The Foxboro Company

Invensys Systems, Inc.

Paul C Badavas

Peter Daniel Hansen

Richard L Thibault

Thomas B Kinney

Bo Ling

T Eric Christiansen

Alexander Park Johnson

Seyamak Keyghobad

Nicholas P DiVittorio

Nicolaas E Hemkes

The invention provides improved methods and apparatus for control using field and control devices that provide a virtual machine environment and that communicate via an IP network. By way of non-limiting example, such field device can be an 'intelligent' transmitter or actuator that includes a low power processor, along with a random access memory, a read-only memory, FlashRAM, and a sensor interface. The processor can execute a real-time operating system, as well as a Java virtual machine (JVM). Java byte code executes in the JVM to configure the field device to perform typical process control functions, e.g., for proportional integral derivative (PID) control and signal conditioning. Control networks can include a plurality of such field and control devices interconnected by an IP network, such as an Ethernet.

Methods and apparatus for control using control devices that provide a virtual machine environment and that communicate via an IP network

The provides improved control devices, systems and methods for operation thereof. These rely on control devices that provide virtual machine environments in which Java objects, or other such software constructs, are executed to implement control (e.g., to monitor and/or control a device, process or system). These objects define blocks which are the basic functional unit of the control. They also define the input, output and body parts from which blocks are formed, and the signals that are communicated between blocks. The objects also define nested and composite groupings of blocks used to control loops and higher-level control functions.

Methods and apparatus for object-based process control

A control system has blocks or other components that facilitate validation of their own replacements, e.g., downloaded via e-commerce transactions. The system includes first and second process control components. The first component is coupled to a third process control component, with which it transfers information, e.g., as part of an active or ongoing control process. The second component can be, for example, an update or other potential replacement for the first component. The first and/or second components can effect substitution of the second component for the first. More particularly, they can effect coupling of the second component for information transfer with the third component and decoupling of the first component from such transfer with the third component. Preferably, such coupling and decoupling occur while the process control system remains active.

Process control system and method with improved distribution, installation and validation of components

A method and apparatus for training and optimizing a neural network for use in controlling multivariable nonlinear processes. The neural network can be used as a controller generating manipulated variables for directly controlling the process or as part of a controller structure generating predicted process outputs. The neural network is trained and optimized off-line with historical values of the process inputs, outputs, and their rates of change. The determination of the manipulated variables or the predicted process outputs are based on an optimum prediction time which represents the effective response time of the process output to the setpoint such that the greatest change to the process output occurs as a result of a small change made to its paired manipulated variable.

Method and apparatus for providing multivariable nonlinear control

A method and apparatus for a robust process control system that utilizes a neural-network multivariable inner-loop PD controller cascaded with decoupled outer-loop controllers with integral action, the combination providing a multivariable nonlinear PID and feedforward controller. The inner-loop neural-network controller is trained to achieve optimal performance behavior when future process behavior repeats the training experience. The outer-loop controllers compensate for process changes, unmeasured disturbances, and modeling errors. In the first and second embodiments, the neural network is used as an inner-loop controller in a process control system having a constraint management scheme which prevents integral windup by controlling the action of the outer-loop controllers when limiting is detected in the associated manipulated-variable control path. In the second and third embodiments, the neural-network controller is used without the integral controllers or the constraint management scheme as a simple PD feedforward controller.

Multivariable nonlinear process controller

A method and apparatus for a robust process control system which utilizes a neural-network based multivariable inner-loop PD controller cascaded with decoupled outer-loop controllers with integral action, the combination providing a multivariable nonlinear PID and feedforward controller. The inner-loop PD controller employs a quasi-Newton iterative feedback loop structure whereby the manipulated variables are computed in an iterative fashion as a function of the difference between the inner loop setpoint and the predicted controlled variable as advanced by the optimum prediction time, in order to incorporate the downstream limiting effects on the non-limited control loops. The outer-loop controllers compensate for unmodeled process changes, unmeasured disturbances, and modeling errors by adjusting the inner-loop target values.

Method and apparatus for controlling multivariable nonlinear processes

An improved setpoint control system is disclosed for use in conjunction with process control equipment. The system generates signals representing a statistical characteristic of batches, or subgroups, of monitored values of a controlled variable. From these statistical characteristic signals, the system generates an optimum setpoint signal as a sum of a specification limit and a scaled value of the standard deviation of averages of each of a plurality of batches controlled variable values.

Method and apparatus for process control with opimum setpoint

A statistical process control system provides asymmetrical nonlinear automatic closed-loop feedback control. The system has application, for example, in the control of equipment that responds to a controlled variable signal to vary a measurable characteristic of a process. The system uses an accumulated deviation of a measured subgroup means minus a target value, divided by the subgroup standard deviation, to modify the manipulated variable each time the accumulated value exceeds a decision interval above or below the target. The system permits the use of independent slack variables subtracted from the accumulated deviation to more closely model the underlying process. Likewise, the system permits the use of independent alarm values and variable gains to permit greater control of the process.

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Southboro, MA, United States of America

Paul C Badavas