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

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>H₂</em> and <em>H_∞</em> norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed <em>H₂/H_&infin;</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>l₂-l_&infin;</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.

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.

Modern power networks are being driven ever closer to both their physical and operational limits. As a result, control systems are being increasingly relied on to assure satisfactory system performance. Power system stabilizers (PSSs) are one example of such controllers. Their purpose is to increase system damping and they are typically designed using a model of the network that is valid during nominal operating conditions. The limitation of this design approach is that during off-nominal operating conditions, such as those triggered by daily load fluctuations, performance of the controller can degrade. The research presented in this report attempts to evaluate the possibility of employing an adaptive PSS as a means of avoiding the performance degradation precipitated by off-nominal operation. Conceptually, an adaptive PSS would be capable of identifying changes in the network and then adjusting its parameters to ensure suitable damping of the identified network. This work begins with a detailed look at the identification algorithm employed followed by a similarly detailed examination of the control algorithm that was used. The results of these two investigations are then combined to allow for a preliminary assessment of the performance that could be expected from an adaptive PSS. The results of this research suggest that an adaptive PSS is a possibility but further work is needed to confirm this finding. Testing using more complex network models must be carried out, details pertaining to control parameter tuning must be resolved and closed-loop time domain simulations using the adaptive PSS design remain to be performed. / May 2007

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.

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.

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.

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>H₂</em> and <em>H_∞</em> norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed <em>H₂/H_&infin;</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>l₂-l_&infin;</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.

This thesis develops approximate dynamic programming (ADP) strategies suitable for process control problems aimed at overcoming the limitations of MPC, which are the potentially exorbitant on-line computational requirement and the inability to consider the future interplay between uncertainty and estimation in the optimal control calculation. The suggested approach solves the DP only for the state points visited by closed-loop simulations with judiciously chosen control policies. The approach helps us combat a well-known problem of the traditional DP called 'curse-of-dimensionality,' while it allows the user to derive an improved control policy from the initial ones. The critical issue of the suggested method is a proper choice and design of function approximator. A local averager with a penalty term is proposed to guarantee a stably learned control policy as well as acceptable on-line performance. The thesis also demonstrates versatility of the proposed ADP strategy with difficult process control problems. First, a stochastic adaptive control problem is presented. In this application an ADP-based control policy shows an "active" probing property to reduce uncertainties, leading to a better control performance. The second example is a dual-mode controller, which is a supervisory scheme that actively prevents the progression of abnormal situations under a local controller at their onset. Finally, two ADP strategies for controlling nonlinear processes based on input-output data are suggested. They are model-based and model-free approaches, and have the advantage of conveniently incorporating the knowledge of identification data distribution into the control calculation with performance improvement.

This thesis proposes an adaptive control approach to regulate the hydrogen feed of a fuel cell. The goal of the controller is to maintain the so-called hydrogen excess ratio, defined as the ratio between the hydrogen fed to the cell stake and those consumed in the stake, at a desired level when the fuel cell is under load variation. Maintaining the hydrogen excess ratio at an appropriate level would avoid hydrogen starvation, which is crucial for slowing degeneration of the fuel cell membranes and prolonging the life of the cell stake. The control approach we propose is based on the receding horizon linear quadratic optimal control algorithm with an on-line turning scheme which updates the plant model according to real-time measurement. To ease the computational complexity and make real-time turning realizable, we adopt a simple autoregressive with external disturbance (ARX) model to approximate the complicate chemical/electrical process of the fuel cell. The proposed adaptive control approach is implemented on an experimental platform. The experimental results show that the proposed control works with reasonably good performance.