PROBEwatch - Online Control Loop Benchmarking (under development)
Nonlinear Control Design Toolbox (to be launched April 2006)
PROBE - Control Loop Benchmarking and Performance Assessment Software
PROBE is the ACTC's own control loop
benchmarking tool, which was launched in
February 2003. PROBE allows the performance of control loops to be compared against
a number of benchmarks, including the well known Minimum Variance (MV) benchmark, using standard
operating data. The more advanced benchmarks do require some knowledge of the process under control,
though for MV only the loop dead-time is required.
Control loop benchmarking using PROBE is a three stage process; firstly standard loop operating
data is collected, this is then imported into PROBE which calculates a % performance relative
to a theoretical best controller. This allows any poorly performing loops to be readily identified.
The vital final step involves taking appropriate corrective action (e.g. re-tuning, correcting a faulty
sensor or actuator, or eliminating a disturbance).
In its current form PROBE does not have the capability to identify what corrective action to take.
However, several ACTC members have expressed strong interest in taking this forward to make
something that is even more useful. Firstly, they are keen to have a tool that is more closely
integrated with the plant control systems, working on-line and providing continuous monitoring.
Secondly they are keen to have greater functionality, such as the automatic identification of dead-time
and suggestions for corrective action. The ACTC has taken these suggestions on-board, and will be
developing a new on-line version of PROBE.
The current version of PROBE operates standalone (i.e. does not require any other packages)
and runs in a MS-Windows environment. The software is available on a CD-ROM, which includes a
45-day evaluation license. For ACTC members licenses are free-of-charge, but are tied to the
duration of membership. Please contact us for a copy of
the CD-ROM or a new activation code.
Model Based Predictive Control Toolbox for Matlab
Version 1.2 now available - compatible with Matlab R12.1 and R13.
The Model-Based Predictive Control (MBPC) Toolbox for MATLAB is
an integrated graphical environment which allows engineers to
design and test predictive control algorithms without detailed
knowledge of the subject. This allows predictive controllers to
tried on system simulations very quickly, removing the design
burden from the engineer.
The Toolbox uses a step by step process to allow the user to
synthesise a predictive controller. The fixed inputs and desired
outputs of the system are specified before simulation. The package
automatically generates Kalman filter settings and predictive
controller parameters which stabilise the system. Finally,
a simulation of the system incorporating the designed predictive
controller can be used to evaluate performance.
The package includes the following features:
controller
designs for both unconstrained and dynamically constrained operation
static
optimisation
state-space
framework, useful for large systems
standard
GPC and the new LQGPC algorithms, which provides improved robustness
reference,
disturbance and measurement noise models
a
full nonlinear gas turbine model for demonstration purposes
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NOW
Genetic Algorithms for System Identification Toolbox for MATLAB
This software accompanies the ACTC Case Study Report
"Genetic Algorithms for System Identification" (CS20/2000),
and it should be used in conjunction with the Case Study report.
The software is written for MATLAB. SIMULINK is only required
to generate test data and to run the demonstrations.
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NOW
6 DoF Non-linear Ship Modelling Package for Matlab/Simulink
This toolbox extends the functionality of the 4DoF package to provide
more ship models, including underwater vehicles, and the full six
degrees of freedom systems. Propulsion systems, weapons systems and
environmental models can all be easily incorporated into the
vehicle models.
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NOW
4 DoF Non-linear Ship Modelling Package for MATRIXX
This toolbox has been developed by the ACTC for the Marine SIG and
consists of a 4DoF non-linear dynamic ship model built using SystemBuild.
The present model is for a high speed container ship, though other ship
models and additional features will be developed in the future to create
a truly useful and generic model.
EASY_KIT Toolbox for MATLAB
EASY_KIT is an integrated graphical front end for MATLAB that allows
engineers in industry to design robust controllers (PID, LQG, H2
and H∞) without the need for in-depth expertise
of the various MATLAB control toolboxes and associated theory. The package
integrates all the general stages of control design, including plant model
specification, weighting function tuning, frequency and time responses
and linear or non-linear simulations. A worked example looks at the dynamic
ship positioning problem and an on-line tour guide of the package is included.
Self-Tuning Control Software
This linear identification and control design tool for MS-DOS uses Recursive
Least Squares algorithms (Peterka's square root filtering or Bierman's
UD factorisation) to identify a discrete plant model. Controllers can be
specified as open loop, fixed gain PID, self-tuning PID, self-tuning LQG,
self-tuning H∞ or self-tuning generalised
predictive control (GPC). The ability to interface to a real plant via
a serial communications link is included, and is specifically set-up for
a Turnbull TCS controller. This can be re-programmed by the user for use
with other systems.
Nonlinear Self-Tuning Control Software
This is an extension of the above Linear Self-tuning Control Software
package, where the identification and plant can now include nonlinear elements.
A Recursive Extended Nonlinear Least Squares algorithms (using Bierman's
UD factorisation) is used to identify the discrete plant model and the
same controllers as provided in the Linear Self-Tuning package are available.
The ability to interface to a real plant via the serial link still exists.
The improved user interface now works under MS-Windows.
Multivariable Robust Control Toolbox for MATLAB
This package is used to design and test optimal multivariable robust
controllers and acts as a Graphical User Interface extending the MATLAB
Robust Control Toolbox. Both LQG and H∞
controllers can be designed using either the state-space approach (using
the Robust Control Toolbox) or the polynomial approach (internal to the
MRC Toolbox). The analysis can be in either frequency or time domain.
Note: This toolbox operates in the OpenWindows or X-Windows environments
running on a Sun workstation.
Robust H2 Feedback/Feedforward Control Design Toolbox
for MATLAB (Polynomial Approach)
This toolbox complements the MATLAB Robust Control Toolbox by implementing
robust controller designs, either H2, H∞
or mixed H2/H∞, using the polynomial
approach. An interactive menu allows the user to enter plant models, gains
and weightings associated with the controller. Both frequency and time
domain analysis of open loop and closed loop systems may be performed.
Includes four design examples and an on-line tour guide of the package.
H∞ Robust Control Toolbox
for MATLAB
This toolbox is intended for solving some frequently encountered scalar
LQG and H∞ control problems, both in continuous
and discrete time. Generalised or mixed sensitivity H∞
controllers or standard or generalised LQG controllers can be specified,
coupled with appropriate filter elements for the weightings. Both frequency
and time domain analysis of closed loop systems are available.