Finite sample properties of system identification for control.
The asymptotic convergences properties of system identification methods
are well known, but comparatively little is known about their behaviour
for the practical case when only a finite number of data points are available
for system identification. One open problem is the following: Assume the
true system is a linear system of known order but with unknown parameters.
The system parameters are estimated using least squares identification,
and a controller, e.g. a pole placement controller, is designed based on
the identified model. What is the probability that the resulting closed
loop is stable? How many data points do we need for identification in order
to guarantee with high probability, say 0.9995, that the closed loop system
is stable? Given that we know that the model has been obtained by a system
identification experiment, can we design the controller in such a way that
the closed loop system is stabilised with the highest possible probability?
System identification for pole placement control
System identification and control design are an iterative process,
and several schemes for iterative identification and control have been
developed. The basic steps in such schemes are: First a model is
identified using open loop data, and a controller is designed. The
controller is implemented, and the system is operated in closed
loop. During closed loop operation more data is collected and the
system is re-identified using closed loop data. The controller is then
redesigned based on the new model. The controller is implemented, and
the whole process is repeated until the controller performance is
satisfactory. In this project we will focus on pole placement control,
and question we will address is: How should the identification
criterion be chosen? How should the input signal (in open loop) be
chosen? Should the data be filtered in a particular way before we use
them for system identification? What is the best way of combining data
collected in open and closed loop? Does the parameter estimate
converge as the number of identification/control iterations tends to
Modelling and control of open water channels.
A number of projects are available in this area. Some of them are listed below.
Control of large irrigation networks.
Irrigation networks can be large with hundreds of kilometers of channels and a large number of gates for regulation of water levels and flows. A typical approach to control design is to start with a small portion of the channel and design a controller for this portion, and then extend the design to incorporate more and more gates and water levels. However, there is no guarantee that a control strategy which works well for a small part of the channel will also work well for the total channel networks. In this project we will investigate which control strategies scales well and if particular conditions must be satisfied for the controllers to scale well.
Controller configurations for decentralised control of irrigation channels.
A feature of a decentralised control strategy is that one gate controls one water level. Advantages of such a control strategy is that the design of the controllers is relatively easy and the communication requirements, i.e. the amount of data that has to be sent over a radio network, are small. A drawback is that the performance will not be as good as for a centralised multivariable controller. With a decentralised controller there is still a choice of configuration, e.g. which water level should a gate control. Two common strategies are upstream control, where the gate controls the immediate upstream level, or distant downstream control where it controls the water levels immediately upstream of the next downstream gate. Another strategy is very distant downstream control where a gate controls the water level upstream of a gate far downstream, and the intermediate gates are in upstream control mode. In this project we will study different controller configuration for decentralised control, and assess there performance and compare with multivariable controllers. Another research problem to be adressed is the development of guidelines for decentralised controller configurations taking into account the geometry of the channel and the control objectives
Multivariable control of irrigation channels
In this project we will study design of multivariable controllers for irrigation channels. Multivariable controllers differs from decentralised controllers where one gate is responsible for the control one water level, in the sense that all gates are contributing to the regulation of all water levels. Generally we expect to get better performance out of a multivariable controller, but they are also more difficult to design. As data from irrigation channels has to be sent over a radio network, there are communication constraints, and design of multivariable control systems where the signals are sampled at different rates becomes important. Another issue which must be addressed in this project is controller reduction, that is based upon the designed multivariable controller, find a controller which is simpler and easier to tune and implement, but with nearly the same performance as the full multivariable controller.
Initial control design using physical data only.
Relevant operational data are quite often missing when an automatic control system is installed for the first time on an irrigation channel. The initial choice of controller parameters must therefore be based on geometric data, such as length, width, slope and roughness of the irrigation channel. In this project we will investigate how to tune controllers based on such physical information, and how to improve the controllers when operational data such as water levels and gate positions, become available.
Performance monitoring and fault detection in irrigation channels.
Due to the sheer size of irrigation networks and the number of gates and sensors involved, faults are bound to occur. The most typical faults are sensor failure (water levels and gate positions) and actuator failure (motors driving the gates). Some failures are easy to detect, but other such as a sensor slowly drifting off can be difficult to detect. In other cases it can be easy to detect that something is wrong, but not what has gone wrong, e.g. has the gate got stuck or is it the gate position sensor that is faulty? In this project we will develop a fault detection and performance monitoring system. Such a system will monitor the data from the irrigation channel, extract relevant information and make comparison with the expected behaviour of the irrigation channel. If the difference between what is observed and what is measure is large an alarm is raised.
More projects are listed on CSSIP's webpage.
Interested in environmental problems, modelling, control and signal processing?
More projects are available, both of a theoretical and practical nature, contact email@example.com
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