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LQG-Benchmark Performance Assessment
The Linear Quadratic Guassian (LQG)-benchmark for Performance Assessment (LQGPA) programme was developed in MATLAB® to allow easy performance assessment of control loops (both SISO and MIMO cases) using a subspace approach and LQG as benchmark. This programme was developed by the Computer Process Control Group at the University of Alberta.
Assessing the performance of a control loop needs some optimal control law as benchmark. Unlike in the minimum variance case, the LQG benchmark focuses on both the input and output variances and provides a “trade-off” curve, which represents the feasible performance range for linear controllers. Different performance indices can be defined for a process with respect to LQG curve. These indices can provide useful information such as how good the current controller performance is and if further tuning of the controller is required.
The algorithm used by programme can be stated in the following steps:
- First, either some data obtained from an open-loop or closed-loop identification experiment or the actual process model is needed.
- If the data are given, identification is performed to determine an estimated model for the process.
- Using the model, calculations are performed to obtain the LQG-trade-off curve, which is used as the benchmark.
- The required minimum variances are determined.
- Finally, all the performance indices are determined.
Manual
The manual for using the toolbox can be found here.
System Requirements
This programme has the following prerequisites:
- Windows XP or newer.
- MATLAB® 2006a or newer. There may be issues with more recent (MATLAB® 2009a or newer) version of MATLAB®. The programme has not been tested on older versions of MATLAB®.
- System Identification Toolbox from MATLAB®.
Download
The files can be downloaded from the Downloads section.
Developers
This software has been developed by the following people:
References
- Nima Danesh Pour, Biao Huang, and Sirish L. Shah (2009). “Consistency of noise covariance estimation in joint input–output closed-loop subspace identification with application in LQG benchmarking” in Journal of Process Control, Volume 19, pp. 1649-1657. doi: 10.1016/j.jprocont.2009.10.005