Integrated control and diagnostics system

#The present invention is directed generally to systems that perform both control and diagnostics operations on a monitored system, and more specifically, a system in which the diagnostics system is integrated with the control system to provide optimum control and health assessment of the monitored system based on the output of both systems.

#Known control systems typically are feed-forward or feedback systems, which implement closed-loop control to obtain and maintain certain operating conditions. Conventionally, control systems are often used to maintain a prescribed controlled system operating state or condition such as temperature, speed, position, trajectory, torque, etc., or to achieve a prescribed system state such as for motion control and robotics applications. These systems typically implement a stable control law that works to maintain operating performance notwithstanding certain operational and/or physical constraints and external disturbances. Moreover, many of these systems exhibit non-linear characteristics. For example, servo actuator systems have inherent non-linear characteristics. Control design typically requires that a nonlinear system be treated as linear, usually by canceling the nonlinearity using feedback. These “linearizing” techniques have not found wide acceptance because they are generally not robust enough to operate in real-world applications. Often they are infeasible due to the large number of control inputs required to regulate the system. Consequently, these techniques have had relatively little use in solving real-world problems. For instance, the non-linearities associated with the aforementioned servo actuator systems cannot be ignored in control design. As such, a control system is needed that accounts for system nonlinearities using an alternative to correcting for such nonlinearlities with “linearizing” techniques.

#Some known control systems utilize non-linear control methods such as model-reference (MRAC), gain scheduling, controller scheduling, fuzzy logic, or feedback linearization, to dynamically modify the operation of the controller in response to sensed changes in system behavior. Such changes in behavior include different system dynamics including compliance, noise or other related changes. Moreover, such systems may be time varying and may be difficult or impossible to model for control purposes. Notably, these control systems typically operate in isolation from any diagnostic or prognostic systems.

#In the annexed drawings:

#FIG. 1 is a block diagram of a prior art system including independent diagnostics and control apparatus;

#FIG. 2 is a generic block diagram of an integrated diagnostics and control system according to a preferred embodiment of the present invention;

#Turning initially to FIG. 2, a controlled system 20 (e.g., a motor) includes an integrated control and diagnostics system 22 for operating a plant or process 21. System 22 includes both a control module 24 and a diagnostics module 26 in a tightly coupled hybrid configuration. With the integrated configuration, the control and diagnostics modules 24 and 26, respectively, are preferably packaged in a single unit and thus able to share a common set of sensors 28 to collect system data, and a common bus or shared memory to exchange data and information. Sensors 28 preferably comprise a plurality of sensors embedded in controlled system 20 to measure parameters associated with controlled system electrical/mechanical components 27 (for example, stator windings) including, inter alia, motor speed, motor current, voltage, temperature, vibration, magnetic flux, and lubrication characteristics. Although preferably implemented with cost-effective, off-the-shelf sensors, sensors 28 can be implemented with sensor technology that includes intelligent sensors, self-validating sensors and smart sensors with embedded diagnostics. In one preferred application, the hardware components of the system shown in FIG. 2 can be assembled to comprise an intelligent servomotor including a brushless DC motor with integrated control, diagnostics, communications and power electronics.

#Control module 24 is communicably integrated within controlled system 20 in a closed-loop feedback configuration and outputs an appropriate driving output to maintain operation of controlled system 20 at a predetermined set point value (e.g., at a particular velocity). In operation, control module 24 receives a data signal from sensor 28 and processes that signal to generate a driving output. In addition to applying the driving output to controlled system 20, system 22 can use the driving output to facilitate generation of an assessment of the health of the controlled system 20, or perform a system simulation. The health assessment is generated by an algorithm designed according to a particular model associated with controlled system 20, as discussed in further detail below. Integrated diagnostics module 26 is preferably of the type shown and described in U.S. Pat. No. 5,917,428 to Discenzo et al., which is expressly incorporated herein by reference. The module disclosed in Discenzo et al. uses an on-board processor and sensors to provide diagnostics for motor bearings and other mechanical and electrical components based on current, voltage, stator temperature, vibration measurements, etc. In addition to indicating the health of controlled system 20 to the operator, an output of diagnostics module 26 may be applied to control module 24, preferably as a recommended change in control when necessary.

#Overall, integrated control and diagnostics system 22 operates to efficiently utilize both control and diagnostics features to maximize, for example, the time-life trajectory (i.e., a relationship showing how the performance of the controlled system changes over time and eventual system failure and under various operating parameters) of controlled system 20 according to detailed user specifications. For example, if the output of diagnostics module 26 indicates that motor stator winding insulation failure is imminent, the drive algorithm (embedded in control module 24, described below) can be dynamically reconfigured accordingly to reduce the possibility of a premature, inconvenient or hazardous outage. Conversely, diagnostics can be optimized by stimulating the system via actuation of the control and thereafter observing the system response.