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.

One of the key challenges in the Oil & Gas industry is to best manage reservoirs under different conditions, constrained by production rates based on various economic scenarios, in order to meet energy demands and maximize profit. To address the energy demand challenges, a transformation in the paradigm of the utilization of "real-time" data has to be brought to bear, as one changes from a static decision making to a dynamical and data-driven management of production in conjunction with real-time risk assessment. The use of modern methods of computational modeling and simulation may be the only means to account for the two major tasks involved in this paradigm shift: (1) large-scale computations; and (2) efficient utilization of the deluge of data streams. Recently, history matching and optimization were brought together in the oil industry into an integrated and more structured approach called optimal closed-loop reservoir management. Closed-loop control algorithms have already been applied extensively in other engineering fields, including aerospace, mechanical, electrical and chemical engineering. However, their applications to porous media flow, such as - in the current practices and improvements in oil and gas recovery, in aquifer management, in bio-landfill optimization, and in CO2 sequestration have been minimal due to the large-scale nature of existing problems that generate complex models for controller design and real-time implementation. Their applicability to a realistic field is also an open topic because of the large-scale nature of existing problems that generate complex models for controller design and real-time implementation, hindering its applicability. Basically, three sources of high-dimensionality can be identified from the underlying reservoir models: size of parameter space, size of state space, and the number of scenarios or realizations necessary to account for uncertainty. In this paper we will address type problem of high dimensionality by focusing on the mitigation of the size of the state-space models by means of model-order reduction techniques in a systems framework. We will show how one can obtain accurate reduced order models which are amenable to fast implementations in the closed-loop framework .The research will focus on System Identification (System-ID) (Jansen, 2009) and Model Predictive Control (MPC) (Gildin, 2008) to serve this purpose. A mathematical treatment of System-ID and MPC as applied to reservoir simulation will be presented. Linear MPC would be studied on two specific reservoir models after generating low-order reservoir models using System-ID methods. All the comparisons are provided from a set of realistic simulations using the commercial reservoir simulator called Eclipse. With the improvements in oil recovery and reductions in water production effectively for both the cases that were considered, we could reinforce our stance in proposing the implementation of MPC and System-ID towards the ultimate goal of "real-time" production optimization.

Inferential control of boric acid production system using the reaction of colemanite with sulfuric acid in four continuously stirred tank reactors (CSTR) connected in series is aimed. In this control scheme, pH of the product is measured on-line instead of boric acid concentration for control purposes. An empirical correlation between pH and boric acid concentration is developed using the collected data in a batch reacting system in laboratory-scale and this correlation is utilized in the control system for estimator design. The transfer function model of the 4-CSTR system previously obtained is used in the MPC controller design. In the experiments done previously for the modelling of 4-CSTR system, it was observed that the reaction goes complete within the first reactor. Therefore, the control is based on the measurements of pH of the second reactor by manipulating the flow rate of sulfuric acid given to the first reactor, while the flow rate of colemanite fed to the system is considered as disturbance. The designed controller&rsquo / s performance is tested for set point tracking, disturbance rejection and robustness issues using a simulation program. It is found that, the designed controller is performing satisfactorily, using the inferential control strategy for this complex reacting system.

Quality of Service (QoS) in real-time media applications can be defined as the ability to guarantee the delivery of packets from source to destination over best-effort networks within some constraints. These constraints defined as the QoS metrics are end-to-end packet delay, delay jitter, throughtput, and packet losses. Transporting real-time media applications over best-effort networks, e.g. the Internet, is an area of current research. Both the Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP) have failed to provide the desired QoS. This research aims at developing application-level end-to-end QoS controls to improve the user-perceived quality of real-time media applications over best-effort networks, such as, the public Internet. In this research an end-to-end packet based approach is developed. The end-to- end packet based approach consists of source buffer, network simulator ns-2, destina- tion buffer, and controller. Unconstrained model predictive control (MPC) methods are implemented by the controller at the application layer. The end-to-end packet based approach uses end-to-end network measurements and predictions as feedback signals. Effectiveness of the developed control methods are examined using Matlab and ns-2. The results demonstrate that sender-based control schemes utilizing UDP at transport layer are effective in providing QoS for real-time media applications transported over best-effort networks. Significant improvements in providing QoS are visible by the reduction of packet losses and the elimination of disruptions during the playback of real-time media. This is accompanied by either a decrease or increase in the playback start-time.

<p>Arbetet har utförts på Saab Bofors Dynamics i Karlskoga och dess syfte var att undersöka om det är möjligt att använda modellbaserad prediktionsreglering, MPC, vid slutfasstyrning av en viss typ av robot. Som referensram används linjärkvadratisk reglering, LQ, eftersom denna reglermetod har undersökts tidigare och visat sig fungera bra vid slutfasstyrning, dock för en annan typ av robot. Anledningen till att man vill undersöka om det är möjligt att använda MPC är att styrlagen enkelt tar hand om begränsningar på systemet på ett direkt och intuitivt sätt.</p><p>Styrlagarnas uppgift är att styra en robot i dess slutfas då det finns krav och önskemål på roboten som bör vara uppfyllda. Till exempel finns det begränsningar på styrsignalen samt önskemål om att träff ska ske i en viss träffpunkt och även med en viss träffvinkel. För att utvärdera resultaten undersöks och jämförs de två styrlagarnas prestanda och robusthet.</p><p>För att kunna utvärdera styrlagarnas egenskaper och jämföra dem implementeras de båda i en befintlig detaljerad simuleringsmiljö, som har utvecklats på Saab Bofors Dynamics i Karlskoga.</p><p>De prestanda och robusthetstester som har utförts uppvisar små skillnader på de två styrlagarna och slutsatsen blir därmed att det är möjligt att använda modellbaserad prediktionsreglering vid slutfasstyrning av en viss typ av robot eftersom det sedan tidigare är känt att linjärkvadratisk reglering är en bra styrlag att använda. För att se vilken av de två styrlagarna som är bäst vid slutfasstyrning av en viss typ av robot behöver det göras vissa ändringar och mer detaljerade undersökningar utföras.</p>

In this thesis, I study the computational advantages of the allocentric represen- tation as compared to the egocentric representation for autonomous local navigation. Whereas in the allocentric framework, all variables of interest are represented with respect to a coordinate frame attached to an object in the scene, in the egocentric one, they are always represented with respect to the robot frame at each time step. In contrast with well-known results in the Simultaneous Localization and Mapping literature, I show that the amounts of nonlinearity of these two representations, where poses are elements of Lie-group manifolds, do not affect the accuracy of Gaussian- based filtering methods for perception at both the feature level and the object level. Furthermore, although these two representations are equivalent at the object level, the allocentric filtering framework is better than the egocentric one at the feature level due to its advantages in the marginalization process. Moreover, I show that the object- centric perspective, inspired by the allocentric representation, enables novel linear- time filtering algorithms, which significantly outperform state-of-the-art feature-based filtering methods with a small trade-off in accuracy due to a low-rank approximation. Finally, I show that the allocentric representation is also better than the egocentric representation in Model Predictive Control for local trajectory planning and obstacle avoidance tasks.

Ensuring the reliable operation of engineering systems has long been a subject of great practical and academic interest. This interest is clearly demonstrated by the preponderance of literature in the area of Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC), spanning the past three decades. However, increasingly stringent performance and safety requirements have led to engineering systems with progressively increasing complexity. This complexity has rendered many traditional FDD and FTC methods exceedingly cumbersome, often to the point of infeasibility. This thesis aims to enable FDD and FTC for complex engineering systems of interacting dynamic subsystems. For such systems, generic FDD/FTC methods have remained elusive. Effects caused by nonlinearities, interactions between subsystems and varying usage patterns complicate FDD and FTC. The goal of this thesis is to develop methods for FDD and FTC that will allow decoupling of anomalies occurred inside the monitored system from those occurred in the systems affecting the monitored system, as well as enabling performance recovery of the monitored system. In pursuit of these goals, FDD and FTC methods are explored that can account for operating regime-dependent effects in monitoring, diagnosis, prognosis and performance recovery for two classes of machines: those that operate in modes that can change only at distinct times (which often occur in manufacturing opera- tions such as drilling, milling, turning) and for those that operate in regimes that are continuously varying (such as automotive systems or electric motors). For machines that operate in modes that can change only at distinct times, a degradation model is postulated which describes how the system degrades over time for each operating regime. Using the framework of Hidden Markov Models (HMMs), modeling and identification tools are developed that enable identification a HMM of degradation for each machine operation. In the sequel, monitoring and prognosis methods that naturally follow from the framework of HMMs are also presented. The modeling and monitoring methodology is then applied to a real-world semiconductor manufacturing process using data provided by a major manufacturer. For machines that operate in regimes that are continuously varying, a behavioral model is postulated that describes the input-output dynamics of the nor- mal system in different operating regimes. Monitoring methods are presented that have the capability to account for operating regime-dependent modeling accuracies and isolate faults that have not been anticipated and for which no fault models are available. By conducting fault detection in a regime-dependent fashion, changes in modeling errors that are due to operating regime changes can be successfully distinguished from changes that are due to truly faulty operation caused by changes in the system dynamics. Enabled by this, unanticipated faults can be isolated through proliferation of the fault detection through the various subsystems of the anoma- lous system. The FDD methodology is applied to detect and diagnose faults for a multiple-input multiple-output Exhaust Gas Recirculation system in a diesel engine. Finally, methods to facilitate the recovery of normal system behavior are detailed. Using the same local model structure that was pursued for behavioral models, it is envisioned that the nominal controller will be reconfigured to attempt to recover nominal behavior as much as possible. To enable this reconfiguration, methods for automated design of closed-loop controllers for the local modeling structure are presented. Using a model-predictive approach with rigorous stability considerations, it is shown that the controllers can track a reference trajectory. Such a trajectory could be generated by any model that satisfies the control objectives, for normal or faulty systems. The controllers are then demonstrated on a benchmark nonlinear system that is nonlinear in the control. / text