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The Use of Positioning Systems for Look-Ahead Control in Vehicles / Användning av positioneringssystem för prediktiv reglering av fordonGustafsson, Niklas January 2006 (has links)
<p>The use of positioning systems in a vehicle is a research intensive field. In the first part of this thesis an increase in new applications is disclosed through a mapping of patent documents on how positioning systems can support adaptive cruise control, gear changing systems and engine control. Many ideas are presented and explained and the ideas are valued. Furthermore, a new method for selective catalytic reduction (SCR) control using a positioning system is introduced. It is concluded that look-ahead control, where the vehicle position in relation to the upcoming road section is utilized could give better fuel efficiency, lower emissions and less brake, transmission and engine wear.</p><p>In the second part of this thesis a real time test platform for predictive speed control algorithms has been developed and tested in a real truck. Previously such algorithms could</p><p>only be simulated. In this thesis an algorithm which utilizes model predictive control (MPC) and dynamic programming (DP) been implemented and evaluated. An initial comparative fuel test shows a reduction in fuel consumption when the MPC algorithm is used.</p>
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Topics in nonlinear control. : Output Feedback Stabilization and Control of Positive SystemsImsland, Lars January 2002 (has links)
<p>The contributions of this thesis are in the area of control of systems with nonlinear dynamics. The thesis is divided into three parts. The two first parts are similar in the sense that they both consider output feedback of rather general classes of nonlinear systems, and both approaches are based on mathematical programming (although in quite different ways). The third part contains a state feedback approach for a specific system class, and is more application oriented.</p><p>The first part treats control of systems described by nonlinear difference equations, possibly with uncertain terms. The system dynamics are represented by piecewise affine difference inclusions, and for this system class, piecewise affine controller structures are suggested. Controller synthesis inequalities for such controller structures are given in the form of Bilinear Matrix Inequalities (BMIs). A solver for the BMIs is developed. The main contribution is to the output feedback case, where an observer-based controller structure is proposed. The theory is exemplified through two examples.</p><p>In the second part the output feedback problem is examined in the setting of Nonlinear Model Predictive Control (NMPC). The state space formulation of NMPC is inherently a state feedback approach, since the state is needed as initial condition for the prediction in the controller. Consequently, for output feedback it is natural to use observers to obtain estimates of the state. A high gain observer is applied for this purpose. It is shown that for several existing NMPC schemes, the state feedback stability properties ``semiglobally'' hold in the output feedback case. The theory is illuminated with a simple example.</p><p>Finally, a state feedback controller for a class of positive systems is proposed. Convergence of the state to a certain subset of the first orthant, corresponding to a constant ``total mass'' (interpreting states as masses) is obtained. Conditions are given under which convergence to this set implies asymptotic stability of an equilibrium. Simple examples illustrate some properties of the controller. Furthermore, the control strategy is applied to the stabilization of a gas-lifted oil well, and simulations on a rigorous multi-phase dynamic simulator of such a well demonstrate the controller performance.</p>
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Advanced control of the twin screw extruderIqbal, Mohammad Hasan 11 1900 (has links)
This research deals with the modeling and control of a plasticating twin screw extruder (TSE) that will be used to obtain consistent product quality. The TSE is a widely used process technology for compounding raw polymers. Compounding creates a polymer with improved properties that satisfy the demand of modern plastic applications. Modeling and control of a TSE is challenging because of its high nonlinearity, inherent time delay, and multiple interactive dynamic behavior. A complete methodology is proposed in this thesis to design an advanced control scheme for a TSE. This methodology was used to develop a model predictive control scheme for a laboratory scale plasticating TSE and to implement the control scheme in real-time. The TSE has a processing length of 925 mm and a length to screw diameter ratio (L/D) of 37. High density polyethylenes with different melt indices were used as processing materials.
Manipulated variables and disturbance variables were selected based on knowledge of the process. Controlled variables were selected using a selection method that includes a steady state correlation between process output variables and product quality variables, and dynamic considerations. Two process output variables, melt temperature (Tm) at the die and melt pressure (Pm) at the die, were selected as controlled variables.
A new modeling approach was proposed to develop grey box models based on excitation in the extruder screw speed (N), one of the manipulated variables. The extruder was excited using a predesigned random binary sequence (RBS) type excitation in N and nonlinear models relating Tm and Pm to N were developed using this approach. System identification techniques were used to obtain model parameters. The obtained models have an autoregressive moving average with exogenous (ARMAX) input structure and the models explain the physics of the extrusion process successfully.
The TSE was also excited using a predesigned RBS in the feed rate (F) as a manipulated variable. Models relating Tm and Pm to F were developed using a classical system identification technique; both models have ARMAX structures. The model between Pm and F was found to give excellent prediction for data obtained from a stair type excitation, indicating that the obtained models provide a good representation of the dynamics of the twin screw extruder.
Analysis of the TSE open loop process indicated two manipulated variables, N and F, and two controlled variables, Tm and Pm. Thus, a model predictive controller (MPC) was designed using the developed models for this 2X2 system and implemented in real-time. The performance of the MPC was studied by checking its set-point tracking ability. The robustness of the MPC was also examined by imposing external disturbances.
Finally, a multimodel operating regime was used to model Tm and N. The operating regime was divided based on the screw speed, N. Local models were developed using system identification techniques. The global model was developed by combining local models using fuzzy logic methodology. Simulated results showed excellent response of Tm for a wide operating range. A similar approach was used to design a global nonlinear proportional-integral controller (n-PI) and a nonlinear MPC (n-MPC). Both the controllers showed good set-points tracking ability over the operating range. The multiple model-based MPC showed smooth transitions from one operating regime to another operating regime. / Process Control
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Topics in nonlinear control. : Output Feedback Stabilization and Control of Positive SystemsImsland, Lars January 2002 (has links)
The contributions of this thesis are in the area of control of systems with nonlinear dynamics. The thesis is divided into three parts. The two first parts are similar in the sense that they both consider output feedback of rather general classes of nonlinear systems, and both approaches are based on mathematical programming (although in quite different ways). The third part contains a state feedback approach for a specific system class, and is more application oriented. The first part treats control of systems described by nonlinear difference equations, possibly with uncertain terms. The system dynamics are represented by piecewise affine difference inclusions, and for this system class, piecewise affine controller structures are suggested. Controller synthesis inequalities for such controller structures are given in the form of Bilinear Matrix Inequalities (BMIs). A solver for the BMIs is developed. The main contribution is to the output feedback case, where an observer-based controller structure is proposed. The theory is exemplified through two examples. In the second part the output feedback problem is examined in the setting of Nonlinear Model Predictive Control (NMPC). The state space formulation of NMPC is inherently a state feedback approach, since the state is needed as initial condition for the prediction in the controller. Consequently, for output feedback it is natural to use observers to obtain estimates of the state. A high gain observer is applied for this purpose. It is shown that for several existing NMPC schemes, the state feedback stability properties ``semiglobally'' hold in the output feedback case. The theory is illuminated with a simple example. Finally, a state feedback controller for a class of positive systems is proposed. Convergence of the state to a certain subset of the first orthant, corresponding to a constant ``total mass'' (interpreting states as masses) is obtained. Conditions are given under which convergence to this set implies asymptotic stability of an equilibrium. Simple examples illustrate some properties of the controller. Furthermore, the control strategy is applied to the stabilization of a gas-lifted oil well, and simulations on a rigorous multi-phase dynamic simulator of such a well demonstrate the controller performance.
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Non-linear model predictive control for autonomous vehiclesAbbas, Muhammad Awais 01 November 2011 (has links)
With the advent of faster computer processors and better optimization algorithms,
Model Predictive Control (MPC) systems are more readily used for real-time applications.
This research focuses on the application of MPC to trajectory generation of autonomous
vehicles in an online manner. The operating environment is assumed to be unknown with
various different types of obstacles. Models of simplified 2-D dynamics of the vehicle are
developed, discretized and validated against a nonlinear CarSim vehicle model. The developed
model is then used to predict future states of the vehicle. The relationship of the
weight transfer to the tire slip angle is investigated. The optimal trajectory tracking problem
is formulated in terms of a cost function minimization with constraints. Initially, a gradient
descent method is used to minimize the cost function. A MATLAB based MPC controller
is developed and interfaced with CarSim in order to test the controller on a vehicle operating
in a realistic environment. The effects of varying MPC look-ahead horizon lengths on
the computation time, simulation cost and the tracking performance are also investigated.
Simulation results show that the new MPC controller provides satisfactory online obstacle
avoidance and tracking performance. Also, a trajectory tracking criterion with goal point information
is found to be superior to traditional trajectory tracking methods since they avoid
causing the vehicle to retreat once a large obstacle is detected on the desired path. It is further
demonstrated that at a controller frequency of 20Hz, the implementation is real-time
implementable only at shorter horizon lengths. / UOIT
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Networked Control System Design and Parameter EstimationYu, Bo 29 September 2008
Networked control systems (NCSs) are a kind of distributed control systems in which the data between control components are exchanged via communication networks. Because of the attractive advantages of NCSs such as reduced system wiring, low weight, and ease of system diagnosis and maintenance, the research on NCSs has received much attention in recent years. The first part (Chapter 2 - Chapter 4) of the thesis is devoted to designing new controllers for NCSs by incorporating the network-induced delays. The thesis also conducts research on filtering of multirate systems and identification of Hammerstein systems in the second part (Chapter 5 - Chapter 6).<br /><br />
Network-induced delays exist in both sensor-to-controller (S-C) and controller-to-actuator (C-A) links. A novel two-mode-dependent control scheme is proposed, in which the to-be-designed controller depends on both S-C and C-A delays. The resulting closed-loop system is a special jump linear system. Then, the conditions for stochastic stability are obtained in terms of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be efficiently solved by a sequential LMI optimization algorithm. Further, the control synthesis problem for the NCSs is considered. The definitions of <em>H<sub>2</sub></em> and <em>H<sub>∞</sub></em> norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed <em>H<sub>2</sub>/H<sub>∞</sub></em> control problem is solved under the framework of LMIs. <br /><br />
To compensate for both S-C and C-A delays modeled by Markov chains, the generalized predictive control method is modified to choose certain predicted future control signal as the current control effort on the actuator node, whenever the control signal is delayed. Further, stability criteria in terms of LMIs are provided to check the system stability. The proposed method is also tested on an experimental hydraulic position control system. <br /><br />
Multirate systems exist in many practical applications where different sampling rates co-exist in the same system. The <em>l<sub>2</sub>-l<sub>∞</sub></em> filtering problem for multirate systems is considered in the thesis. By using the lifting technique, the system is first transformed to a linear time-invariant one, and then the filter design is formulated as an optimization problem which can be solved by using LMI techniques. <br /><br />
Hammerstein model consists of a static nonlinear block followed in series by a linear dynamic system, which can find many applications in different areas. New switching sequences to handle the two-segment nonlinearities are proposed in this thesis. This leads to less parameters to be estimated and thus reduces the computational cost. Further, a stochastic gradient algorithm based on the idea of replacing the unmeasurable terms with their estimates is developed to identify the Hammerstein model with two-segment nonlinearities. <br /><br />
Finally, several open problems are listed as the future research directions.
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Model Predictive Control for Active Magnetic BearingsLundh, Joachim January 2012 (has links)
This thesis discuss the possibility to position control a rotor levitated with active magnetic bearings. The controller type considered is model predictive control which is an online strategy that solves an optimization problem in every sample, making the model predictive controller computation-intense. Since the sampling time must be short to capture the dynamics of the rotor, very little time is left for the controller to perform the optimization. Different quadratic programming strategies are investigated to see if the problem can be solved in realtime. Additionally, the impact of the choices of prediction horizon, control horizon and terminal cost is discussed. Simulations showing the characteristics of these choises are made and the result is shown. / Det här examensarbetet diskuterar möjligheten att positionsreglera en rotor som leviteras på aktiva magnetlager. Reglerstrategin som används är modellbaserad prediktionsreglering vilket är en online-metod där ett optimeringsproblem löses i varje sampel. Detta gör att regulatorn blir mycket beräkningskrävande. Samplingstiden för systemet är mycket kort för att fånga dynamiken hos rotorn. Det betyder att regulatorn inte ges mycket tid att lösa optimeringsproblemet. Olika metoder för att lösa QP-problem betraktas för att se om det är möjligt att köra regulatorn i realtid. Dessutom diskuteras hur valet av prediktionshorisont, reglerhorisont och straff på sluttillståndet påverkar regleringen. Simuleringar som visar karakteristiken av dessa val har utförts.
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A practical approach to detection of plant model mismatch for MPCCarlsson, Rickard January 2010 (has links)
The number of MPC installations in industry is growing as a reaction to demands of increased efficiency. An MPC controller uses an internal plant model to run real-time predictive optimization of future inputs. If a discrepancy between the internal plant model and the plant exists, control performance will be affected. As time from commissioning increases the model accuracy tends to deteriorate. This is natural as the plant changes over time. It is important to detect these changes and re-identify the plant model to maintain control performance over time. A method for identifying Model Plant Mismatch for MPC applications is developed. Focus has been on developing a method that is simple to implement but still robust. The method is able to run in parallel with the process in real time. The efficiency of the method is demonstrated via representative simulation examples.An extension to detection of nonlinear mismatch is also considered, which is important since linear plant models often are used within a small operating range. Since most processes are nonlinear this discrepancy is inevitable and should be detected. / Ökade krav på effektivitet gör att industrin söker efter mer avancerad processtyrning. MPC har växt fram som en kandidat. En MPC regulator änvänder en modell av systemet för att samtidigt som systemet körs utföra en optimering av framtida styrsignaler. Om modellen innehåller felaktigheter kan reglerprestandan påverkas. En modell försämras normalt då tiden från idrifttagning växer eftersom systemet förändras med tiden. Det är av största vikt att upptäcka dessa förändringar och sedan uppdatera modellen för att reglerprestandan inte ska påverkas. Avsikten är att utveckla en metod för att upptäcka modellfel med fokus på att den ska vara enkel att implementera. Det ska även vara möjligt att använda metoden parallellt med en process. För att utvärdera metoden så körs den på ett antal representativa simuleringsexempel. Det har även varit en avsikt att utveckla en metod för detektion av ickelinjära modellfel. Motivet till det är att linjära modeller används för att beskriva ickelinjära processer och då är modellfel naturliga.
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Missilstyrning med Model Predictive Control / Missile Control using Model Predictive ControlRosdal, David January 2005 (has links)
This thesis has been conducted at Saab Bofors Dynamics AB. The purpose was to investigate if a non-linear missile model could be stabilized when the optimal control signal is computed considering constraints on the control input. This is particularly interesting because the missile is controlled with rudders that have physical bounds. This strategy is called Model Predictive Control. Simulations are conducted to compare this strategy with others; firstly simulations with step responses and secondly simulations when the missile is supposed to hit a moving target. The latter is performed to show that the missile can be stabilized in its whole area of operation. The simulations show that the controller indeed can stabilize the missile for the given scenarios. However, this control strategy does not show any obvious improvements in comparison with alternative ones.
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The Use of Positioning Systems for Look-Ahead Control in Vehicles / Användning av positioneringssystem för prediktiv reglering av fordonGustafsson, Niklas January 2006 (has links)
The use of positioning systems in a vehicle is a research intensive field. In the first part of this thesis an increase in new applications is disclosed through a mapping of patent documents on how positioning systems can support adaptive cruise control, gear changing systems and engine control. Many ideas are presented and explained and the ideas are valued. Furthermore, a new method for selective catalytic reduction (SCR) control using a positioning system is introduced. It is concluded that look-ahead control, where the vehicle position in relation to the upcoming road section is utilized could give better fuel efficiency, lower emissions and less brake, transmission and engine wear. In the second part of this thesis a real time test platform for predictive speed control algorithms has been developed and tested in a real truck. Previously such algorithms could only be simulated. In this thesis an algorithm which utilizes model predictive control (MPC) and dynamic programming (DP) been implemented and evaluated. An initial comparative fuel test shows a reduction in fuel consumption when the MPC algorithm is used.
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