Using Advanced Control to Design More Efficient Machines
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Once you can measure your inefficiencies, you are on the way to creating a more effective design.
Specifically for machines, the ability and insight to understand issues, ranging from machine condition monitoring to predictive maintenance to power monitoring, reveals areas of opportunity for design improvements.
Figure 1. By accurately measuring an issue, you can more precisely detect the problem. This detection, combined with design techniques, offers a strong starting point for a better design.
Measurements Critical to Design Accuracy
Assume you have identified issues in your current machine or device; perhaps you are measuring reactor power consumption and realize you are pulling more power than needed. You can use this data as an input to your control algorithm and improve or fix power consumption. Similarly, you can measure machine vibration to discover potential failures or recognize inefficiencies. This real-world input is extremely valuable for creating accurate models to design more effective and efficient devices.
Figure 2. Algorithm engineering combines theoretical algorithm designs with real-world data from prototypes to provide more accurate end designs.
Algorithm Engineering to “Fix It”
Many applications, from automotive to medical device design, cannot rely on a “design only” or “simulation only” design process because of their critical nature. Rather, these sophisticated control applications require a hardware and software platform conducive to iteration and exploration. To truly apply the lessons learned in the measurement phase of your project, you must integrate real-world data for simulation and design validation. The sooner you can integrate this data, the shorter the overall process becomes and, oftentimes, the more accurate the model or final design proposal becomes.
The combination of theoretical algorithm design with real-world data is called algorithm engineering. By combining an algorithm with a real world hardware device, you can more accurately verify and validate the algorithm results and behavior. The real-world device can be a simple data acquisition or stimulus device, or you can take the algorithm and implement it on an embedded platform such as a field-programmable gate array (FPGA) or microprocessor similar to the final system design.
Figure 3. The LabVIEW Control Design and Simulation Module provides built-in control capabilities such as Kalman filters and MIMO state feedback control integrated with control hardware platforms, ensuring a reliable algorithm engineering and deployment platform.
The NI LabVIEW Control Design and Simulation Module is an ideal “fix it” tool not only for built-in design capabilities – including the ability to construct plant and control models using transfer function, state space, or zero-pole gain – but also for system performance analysis with tools such as step response, pole-zero maps, and Bode plots as well as the simulation of linear, nonlinear, and discrete-time systems with a wide variety of solvers. These software capabilities can be integrated with LabVIEW Real-Time to easily deploy your dynamic control systems.
Figure 4. Seaplace used LabVIEW control tools to design a reliable, real-time system capable of reading a variety of sensors, calculating control laws, and managing actuators on a ship to precisely control positioning.
Case Study – Dynamic Positioning on a Split-Hopper Vessel
Seaplace, an engineering and consulting firm offering services to both naval shipbuilding and offshore industries, needed to develop an efficient dynamic positioning (DP) system to implement on a split-hopper vessel. The purpose of the DP system is to interact with thrusters and propellers to maintain a fixed position and heading while the ship deposits stones to build a dike in the water. DP is a complex problem requiring information sensor acquisition including GPS and differential GPS, gyroscopes, chip logs, and anemometers to measure ship position and the effect of wind and current waves. The sensor information is fed into an estimation algorithm, such as a Kalman filter, to determine the actuation needed on the main propellers and side thrusters to keep the ship in place. These system specifications required software that provided algorithm engineering capabilities including dynamic system modeling as well as robust, reliable execution on a hardware system offering the computational power needed to run complex algorithms, real-time sensor acquisition, and actuator updates and redundancy.
The solution adopted by Seaplace relies on a PXI-based, high-performance, real-time controller used as the main system CPU where high-level algorithms are carried out while two NI CompactRIO systems simultaneously execute the code to control the thrusters. This FPGA-based hardware plays a key role in implementing safety guards to keep actuation under control in case of a link error with the master PXI system.
Better, More Efficient Design
By first gathering accurate measurement results to pinpoint your bottlenecks and inefficiencies, you can then use that real-world information to implement algorithm engineering techniques and design a better control system. Using the LabVIEW control platform, you can integrate reliable, real-time hardware and easy-to-use software to create a better, more efficient design, even for the most complex scenarios.
– Shelley Gretlein
Shelley Gretlein is the LabVIEW Real-Time and Embedded senior group manager. She holds bachelor’s degrees in computer science and management systems from the University of Missouri-Rolla.
– Javier Gutierrez
Javier Gutierrez is the product manager for LabVIEW control and simulation. He holds a bachelor’s degree in electrical engineering and a master’s degree in controls from the University of Málaga, Spain.
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This article first appeared in the Q2 2008 issue of Instrumentation Newsletter.
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