This dissertation aims to reduce urban traffic congestion with street traffic signal control. The traffic signal controllers in the U.S. follow the National Electrical Manufacturing Association Standards (NEMA Standards). In a NEMA controller, the control parameters for a coordinated control are cycle, green splits, and offset. This dissertation proposed a virtual phase-link concept and developed a macroscopic model to describe the dynamics of a traffic network. The coordinated optimal splits control problem was solved using model predictive control. The outputs of the solution are the green splits that can be used in NEMA controllers. I compared the proposed method with a state-of-the-practice signal timing software under coordinated-actuated control settings. It was found that the proposed method significantly outperformed the benchmarking method. I compared the proposed NEMA-based virtual phase-link model and a Max Pressure controller model using Vissim. It was found that the virtual phase-link method outperformed two control strategies and performed close, but not as good as, the Max Pressure control strategy. The disadvantage of the virtual phase-link method stemmed from the waste of green time during a fixed control cycle length and the delay which comes from the slowing down of platoon during a road link to allow vehicles to switch lanes. Compared to the Max Pressure control strategy, the virtual phase-link method can be implemented by any traffic controller that follows the NEMA standards. The real-time requirement of the virtual phase-link method is not as strict as the Max Pressure control strategy. I introduced the offsets optimization into the virtual phase-link method. I modeled the traffic arrival pattern based on the optimization results from the virtual phase-link control method. I then derived a phase delay function based on the traffic arrival pattern. The phase delay function is a function of the offset between two consecutive intersections. This phase delay function was then used for offsets optimization along an arterial. I tested the offsets optimization method against a base case using microscopic simulations. It was found that the proposed offset optimization method can significantly reduce vehicle delays. / Doctor of Philosophy / The goal of this work is to reduce traffic congestion by providing optimized signal timing plans to controllers. Knowing that the controllers in the U.S. follow National Electrical Manufacturing Association (NEMA) Standards, I proposed a virtual phase-link concept and modeled the road traffic network under NEMA controllers’ control as a set of virtual phase-links. Each virtual phase-link corresponds to a NEMA phase at an intersection. I then proposed a NEMA-based virtual phase-link street traffic model. The control variables are the green time allocated to each phase. I compared the proposed NEMA-based virtual phase-link control method with a state-of-the-practice signal timing software using simulation experiments. It was found that the proposed control methods significantly outperformed the signal timing software. I implemented a state-of-the-art adaptive control strategy, Max Pressure control. I compared the proposed NEMA-based virtual phase-link control method with the Max Pressure control strategy. I found that the virtual phase-link control method performed close, but not as good as, the Max Pressure control strategy. The disadvantage of the virtual phase-link method stemmed from the waste of green time during a fixed control cycle length and the delay which comes from the slowing down of platoon during a road link to allow vehicles to switch lanes. The Max Pressure control needs non-conventional controllers which can potentially switch to any phase at any time. Compared to the Max Pressure control strategy, the virtual phase-link method can be implemented by any traffic controller that follows the NEMA standards. The real-time requirement of the virtual phase-link method is not as strict as the Max Pressure control strategy. I then augmented the virtual phase-link method with optimal offsets control. The offsets are the time differences of the coordinated phases comparing to a reference point in a control cycle. I derived a phase delay function and used that function to optimize the offsets by minimizing the associated delays. The simulation experiments showed that the proposed offsets optimization method could reduce the delay along the coordinated path significantly.

Traditional generators have been decommissioned or replaced by renewable energy generation due to utility long-standing goals. However, instead of flattening the entire plant, the rotating mass of generator can be utilized as a storage unit (inertia resource) to mitigate the frequency swings during transient caused by the renewables. The goal of this work is to design a control strategy utilizing the decommissioned generator interfaced with power grid via a back-to-back converter to provide inertia support. This is referred to as decoupled synchronous machine system (DSMS). On top of that, the grid-side converter is capable of providing reactive power as an auxiliary voltage controller. However, in a practical setting, for power utilities, the detailed state equations of such device as well as the complicated nonlinear power system are usually unobtainable making the controller design a challenging problem. Therefore, a model free, purely data-driven strategy for the nonlinear controller design using Koopman operator-based framework is proposed. Besides, the time delay embedding technique is adopted together with Koopman operator theory for the nonlinear system identification. Koopman operator provides a linear representation of the system and thereby the classical linear control algorithms can be applied. In this work, model predictive control is adopted to cope with the constraints of the control signals. The effectiveness and robustness of the proposed system are demonstrated in Kundur two-area system and IEEE 39-bus system. / Doctor of Philosophy / Power system is facing an energy transformation from the traditional fuel to sustainable renewable such as solar, wind and so on. Unlike the traditional fuel energized generators, the renewable has very little inertia to maintain frequency stability. Therefore, this work proposes a new system referred to as decoupled synchronous machine system (DSMS) to support the grid frequency. DSMS consists of the rotating mass of generator and a back-to-back converter which can be utilized as an inertia resource to mitigate the frequency oscillations. In addition, the grid-side converter can provide reactive power to improve voltage performance during faults. This work aims to design a control strategy utilizing DSMS to support grid frequency and voltage. However, an explicit mathematical model of such device is unobtainable in a practical setting making data-driven control the only option. A data-driven technique which is Koopman operator-based framework together with time delay embedding algorithm is proposed to obtain a linear representation of the system. The effectiveness and robustness of the proposed system are demonstrated in Kundur two-area system and IEEE 39-bus system.

We present a motion control framework which allows a group of robots to work together to decide upon their motions by minimizing a collective cost without any central computing component or any one agent performing a large portion of the computation. When developing distributed control algorithms, care must be taken to respect the limited computational capacity of each agent as well as respect the information and communication constraints of the network. To address these issues, we develop a distributed, behavior-based model predictive control (MPC) framework which alleviates the computational difficulties present in many distributed MPC frameworks, while respecting the communication and information constraints of the network. In developing the multi-agent control framework, we make three contributions. First, we develop a distributed optimization technique which respects the dynamic communication restraints of the network, converges to a collective minimum of the cost, and has transients suitable for robot motion control. Second, we develop a behavior-based MPC framework to control the motion of a single-agent and apply the framework to robot navigation. The third contribution is to combine the concepts of distributed optimization and behavior-based MPC to develop the mentioned multi-agent behavior-based MPC algorithm suitable for multi-robot motion control.

Networked control systems (NCSs) present many advantages such as easy installation and maintenance, flexible layouts and structures of components, and efficient allocation and distribution of resources. Consequently, they find potential applications in a variety of emerging industrial systems including multi-agent systems, power grids, tele-operations and cyber-physical systems. The study of NCSs with nonlinear dynamics (i.e., nonlinear NCSs) is a very significant yet challenging topic, and it not only widens application areas of NCSs in practice, but also extends the theoretical framework of NCSs with linear dynamics (i.e., linear NCSs). Numerous issues are required to be resolved towards a fully-fledged theory of industrial nonlinear NCS design. In this dissertation, three important problems of nonlinear NCSs are investigated: The robust filtering problem, the robust model predictive control (MPC) problem and the robust distributed MPC problem of large-scale nonlinear systems. In the robust filtering problem of nonlinear NCSs, the nonlinear system model is subject to uncertainties and external disturbances, and the measurements suffer from time delays governed by a Markov process. Utilizing the Lyapunov theory, the algebraic Hamilton-Jacobi inequality (HJI)-based sufficient conditions are established for designing the H_infty nonlinear filter. Moreover, the developed results are specialized for a special type of nonlinear systems, by presenting the HJI in terms of matrix inequalities. For the robust MPC problem of NCSs, three aspects are considered. Firstly, to reduce the computation and communication load, the networked MPC scheme with an efficient transmission and compensation strategy is proposed, for constrained nonlinear NCSs with disturbances and two-channel packet dropouts. A novel Lyapunov function is constructed to ensure the input-to-state practical stability (ISpS) of the closed-loop system. Secondly, to improve robustness, a networked min-max MPC scheme are developed, for constrained nonlinear NCSs subject to external disturbances, input and state constraints, and network-induced constraints. The ISpS of the resulting nonlinear NCS is established by constructing a new Lyapunov function. Finally, to deal with the issue of unavailability of system state, a robust output feedback MPC scheme is designed for constrained linear systems subject to periodical measurement losses and external disturbances. The rigorous feasibility and stability conditions are established. For the robust distributed MPC problem of large-scale nonlinear systems, three steps are taken to conduct the studies. In the first step, the issue of external disturbances is addressed. A robustness constraint is proposed to handle the external disturbances, based on which a novel robust distributed MPC algorithm is designed. The conditions for guaranteeing feasibility and stability are established, respectively. In the second step, the issue of communication delays are dealt with. By designing the waiting mechanism, a distributed MPC scheme is proposed, and the feasibility and stability conditions are established. In the third step, the robust distributed MPC problem for large-scale nonlinear systems subject to control input constraints, communication delays and external disturbances are studied. A dual-mode robust distributed MPC strategy is designed to deal with the communication delays and the external disturbances simultaneously, and the feasibility and the stability conditions are developed, accordingly. / Graduate / 0548 / 0544

Platooning is an interesting area which involve the possibility of decreasing the fuel consumption of heavy-duty vehicles. By reducing the inter-vehicle spacing in the platoon we can reduce air drag, which in turn reduces fuel consumption. Two fuel-efficient model predictive controllers for HDVs in a platoon has been formulated in this master thesis, both utilizing road grade preview information. The first controller is based on linear programming (LP) algorithms and the second on quadratic programming (QP). These two platooning controllers are compared with each other and with generic controllers from Scania. The LP controller proved to be more fuel-efficient than the QP controller, the Scania controllers are however more fuel-efficient than the LP controller.

Please read the abstract (Summary) in the section, 00front of this document / Thesis (PhD (Electronic Engineering))--University of Pretoria, 2008. / Electrical, Electronic and Computer Engineering / PhD / unrestricted

The control and optimization of chemical plants is a major area of research as it has the potential to improve both economic output and plant safety. It is often prudent to separate control and optimization tasks of varying complexities and time scales, creating a hierarchical control structure. Within this structure, it is beneficial for one control layer to be able to account for the effects of other layers. A clear example of this, and the basis of this work, is closed-loop dynamic real-time optimization (CL-DRTO), in which an economic optimization method considers both the plant behavior and the effects of an underlying model predictive controller (MPC). This technique can be expanded on to allow its use and methods to be employed in a greater diversity of applications, particularly unstable and uncertain plant environments. First, this work seeks to improve on existing robust MPC techniques, which incorporate plant uncertainty via direct multi-scenario modelling, by also including future MPC behavior through the use of the CL modelling technique of CL-DRTO. This allows the CL robust MPC to account for how future MPC executions will be affected by uncertain plant behavior. Second, Lyapunov MPC (LMPC) is a generally nonconvex technique which focuses on effective control of plants which exhibit open-loop unstable behavior. A new convex LMPC formulation is presented here which can be readily embedded into a CL-DRTO scheme. Next, uncertainty handling is incorporated directly into a CL-DRTO via a robust multi-scenario method to allow for the economic optimization to take uncertain plant behavior into account while also modelling MPC behavior under plant uncertainty. Finally, the robust CL-DRTO method is computationally expensive, so a decomposition method which separates the robust CL-DRTO into its respective scenario subproblems is developed to improve computation time, especially for large optimization problems. / Thesis / Doctor of Philosophy (PhD) / It is common for control and optimization of chemical plants to be performed in a multi-layered hierarchy. The ability to predict the behavior of other layers or the future behavior of the same layer can improve overall plant performance. This thesis presents optimization and control frameworks which use this concept to more effectively control and economically optimize chemical plants which are subject to uncertain behavior or instability. The strategy is shown, in a series of simulated case studies, to effectively control chemical plants with uncertain behavior, control and optimize unstable plant systems, and economically optimize uncertain chemical plants. One of the drawbacks of these strategies is the relatively large computation time required to solve the optimization problems. Therefore, for uncertain systems, the problem is separated into smaller pieces which are then coordinated towards a single solution. This results in reduced computation time.

Controle preditivo baseado em modelos (MPC) é uma técnica de controle amplamente utilizada na indústria de processos químicos. Por outro lado, o método de identificação em subespaços (SID) tem se mostrado uma alternativa eficiente para os métodos clássicos de identificação de sistemas. Pela combinação dos conceitos de MPC e SID, surgiu, no final da década de 90, uma nova técnica de controle, denominada controle preditivo com enfoque em subespaços (SPC). Essa técnica também é conhecida como controle preditivo orientado a dados. Ela substitui por um único passo as três etapas do projeto de um MPC: a identificação do modelo, o cálculo do observador de estados e a construção das matrizes de predição. Este trabalho tem como principal objetivo revisar estudos feitos na área de SPC, aplicar esse método em sistemas típicos da indústria química e propor novos algoritmos. São desenvolvidos três algoritmos de excitação interna para o método SPC, que permitem gerar dados persistentemente excitantes enquanto um controle mínimo do processo é garantido. Esses algoritmos possibilitam aplicar identificação em malha fechada, na qual o modelo do controlador SPC é reidentificado utilizando dados previamente excitados. Os controladores SPC e SPC com excitação interna são testados e comparados ao MPC por meio de simulações em dois processos distintos. O primeiro consiste em uma coluna debutanizadora de uma unidade de destilação, para a qual são disponibilizados dois modelos lineares referentes a pontos de operação diferentes. O segundo é um reator de polimerização de estireno com dinâmica não linear, cujo modelo fenomenológico é conhecido. Os resultados dos testes indicam que o SPC é mais suscetível a ruídos de medição. Entretanto, verifica-se que esse controlador corrige perturbações nos set-points das variáveis controladas mais rapidamente que o MPC. Simulações realizadas para o SPC com excitação interna mostram que os algoritmos propostos neste trabalho excitam o sistema satisfatoriamente, de modo que modelos mais precisos são obtidos na reidentificação com os dados excitados. / Model Predictive Control (MPC) technology is widely used in chemical process industries. Subspace identification (SID) on the other hand has proven to be an efficient alternative for classical system identification methods. Based on the results from MPC and SID, it was developed in the late 90s a new control approach, called Subspace Predictive Control (SPC). This approach is also known as data-driven predictive control. In this new method, one single operation replaces the three steps in a MPC controller design: system identification, the state observer design and the predictor matrices construction. The aim of this work is to review studies in the field of SPC, to apply this technology to typical systems of chemical industry and to propose new algorithms. It is developed three internal excitation algorithms for the SPC method, which allow the system to be persistently excited while a minimal control of the process is still guaranteed. These algorithms enable the application of closedloop identification, where the SPC controller model is re-identified using the previously excited data. The SPC controller and the SPC controller with internal excitation are tested through simulation for two different processes. The first one is a debutanizer column of a distillation unit for which two linear models corresponding to two different operating points are available. The second one is a non-linear system consisting of a styrene polymerization reactor. A phenomenological model is provided for this system. Tests results indicate that SPC is more susceptible to measurement noises. However, it is noticed that SPC controller corrects perturbations on set-points faster than MPC. Simulations for the SPC with internal excitation show that the proposed algorithms sufficiently excite the system, in the sense that more precise models are obtained from the re-identification with excited data.

Controle preditivo baseado em modelos (MPC) é uma técnica de controle amplamente utilizada na indústria de processos químicos. Por outro lado, o método de identificação em subespaços (SID) tem se mostrado uma alternativa eficiente para os métodos clássicos de identificação de sistemas. Pela combinação dos conceitos de MPC e SID, surgiu, no final da década de 90, uma nova técnica de controle, denominada controle preditivo com enfoque em subespaços (SPC). Essa técnica também é conhecida como controle preditivo orientado a dados. Ela substitui por um único passo as três etapas do projeto de um MPC: a identificação do modelo, o cálculo do observador de estados e a construção das matrizes de predição. Este trabalho tem como principal objetivo revisar estudos feitos na área de SPC, aplicar esse método em sistemas típicos da indústria química e propor novos algoritmos. São desenvolvidos três algoritmos de excitação interna para o método SPC, que permitem gerar dados persistentemente excitantes enquanto um controle mínimo do processo é garantido. Esses algoritmos possibilitam aplicar identificação em malha fechada, na qual o modelo do controlador SPC é reidentificado utilizando dados previamente excitados. Os controladores SPC e SPC com excitação interna são testados e comparados ao MPC por meio de simulações em dois processos distintos. O primeiro consiste em uma coluna debutanizadora de uma unidade de destilação, para a qual são disponibilizados dois modelos lineares referentes a pontos de operação diferentes. O segundo é um reator de polimerização de estireno com dinâmica não linear, cujo modelo fenomenológico é conhecido. Os resultados dos testes indicam que o SPC é mais suscetível a ruídos de medição. Entretanto, verifica-se que esse controlador corrige perturbações nos set-points das variáveis controladas mais rapidamente que o MPC. Simulações realizadas para o SPC com excitação interna mostram que os algoritmos propostos neste trabalho excitam o sistema satisfatoriamente, de modo que modelos mais precisos são obtidos na reidentificação com os dados excitados. / Model Predictive Control (MPC) technology is widely used in chemical process industries. Subspace identification (SID) on the other hand has proven to be an efficient alternative for classical system identification methods. Based on the results from MPC and SID, it was developed in the late 90s a new control approach, called Subspace Predictive Control (SPC). This approach is also known as data-driven predictive control. In this new method, one single operation replaces the three steps in a MPC controller design: system identification, the state observer design and the predictor matrices construction. The aim of this work is to review studies in the field of SPC, to apply this technology to typical systems of chemical industry and to propose new algorithms. It is developed three internal excitation algorithms for the SPC method, which allow the system to be persistently excited while a minimal control of the process is still guaranteed. These algorithms enable the application of closedloop identification, where the SPC controller model is re-identified using the previously excited data. The SPC controller and the SPC controller with internal excitation are tested through simulation for two different processes. The first one is a debutanizer column of a distillation unit for which two linear models corresponding to two different operating points are available. The second one is a non-linear system consisting of a styrene polymerization reactor. A phenomenological model is provided for this system. Tests results indicate that SPC is more susceptible to measurement noises. However, it is noticed that SPC controller corrects perturbations on set-points faster than MPC. Simulations for the SPC with internal excitation show that the proposed algorithms sufficiently excite the system, in the sense that more precise models are obtained from the re-identification with excited data.

A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of satisfaction are approximated by one-step ahead sampling, with a greedy solution to the resulting mixed integer problem. An algorithm is given to enlarge a robustly invariant terminal set to exploit the probabilistic constraints. Exponential basis functions are used to create a Robust MPC algorithm for which the predictions are defined over the infinite horizon. The control degrees of freedom are weights that define the bounds on the state and input uncertainty when multiplied by the basis functions. The controller handles multiplicative and additive uncertainty. Robust MPC is applied to the problem of wind turbine control. Rotor speed and tower oscillations are controlled by a low sample rate robust predictive controller. The prediction model has multiplicative and additive uncertainty due to the uncertainty in short-term future wind speeds and in model linearisation. Robust MPC is compared to nominal MPC by means of a high-fidelity numerical simulation of a wind turbine under the two controllers in a wide range of simulated wind conditions.