The main focus of about this thesis is to design an anti-interference depth controller for underwater remotely operated vehicles(ROV). Since the underwater remotely operated vehicle experiences combination effects of nonlinearities, uncertain and time-varying parameters, and unknown disturbances, demand of robustness for the controller needs to be extremely strict. Therefore, an anti-interference depth controller using PID control and Sliding-mode control is developed. The Matlab simulation tool is employed to simulate the depth control performance of the behavior of the ROV. The simulation is also considered about the model uncertainty of ROV.

The purpose of this Master Thesis is to improve the driving performance of mine hoists. The work is divided into two parts. The first and main part deals with simulation of the rope elongation that occurs at load changes in the mine hoist. A mathematical load model of the elongation in the ropes at a mine hoist is made for four types of mine hoists. Mass less springs and dampers are used to get the elastic behaviour of the ropes. The mathematical model is implemented in Matlab and Simulink for all four hoist types to make load simulations possible. The implementation in the laboratory HoistLab is made by modifying an existing program with the line elongation functionality. It is only done for the tower mounted friction hoist. There are several functions that are modified to make the simulations realistic. The task for the second part of this Master Thesis is to do a pilot study to decide if it is worth making further investigations about how the derivative part will improve the drive performances. A PI controller is designed and gives an acceptable rollback as result when the brakes are released. Then the controller model is extended with the derivative part, D-part, which improves the results essentially. It is still too uncertain how sensitive the system will be for noise when using the derivative part, but the performance potential is clear so the recommendation is to make further investigations. / Syftet med detta examensarbete är att förbättra driftegenskaperna för gruvspel. Arbetet är uppdelat i två olika delar. Den första och största delen handlar om simulering av den lintöjning som uppkommer vid lastförändringar i gruvspel. Matematiska modeller för detta är framtagna för fyra olika sorters typer av gruvspel. Elasticiteten i linorna är modellerad genom masslösa fjädrar och dämpare. De matematiska sambanden är implementerade i Matlab och som modeller i Simulink för att utföra simuleringar. I HoistLab är modellen realiserad genom att utöka ett befintligt lastsimuleringsprogram med de nya funktionerna för lintöjning. Detta är utfört enbart för den toppmonterade typen av friktionsspel. Ett flertal funktioner fick ändras för att få realistiska simuleringar. Den andra delen av examensarbetet går ut på att göra en förstudie kring den deriverande delen i PID-regulatorer och hur den påverkar gruvspelets prestanda. För denna del är en PI-regulator som ger ett acceptabelt resultat av backgången när bromsarna släpps designad. Därefter är modellen utökad med den deriverande delen, D-delen, vilket ger väsentligt bättre resultat. Det är dock osäkert hur bruskänsligt systemet blir när den deriverande delen används men eftersom förbättringspotentialen är tydlig är rekommendationen att göra vidare undersökningar kring D-delen.

This thesis reports the design and control of the ??GreenWeeder??, a non-herbicidal autonomous weeding robot, in order to autonomously track crop rows for weeding through electrocution in the inter-row space. The four wheel mobile robot platform was designed and built with a motorised Ackerman steering system allowing the robot to steer up to 30 degree left and right. It was also equipped with an electronically geared rear wheel drive, a pair of stereo cameras, a SICK LMS-291 laser range finder to localize itself with respect to the crop rows, a GPS system for obtaining the robot position in the field and a long-range communication system for tele-supervision by operators. The first prototype of the robot electrocution system was also designed and constructed to ignite 22kV electrical arcs to destroy weeds. Its operation was tested in the research field of the University of Sydney and the results of this experiment were analysed to improve the efficiency of this first prototype. An improved prototype of the electrocution system was then constructed and attached to a cradle extending out at the back of the mobile robot platform. The testing of this improved prototype was conducted at Lansdowne farm, a research field of the University of Sydney. After the construction of the robot platform, the robot control was considered with the demands for robot localization with respect to crop rows, an autonomously tracking control system and a manual control mode in order to take the robot to transportation vehicles. Firstly, the robot localization was accomplished by utilizing SICK LMS-291 laser range finder sensor for the sensing and perception of the robot. Secondly, the robot control system was developed with a PID controller, a second order model of the robot system and a first order filter. The PID controller is in the standard form with the filtered derivative and the PI part being in automatic reset configuration. The second order model was identified using Matlab System Identification toolbox based on the robot kinematic analysis. The first order filter is utilized for filtering out the lateral deviations of the robot with respect to the crop rows received from the SICK laser sensor. A Simulink simulation model of the robot control system was also built in order to fine-tune PID and filter parameters. Thirdly, the manual control mode of the robot was produced. In this mode, a joystick can be attached to a notebook to wirelessly drive the robot around or it can be plugged into a USB port at the back of the robot to drive it without the notebook. After the robot control was implemented and simulated, some experiments were conducted with the robot autonomously tracking a strip of reflective tape mimicking a crop row stuck into the ground of a laboratory. Depending on distances from the row assigned to the controller, the robot tried to keep those distances away from the row. The results showed the lateral errors of the robot with respect to the row were approximately 4.5 cm which were sufficient for our current agricultural application.

This thesis reports the design and control of the ??GreenWeeder??, a non-herbicidal autonomous weeding robot, in order to autonomously track crop rows for weeding through electrocution in the inter-row space. The four wheel mobile robot platform was designed and built with a motorised Ackerman steering system allowing the robot to steer up to 30 degree left and right. It was also equipped with an electronically geared rear wheel drive, a pair of stereo cameras, a SICK LMS-291 laser range finder to localize itself with respect to the crop rows, a GPS system for obtaining the robot position in the field and a long-range communication system for tele-supervision by operators. The first prototype of the robot electrocution system was also designed and constructed to ignite 22kV electrical arcs to destroy weeds. Its operation was tested in the research field of the University of Sydney and the results of this experiment were analysed to improve the efficiency of this first prototype. An improved prototype of the electrocution system was then constructed and attached to a cradle extending out at the back of the mobile robot platform. The testing of this improved prototype was conducted at Lansdowne farm, a research field of the University of Sydney. After the construction of the robot platform, the robot control was considered with the demands for robot localization with respect to crop rows, an autonomously tracking control system and a manual control mode in order to take the robot to transportation vehicles. Firstly, the robot localization was accomplished by utilizing SICK LMS-291 laser range finder sensor for the sensing and perception of the robot. Secondly, the robot control system was developed with a PID controller, a second order model of the robot system and a first order filter. The PID controller is in the standard form with the filtered derivative and the PI part being in automatic reset configuration. The second order model was identified using Matlab System Identification toolbox based on the robot kinematic analysis. The first order filter is utilized for filtering out the lateral deviations of the robot with respect to the crop rows received from the SICK laser sensor. A Simulink simulation model of the robot control system was also built in order to fine-tune PID and filter parameters. Thirdly, the manual control mode of the robot was produced. In this mode, a joystick can be attached to a notebook to wirelessly drive the robot around or it can be plugged into a USB port at the back of the robot to drive it without the notebook. After the robot control was implemented and simulated, some experiments were conducted with the robot autonomously tracking a strip of reflective tape mimicking a crop row stuck into the ground of a laboratory. Depending on distances from the row assigned to the controller, the robot tried to keep those distances away from the row. The results showed the lateral errors of the robot with respect to the row were approximately 4.5 cm which were sufficient for our current agricultural application.

Automatic control is used to operate all kinds of processes: everything from temperature in houses to the control of robots. The course in automatic control in Uppsala University includes laboratory experiments where students conduct tests on a wheel controlled by a, so called, PID-controller which is one of the most widely used control mechanisms. This is a prime opportunity for students to get practical experience of working with PID-regulators and test how different parameters influence results. That system has been improved in this project as there were previously several issues with the system. The system was buggy and one of the tasks where the students are to test the systems’ reaction to oscillating input signals was cancelled because that feature had not been implemented yet. These issues were successfully fixed in this project and all tasks are now doable. Another problem was (and is still to some degree) that a lot of measurement noise occurs when measuring speed. This in turn causes the part of the controller that is sensitive to noise (the derivative part, for those familiar with PID-controllers) to function poorly. Some improvement has been made to this by using low-pass filtering for control purposes and the least square method for display purposes, but the signal is still noisy. The key to solving this issue lies in implementing an algorithm that can precisely estimate the speed without distorting any other information, or alternatively buy sensors with higher precision.

Yes / Multistage Flash Desalination (MSF) is currently facing an enormous challenge in cutting of the cost: within the last few years, the MSF experienced a gradual decline in investment compared to other techniques of desalting water and thus, a significant improvement is required to remain attractive for capital investors. Improved process control is a cost effective approach to energy conservation and increased process profitability. In this work, a dynamic model is presented using gPROMS model builder to optimize and control MSF process. The Proportional Integral Derivative Controller (PID) and Generic Model Control (GMC) are used successfully to control the Top Brine Temperature (TBT) and the Brine Level (BL) in the last stage at different times of the year. The objectives of this study are: firstly, to obtain optimum TBT and BL profiles for four different seasons throughout the year by minimizing the Total Seasonal Operating Cost (TSOC); secondly, to track the optimum TBT and BL profiles using PID and GMC controllers with and without the presence of constraints; thirdly, to examine how both types of controllers handle the disturbances which occur in the plant. The results are promising and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system.

No description available.

No description available.

Power converters are electronic circuits for conversion, control and regulation of electric power for various applications, such as from tablet computers in milliwatts to electric power systems at megawatts range. There are three basic types of power converters: buck (output voltage less than the input voltage), boost (output voltage higher than the input voltage) and buck-boost converters. The reliability of the power converters has become an essential focus of industrial applications. This research presents modeling and control of DC/DC boost converter using several control methods, such as Proportional-Integral (PI), Linear Quadratic Regulator (LQR) control, and nonlinear control concepts. Based on standard circuit laws, a mathematical model of the boost converter is derived which is expressed as a bilinear system. First a small signal model of the converter is derived to analyze the small deviations around the steady-state operating point which is used to develop closed loop control using the PI and the LQR methods. Simulation results show that the performance of the converter is good for operation around the operating state, however is unacceptable if there are large variations in the load or the reference input. To improve the performance of the closed loop system, the nonlinear control concept is used which shows excellent closed loop performance under large variations of load or setpoint. Comparative simulation results are presented for closed loop performance under various types of disturbances including random variations in load. / Electrical and Computer Engineering

Model predictive control (MPC) has gained great acceptance in the industry since it was developed and first applied about 25 years ago [1]. It has established its place mainly in the advanced control community. Traditionally, MPC configurations are developed and commissioned by control experts. MPC implementations have usually been only worthwhile to apply on processes that promise large profit increase in return for the large cost of implementation. Thus the scale of MPC applications in terms of number of inputs and outputs has usually been large. This is the main reason why MPC has not made its way into low-level loop control. In recent years, academia and control system vendors have made efforts to broaden the range of MPC applications. Single loop MPC and multiple PID strategy replacements for processes that are difficult to control with PID controllers have become available and easier to implement. Such processes include deadtime-dominant processes, override strategies, decoupling networks, and more. MPC controllers generally have more "knobs" that can be adjusted to gain optimum performance than PID. To solve this problem, general PID replacement MPC controllers have been suggested. Such controllers include forward modeling controller (FMC)[2], constraint LQ control[3] and adaptive controllers like ADCO[4]. These controllers are meant to combine the benefits of predictive control performance and the convenience of only few (more or less intuitive) tuning parameters. However, up until today, MPC controllers generally have only succeeded in industrial environments where PID control was performing poorly or was too difficult to implement or maintain. Many papers and field reports [5] from control experts show that PID control still performs better for a significant number of processes. This is on top of the fact that PID controllers are cheaper and faster to deploy than MPC controllers. Consequently, MPC controllers have actually replaced only a small fraction of PID controllers. This research shows that deficiencies in the feedback control capabilities of MPC controllers are one reason for the performance gap between PID and MPC. By adopting knowledge from PID and other proven feedback control algorithms, such as statistical process control (SPC) and Fuzzy logic, this research aims to find algorithms that demonstrate better feedback control performance than methods commonly used today in model predictive controllers. Initially, the research focused on single input single output (SISO) processes. It is important to ensure that the new feedback control strategy is implemented in a way that does not degrade the control functionality that makes MPC superior to PID in multiple input multiple output (MIMO) processes. / text