Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method for changing the difference between the number of active controlled variables and the number of available manipulated variables in a multivariate control system. Controlled variables can be adaptively classified into one of an ordered plurality of classes, with each class having a rank that defines its position within the order. If the number of active valid controlled variables exceeds the number of available manipulated variables, the active controlled variables from the lowest ranking class having active controlled variables are then deactivated according to a predefined priority order by setting their associated setpoints equal to their measured values. Otherwise, inactive controlled variables from the highest ranking class having inactive controlled variables are reactivated according to the same predefined priority order by setting their associated setpoints to the desired setpoint values.

Abstract: 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.

Abstract: An apparatus and method for automatically adjusting the control parameters of a self-tuning controller used to regulate a process having a measured process variable signal. Using the measured process variable signal, an error signal representing a closed-loop response of the process to an upset condition is generated. Local extrema of the error signal is measured and three successive amplitude values are selected to produce measured decay and overshoot characteristics of the error signal. The three successive amplitude values are selected such that the measured decay characteristic is greater than the overshoot characteristic. Based on the measured decay and overshoot characteristics at least one of the control parameters of the controller is automatically adjusted to improve the difference between one of the measured characteristics and a target characteristic.

Abstract: 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.

Abstract: 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.

Abstract: Multivariable adaptive feedforward control may be accomplished by detecting the beginning and ending of a process control disturbance response, characterizing the measured inputs and process output during the disturbance by moments, which comprise time-weighted integrals performed on the process result output and inputs when the disturbance is a measured disturbance, and relating the characterized inputs and process result output in known general transfer function model equations to generate transfer function parameters which are used to calculate the coefficients of feedforward additive or multiplicative compensators.

Abstract: Apparatus and method for adapting the control parameters of a self-tuning controller. The apparatus detects a closed-loop response to a naturally occurring, unmeasured disturbance and determines two characteristics, the attenuation and the period of the response. The attenuation and the period provide sufficient information to partially identify the characteristic equation of the closed-loop system. The apparatus constructs from the partially identified characteristic equation a process model that represents the general behavior of the process. The apparatus generates new control parameters more appropriate for the identified process model, and appropriately updates the controller.

Abstract: An apparatus and method for process control that extracts information from a process for developing a model of the process that is used to design system control. The apparatus includes means for selecting a process model form that has two or more selectable parameters. The apparatus also includes means for deliberately disturbing a process that is in open-loop operation and that is in a substantially settled state and further includes means for measuring the process response. The apparatus selects parameters for the process model form according to a function of the measured process response. In this way a complete model of the process is identified. The apparatus self-tunes by directly calculating new control parameters according to a function of the identified open-loop process model and a preselected target behavior.

Abstract: Multivariable adaptive feedforward control may be accomplished by detecting the beginning and ending of load transients, characterizing the inputs and process result during the transient by moments, and relating the characterized inputs and process result in a general transfer function model to generate transfer functions relating the input to the output. The transfer functions coefficients are refined with successive disturbances by projecting from the current solution to the nearest point in an updated solution space. The transfer function coefficients of the selected solution are then used in compensators acting on the feedforward inputs to adjust a process control output to counteract the anticipated effects of the inputs.