Abstract: An MFC controlling device automation method and related system. The method mainly comprises the steps of automatically controlled algorithmic obtaining of MFC open loop system parameters by an identification technique; automatically controlled algorithmic determining of PI control parameters by a dominant pole-placement method; and automatically controlled calculating of a fuzzy logic rule table. The steps form an automatically controlled single closed loop. By performing said steps, multiple PI control parameter sets are obtainable fast and in an automatic way, and a calculation is done to form a refined fuzzy rule data table, so that a greatly enhanced MFC reaction function is attained.

Abstract: A method of controlling the movements of a multi-actuator electromechanical system having a matrix of locally interconnected analog cells associated therewith is provided. Each cell represents a hardware implementation of a model of fuzzy inference rules. The model includes a fuzzy circuit architecture which may be implemented in an integrated circuit with VLSI CMOS technology that generates and controls a reaction diffusion mechanism typical of auto-waves using a fuzzy neural network. The fuzzy neural network defines the functional relationships that may duplicate simultaneous reaction diffusion equations. The duplication of the simultaneous reaction diffusion equations is provided using two sets of fuzzy rules processing, in a linguistic manner, the state variables of the cells. An oscillatory type dynamic is imposed on each cell where two dynamic processes having different kinetic characteristics coexist.

Abstract: An MFC controlling device automation method and related system. The method mainly comprises the steps of automatically controlled algorithmic obtaining of MFC open loop system parameters by an identification technique; automatically controlled algorithmic determining of PI control parameters by a dominant pole-placement method; and automatically controlled calculating of a fuzzy logic rule table. The steps form an automatically controlled single closed loop. By performing said steps, multiple PI control parameter sets are obtainable fast and in an automatic way, and a calculation is done to form a refined fuzzy rule data table, so that a greatly enhanced MFC reaction function is attained.

Abstract: An aerial work platform supported by a riser boom, a telescoping main boom, and a jib boom. Boom movement may be controlled by a platform control module or a ground control module connected to a controller by a controller area network (CAN). Movement of the platform and the jib boom are limited to a predefined envelope. If an operator attempts to move the platform outside the envelope, the controller automatically retracts the telescoping boom section or automatically levels the jib boom section in order to maintain the platform within the acceptable envelope. Boom section select switches permit the operator to select and move sequentially or simultaneously in different directions. Timers which are part of the system include various interlocks to accomplish safety and power saver features.

Abstract: An integrated control system for a machine such as an engine. The machine is composed of plurality of components such as a throttle valve and a fuel injector. The control system has a plurality of control modules for controlling the respective components and finally controlling the machine through the control of the components. Each control module is associated with at least one parameter for controlling each component. The parameter is evolved under genetic algorithm so as to be adapted to at least one of a predetermined characteristic that is a target of the machine, a characteristic of a user who uses the machine, a using condition and an environmental condition of the machine.

Abstract: A method for transformation of fuzzy logic (FS) into a neural network (NN), in which, in order to form a defuzzified output value (y2) from normalized single-element functions (F1 . . . Fm), the single-element functions (F1 . . . Fm) are each assigned a singleton position (A1 . . . Am) and at least one singleton weighting factor (R1 . . . Rn), those singleton weighting factors (R1 . . . Rn) which are assigned to the same single-element function (F1 . . . Fm) are additively linked, and the singleton weighting factors (R1 . . . Rn) and the additively linked singleton weighting factors (R1 . . . Rn) are weighted via the corresponding singleton positions (A1 . . . Am) and are additively linked in order to form the defuzzified output value (y2). One advantage of the method according to the invention is that the singleton positions (A1 . . .

Abstract: In a process for titrating substances by adding increments of a titrant in time intervals, the course of the reaction is monitored in its approach toward the end point (EP), and the addition of titrant is controlled. At least one differential component is determined from the course of the reaction and used for controlling the addition of titrant. The process can be performed by an apparatus that includes a delivery unit for the titrant and suitable devices for performing the monitoring, differentiating and control functions.

Abstract: The method according to the present invention is used for automatically configuring a fuzzy automatic-control device, in the form of generating transformation criteria. These transformation criteria preferably have change, stop, jump-back and reset rules for the individual states of the fuzzy automatic-control device that represent recognition probabilities. The method allows transformation criteria for configuring fuzzy automatic-control devices to be produced fully automatically by the arithmetic logic unit. Even if the pattern signals change frequently, the fuzzy automatic-control device can thus be reconfigured quickly, flexibly and without complications.

Abstract: Control of an interconnection process for optical fibers involves evaluating the status of all modules used in the process and moving an optical fiber through the process in accordance with the status of the modules. Such control allows more than one fiber, and more than one fiber type, to be processed simultaneously. The processing may be optimized using fuzzy logic to track when operations performed by a module are near completion. A graphical user interface displaying the movement of the optical fibers through the process may also be provided.

Abstract: Control of an interconnection process for optical fibers involves evaluating the status of all modules used in the process and moving an optical fiber through the process in accordance with the status of the modules. Such control allows more than one fiber, and more than one fiber type, to be processed simultaneously. The processing may be optimized using fuzzy logic to track when operations performed by a module are near completion. A graphical user interface displaying the movement of the optical fibers through the process may also be provided.

Abstract: An agricultural harvesting machine (harvester) is automatically controlled and steered so as to completely harvest all crop in a field. A Global Positioning System (GPS) and an Inertial Navigation System provide data regarding the position and orientation of the harvester in a field. A Field Coverage Planner constructs a field coverage plan which defines a path for the harvester to follow in cutting crop from a field. Video images of the field in front of the harvester are derived alternately from two video cameras mounted on opposite sides of the harvester. The video images are processed in a Video Processing Computer to detect the crop line between cut and uncut crop and generate steering signals for steering the harvester along the crop line. Video images are also processed to detect the end of a row at the end of a field and supply a measure of the distance to the end of the row.

Abstract: A system and method for automatically tuning a fuzzy logic process controller is presented. A process under control is automatically assessed to determined process control characteristics including, for example, ultimate gain, ultimate period and time delay. Then, these quantities are used to calculate tunable control parameters which include scaling factors, which are applied to the fuzzy logic controller in order to optimize the performance of the fuzzy logic controller when connected to control the process.

Abstract: This invention relates to a method and apparatus for the control of injection molding and, in particular, for determining a suitable time for transfer from injection velocity control to packing pressure control. The invention uses a derivative of, preferably the nozzle pressure, and inputs this to a fuzzy inference system. The fuzzy inference system uses said rules and knowledge to provide a suitable transfer time as an output. It is preferred that the sample pressure is filtered prior to calculation of the derivative.

Abstract: Methods and apparatus for batch process control in which a Phase Logic Module operable in accordance with the state machine model of the batch server program is embedded within a programmable controller. More specifically, a phase executed by the batch server program in a data processing device is operable in accordance with a pre-defined state machine model (typically compliant with the ISA S88.01 standard). A Phase Logic Module (PLM) is implemented within a programmable controller in accordance with the same state machine model so as to mirror the state machine operation of the batch server process within the programmable controller. The PLM is pre-implemented and standardized. Specific state logic for performing desired control in accordance with the desired batch processing is defined by a control engineer using well known languages and tools and integrated with the standard PLM state machine.

Abstract: Control of an interconnection process for optical fibers involves evaluating the status of all modules used in the process and moving an optical fiber through the process in accordance with the status of the modules. Such control allows more than one fiber, and more than one fiber type, to be processed simultaneously. The processing may be optimized using fuzzy logic to track when operations performed by a module are near completion. A graphical user interface displaying the movement of the optical fibers through the process may also be provided.

Abstract: Method of simultaneously carrying out manufacturing and product engineering integrated with knowledge networking.

Abstract: A distributed control system (14) receives on the input thereof the control inputs and then outputs control signals to a plant (10) for the operation thereof. The measured variables of the plant and the control inputs are input to a predictive model (34) that operates in conjunction with an inverse model (36) to generate predicted control inputs. The predicted control inputs are processed through a filter (46) to apply hard constraints and sensitivity modifiers, the values of which are received from a control parameter block (22). During operation, the sensitivity of output variables on various input variables is determined. This information can be displayed and then the user allowed to select which of the input variables constitute the most sensitive input variables. These can then be utilized with a control network (470) to modify the predicted values of the input variables. Additionally, a neural network (406) can be trained on only the selected input variables that are determined to be the most sensitive.

Abstract: A system and method for monitoring an industrial process and/or industrial data source. The system includes generating time varying data from industrial data sources, processing the data to obtain time correlation of the data, determining the range of data, determining learned states of normal operation and using these states to generate expected values, comparing the expected values to current actual values to identify a current state of the process closest to a learned, normal state; generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm upon detecting a deviation from normalcy.

Abstract: A manufacturing process of a large part of composite materials with a high fiber content (more than 58%) using the resin transfer moulding technique, wherein a fiber pellet (26) is placed in a mould (20) and strongly compressed, whilst the resin is injected at a constant reference flow rate value and while the instantaneous pressure at the mould inlet is lower than a reference pressure value. When this reference pressure value is reached, the injection flow rate is reduced to maintain the pressure at this level, and the compression of the pellet (26) is temporarily reduced. The injection flow rate is then altered to the constant reference flow rate value and the compression is increased, to start another cycle.

Abstract: A method of controlling automatic state returns relates to a dynamic system coupled to an automatic control operating in a discrete negative feedback mode. The method includes a phase for defining a controller, and a phase for operating the system. The defining phase is realized in an open-loop learning phase which includes a step of reading states in which a system may be situated, and a step of generating commands for returning the system to an ideal state. When the commands generating step takes place, the method supplies, for each state, a sequence of discrete commands which are determined by imposing a constraint intended to reduce the error relative to that state below a predetermined threshold.