Abstract: A diagnostic tool automatically collects and stores data indicative of a variability parameter, a mode parameter, a status parameter and a limit parameter associated with each of the different devices, loops or function blocks within a process control system, processes the collected data to determine which devices, loops or function blocks have problems that result in reduced performance of the process control system, displays a list of detected problems to an operator and then suggests the use of other, more specific diagnostic tools to further pinpoint or correct the problems. When the diagnostic tool recommends and executes a data intensive application as the further diagnostic tool, it automatically configures a controller of the process control network to collect the data needed for such a tool.

Abstract: A non-linear process controller drives a process variable to be substantially equivalent to a set point based on the expected load disturbances within the process and on a measurement of the actual magnitude of set point changes. The controller uses control parameters developed in an optimal manner to control the process in response to expected load disturbances when no set point change has occurred. Whenever a set point change is detected, the controller derives a set of set point change control parameters and uses these set point change control parameters in responding to the set point change. Each of the set point change control parameters may be developed as a function of the magnitude of the actual change in the set point and according to a set of process characteristics which may include the time delay and the dominant time constant of the process.

Abstract: A method and apparatus determine the ultimate frequency and ultimate gain of a controlled process. The apparatus comprises a process gain and phase lag calculation module, a ultimate gain (K.sub.U) and ultimate period (T.sub.U) calculation module, and a tuning sequence control module. The tuning sequence control module initiates a tuning sequence and the process gain and phase lag calculation module samples the process input signal and the process output signal, and isolates from the process input signal and the process output signal at least one input sample signal and at least one corresponding output sample signal by bandpass filtering the process input signal and the process output signal at at least one observation frequency. The process gain and phase lag calculation module then calculates a phase lag of each output sample signal with respect to a corresponding input sample signal and a signal gain of each output sample signal with respect to a corresponding input sample signal.

Abstract: A Variable Horizon Predictor (VHP) compensates for dead time, measured disturbances, and time variant dynamics in process control loops. The VHP is comprised of a process model, an uncorrected prediction vector unit, a prediction correction unit, a corrected prediction vector unit, and a horizon selection unit 54. The VHP applies a controller output signal to the process model to form a prediction vector that is stored in the uncorrected prediction vector unit and predicts the process output signal over a prediction horizon. The prediction is corrected by the prediction correction unit, which compares the prediction vector to the process output signal. The corrected prediction vector is then stored in the corrected prediction vector unit. The horizon selection unit isolates a portion of the prediction vector to form a subvector and feeds the subvector to an error vector calculator to form an error signal that is applied to the controller.

Abstract: A system and method for automatically tuning a process controller used to control an industrial process, or the like. The controller is removed from closed-loop control of a process under control, and a signal is applied to the process under control to cause the process to undergo controlled self-oscillation. A first portion of the self-oscillation procedure is used to determine a Time Delay characteristic of the process under control, and a subsequent portion of the self-oscillation procedure as used to calculate an Ultimate Gain characteristic and an Ultimate Period characteristic of the process under control. These three characteristics are then used to calculate control parameters which are used to tune the controller. After tuning, the controller is placed back into a closed-loop with the process under control.