. The error rate of 500 and up to 2500 is less than 5%, thus verifying the superiority of the application of intelligent building control system based on artificial intelligence and CAN algorithm.

In recent years, very long instruction word (VLIW) processor has attracted much attention in that it offers a high instruction level parallelism and reduces the hardware design complexity. In this paper, we present two fault-tolerant schemes for VLIW processors. The first one is termed as test-instruction scheme which is based on the concept of instruction duplication to detect the errors. The process of test-instruction scheme consists of the error detection, error rollback recovery and reconfiguration. The second approach is called self-checking scheme which adopts the concept of self-checking logic to detect the errors. A real-time error recovery procedure is developed to conquer the errors. We implement the proposed designs of fault-tolerant VLIW processor in VHDL and employ the fault injection and fault simulation to validate our schemes. The main contribution of this research is to present the complete frameworks from error detection to error recovery for fault-tolerant design of VLIW processors. Experience learned from this investigation is that the issues of error detection and error recovery entail considering together. Without taking both issues into account simultaneously, the outcomes may lead to the improper conclusions.

This study investigates error detection and recovery for flexible assembly systems. A method for error recovery based upon an expert system is presented.

Based on heat balance analysis, the dynamic heating model of asphalt tank of mixture equipment was established, the heating time and using time nodes were determined, and the trapezoidal dynamic heating process of asphalt tank was also formed to realize the intelligent control and reduce energy consumption. After analyzing different schemes, the control scheme of the combination of feed-forward and feedback to realize intelligent control system for asphalt tank was adopted, which could ensure that the asphalt in the tank is in the operating temperature and the mixing equipment works continuously when the asphalt was used completely. In addition, a simulated analysis of calculation model, which was adopted by intelligent control system, was made in combination of entitative engineering. Results showed that compared with traditional process of heating asphalt, this intelligent heating control system could reduce the heat consumption by 15.8% under the condition of continuous construction.