Process economic improvement subject to safety, operational and environmental constraints is an ultimate goal of using on-line process optimization and control techniques. The dynamic nature of present-day market conditions motivates the consideration of process dynamics within the economic optimization calculation. Two key paradigms for implementing real-time dynamic economic optimization are a dynamic real-time optimization (DRTO) and regulatory MPC two-layer architecture, and a single-level economic model predictive control (EMPC) con figuration. In the two-layer architecture, the economically optimal set-point trajectories computed in an upper DRTO layer are provided to the MPC layer, while in the single-layer EMPC con figuration the economics are incorporated within the MPC objective function. There are limited studies on a systematic performance comparison between these two approaches. Furthermore, these studies do not simultaneously consider the economic, disturbance rejection and computational performance criteria. Thus, it may not be clear under what conditions one particular method is preferable over the other. These reasons motivate a more comprehensive comparison between the two paradigms, with both open and closed-loop predictions considered in the DRTO calculations. In order to conduct this comparison, we utilize two process case studies for the economic analysis and performance comparison of on-line optimization systems. The first case study is a process involving two stirred-tank reactors in-series with an intermediate mixing point, and the second case study is a linear multi-input single-output (MISO) system. These processes are represented using a fi rst principles model in the form of differential-algebraic equations (DAEs) system for the first case study and a simplified linear model of a polymerization reactor for the second case study problem. Both of the case study processes include constraints associated with input variables, safety considerations, and output quality. In these case study problems, the objective of optimal process operation is net profit improvement. The following performance evaluation criteria are considered in this study: (I) optimal value of the economic objective function, (II) average run time (ART) over a same operating time interval, (III) cumulative output constraint violation (COCV) for each constraint. The update time of the single-layer approach is selected to be equal to that of the control layer in the two-layer formulations, while the update time of the economic layer in the two-layer formulation is bigger than that of the single-layer approach. The nonlinear programing (NLP) problems which result in the single-layer and two-layer formulations and the quadratic programing problem which corresponds to the MPC formulation are solved using the fmincon and quadprog optimization solvers in MATLAB. Performance assessment of the single-layer and two-layer formulations is evaluated in the presence of a variety of unknown disturbance scenarios for the first case study problem. The effect of a dynamic transition in the product quality is considered in the performance comparison of the single-layer and two-layer methods in the second case-study problem. The first case study problem results show that for all unknown disturbance scenarios, the economic performance of the single-layer approach is slightly higher than that of the two layer formulations. However, the average computation times for the DRTO-MPC two-layer formulations are at least one order of magnitude lower than that of the EMPC formulation. Also, comparison results of the COCV for the EMPC formulation for different sizes of update time intervals could justify the necessity of the MPC control layer to reduce the COCV for the economic optimization problems with update times larger than that of the MPC control layer. A similar computational advantage of the OL- and CL-DRTO-MPC over the EMPC is observed for the second case study problem. In particular, it is shown that increasing the economic horizon length in the EMPC formulation to a sufficiently large value may result a higher economic improvement. However, the increase in economic optimization horizon would increase the resulting NLP problem size. The computational burden could limit the use of the EMPC formulation with larger economic optimization horizons in real-time applications. The ART of the dual-layer methods is at least two orders of magnitude lower than that of the EMPC methods with an appropriate horizon length. The CL-DRTO-MPC economic performance is slightly less than that of the EMPC formulation with the same economic optimization horizon. In conclusion, the performance comparison on the basis of multiple criteria in this study demonstrates that the economic performance criterion is not necessarily the only important metric, and the operational constraint limitations and the optimization problem solution time could have an important impact on the selection of the most suitable real-time optimization approach. / Thesis / Master of Applied Science (MASc)

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The effective management of forces within heavy vehicles is essential for achieving desired performance outcomes. In this study, an auto-generated Model Predictive Control Allocation (MPCA) algorithm is presented. The controller is designed to distribute forces among individual actuators in a vehicle, focusing primarily on longitudinal forces while exploring lateral force dynamics. The approach integrates models of the actuators with vehicle dynamics, encompassing both point mass and dynamic vehicle models, within the controller framework. Through simulation, proof of the MPC's superior performance in reference tracking could be demonstrated, especially in comparison with baseline simulations employing force ratio split (FRS) and equal split (ES) distribution methods. Furthermore, findings show that it was possible to achieve a more energy efficient force distribution using the MPCs.

Control predictivo basado en modelos (MPC) es una metodología de control ampliamente utilizada en la industria por su habilidad para controlar procesos multivariable con restricciones en sus entradas y sus salidas. Se distinguen dos fases en la implementación de MPC: identificación y control. El propósito de esta tesis es doble: realizar contribuciones en la identificación para MPC y proponer una nueva metodología de control MPC. La respuesta en bucle cerrado de una implementación de MPC depende, en gran medida, de la capacidad de predicción del modelo; luego la identificación del modelo es un punto crucial en MPC y la parte que a menudo exige la mayor parte del tiempo del proyecto. El primer objetivo que cubre la tesis es la identificación para MPC. Puesto que un modelo es una aproximación del comportamiento de un proceso, dicha aproximación se puede hacer teniendo en cuenta el fin que se le va a dar al modelo. En MPC, el modelo se utiliza para realizar predicciones dentro de una ventana futura, luego la identificación para MPC (MRI) tiene en cuenta dicho uso del modelo y considera los errores de predicción dentro de dicha ventana para el ajuste de los parámetros del modelo. En esta tesis, se cubren tres temas dentro de MRI. Primero se define MRI y las distintas formas de abordarlo. Luego se compara en términos de MRI el ajuste de un modelo con múltiples entradas y múltiples salidas con el ajuste de varios modelos con múltiples entradas y una salida concluyendo que el ajuste de un único modelo con múltiples entradas y múltiples salidas proporciona mejores resultados en términos de MRI para horizontes de predicción lo suficientemente grandes. Por último, se propone el algoritmo PLS-PH para implementar MRI con modelos paramétricos en el caso de correlación en los datos de identificación. PLS-PH es un método de optimización numérica por búsqueda lineal basado en PLS (mínimos cuadrados parciales). Se muestra en un ejemplo como PLS-PH es capaz de proporcionar mejores modelos que las técnicas convencionales de MRI en modelos paramétricos en el caso de correlación en los datos de identi ficación. Una vez obtenido el modelo se puede formular el controlador predictivo. En esta tesis se propone LV-MPC, un controlador predictivo para procesos continuos que implementa la optimización en el espacio de las componentes principales. / Laurí Pla, D. (2012). MPC: Relevant Identification and Control in the Latent Variable Space [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15178

Today's Cyber-Physical Systems (CPS) are typically distributed over several computing nodes communicating by way of shared buses such as Controller Area Network (CAN). Their control performance gets degraded due to variable delays (jitters) incurred by messages on the shared CAN bus due to contention and network overhead. This work presents a novel online delay prediction approach that predicts the message delay at runtime based on real-time traffic information on CAN. It leverages the proposed method to improve control quality, by compensating for the message delay using the Model Predictive Control (MPC) algorithm in designing the controller. By simulating an automotive Cruise Control system and a DC Motor plant in a CAN environment, it goes on to demonstrate that the delay prediction is accurate, and that the MPC design which takes the message delay into consideration, performs considerably better. It also implements the proposed method on an 8-bit 16MHz ATmega328P microcontroller and measures the execution time overhead. The results clearly indicate that the method is computationally feasible for online usage. / Master of Science / In today’s world, most complicated systems such as automobiles employ a decentralized modular architecture with several nodes communicating with each other over a shared medium. The Controller Area Network (CAN) is the most widely accepted standard as far as automobiles are concerned. The performance of such systems gets degraded due to the variable delays (jitters) incurred by messages on the CAN. These delays can be caused by messages of higher importance delaying bus access to the messages of lower importance, or due to other network related issues. This work presents a novel approach that predicts the message delays in real-time based on the traffic information on CAN. This approach leverages the proposed method to improve the control quality by compensating for the message delay using an advanced controller algorithm called Model Predictive Control (MPC). By simulating an automotive Cruise Control system and a DC motor plant in a CAN environment, this work goes on to demonstrate that the delay prediction is accurate, and that the MPC design which takes the message delay into consideration, performs considerably better. It also implements the proposed approach on a low end microcontroller (8bit, 16MHz ATmega328P) and measures the time taken for predicting the delay for each message (execution overhead). The obtained results clearly indicate that the method is computationally feasible for use in a real-time scenario.

Quadrupedal robots play a vital role in various applications, from search and rescue operations to exploration in challenging terrains. However, locomotion tasks involving unknown payload transportation on rough terrains pose significant challenges, requiring adaptive control strategies to ensure stability and performance. This dissertation contributes to the advancement of adaptive motion planning and control solutions that enable quadrupedal robots to traverse unknown rough environments while tasked with transporting unknown payloads. In the first project, a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots is developed. This framework integrates an adaptive model predictive control (AMPC) algorithm with a gradient-descent-based adaptive updating law applied to reduced-order locomotion (i.e., template) models. At the high level of the control hierarchy, an indirect adaptive law estimates unknown parameters of the reduced-order locomotion model under varying payloads, ensuring stability during trajectory planning. The optimal trajectories generated by the AMPC are then passed to a low-level and full-order nonlinear whole-body controller (WBC) for tracking. Extensive numerical investigations and hardware experiments on the A1 quadru[pedal robot validate the framework's capabilities, showcasing significant improvements in payload transportation on both flat and rough terrains compared to conventional MPC strategies. Specifically, the robot demonstrates proficiency in transporting unmodeled, unknown static payloads up to 109% of its own mass in experiments on flat terrains and 91% on rough experimental terrains. Moreover, the robot successfully manages dynamic payloads with 73% of its mass on rough terrains. Adaptive controllers must also address external disturbances inherent in real-world environments. Therefore, the second project introduces a hierarchical planning and control scheme with an adaptive L1 nonlinear model predictive control (ANMPC) at the high level, which integrates nonlinear MPC (NMPC) with an L1 adaptive controller. The prescribed optimal state and control input profiles generated by the ANMPC are then fed to the low-level nonlinear WBC. This approach aims to stabilize locomotion gaits in the presence of parametric uncertainties and external disturbances. The proposed controller is analyzed to accommodate uncertainties and external disturbances. Comprehensive numerical simulations and experimental validations on the A1 quadrupedal robot demonstrate its effectiveness on rough terrains. Numerical results suggest that ANMPC significantly improves the stability of the gaits in the presence of uncertainties and external disturbances compared to NMPC and AMPC. The robot can carry payloads up to 109% of its own mass on its trunk on flat and rough terrains. Simulation results show that the robot achieves a maximum payload capacity of 26.3 (kg), which is equivalent to 211% of its own mass on rough terrains with uncertainties and disturbances. / Doctor of Philosophy / In the rapidly advancing domain of robotics, there is a growing demand for intelligent robotic systems capable of adeptly addressing novel and unforeseen scenarios, such as uneven paths or external forces applied to the robots, like kicks and hits. This necessitates robots with the capability to handle diverse tasks with precision, particularly in the domains of object transportation and navigation through unknown terrains in applications such as search and rescue operations or cargo handling. This dissertation introduces innovative motion planning and control frameworks designed to imbue robots with adaptive capabilities, enabling them to adapt to real-world unanticipated scenarios and uncertainties during their movement, particularly when carrying unknown payloads. In the first project, a new framework is developed to enhance payload transportation by quadrupedal robots. This framework integrates an adaptive model predictive control (AMPC) algorithm with a gradient-descent-based adaptive updating law. Through extensive experiments and simulations, the framework shows remarkable improvements in payload transportation on both flat and rough terrains. The robot successfully transports payloads exceeding its own mass by up to 109% on flat terrains and 91% on rough terrains. Recognizing the need to address uncertainties in real-world environments, the second project introduces a hierarchical planning and control scheme with adaptive L1 nonlinear model predictive control (ANMPC). This approach stabilizes legged locomotion in the presence of uncertainties and disturbances. Results demonstrate that ANMPC significantly improves gait stability compared to existing methods. The robot achieves a payload capacity of up to 109% of its own mass on both experimental flat and rough terrains and reaches a maximum of 26.3 kg (around 212% of its own mass) on rough terrain simulations with uncertainties and disturbances.

Advancements in the field of robotics together with an increased need for surveillance  have lead to an interest in utilizing autonomous agents  for area monitoring at sensitive installations. For this Master's thesis, an informative path planner called  Autonomous Surveillance Planner (ASP) was developed to be used with unmanned ground vehicles for area monitoring.  It discretizes an area of operations  into cells and assigns each cell an intruder probability. The planner then chooses the  optimal path by minimizing a cost function describing the probability of not finding an intruder along a path. Since the minimization is computationally costly, the computed path does not cover the entire area but instead only a small portion at a time.  The path from the ASP is then relayed to a timed elastic band local planner which adjusts  the path such that it avoids obstacles and is fast for an agent to execute, as well as computes control signals.   The algorithms were tested both in simulations and during field tests with promising results, showcasing that using unmanned ground vehicles for autonomous area monitoring has potential to be used in a real-world application. ASP was fast enough to be used in real-time  and was able to fully cover the area of operations. The local planner was computationally  demanding, but was able to avoid obstacles and make the agent follow the global path. / <p>Opponent: Samuel Ericson Andersson</p>

This dissertation explores the development of sophisticated distributed layered control algorithms focused on the navigation, planning, and control of multi-agent quadrupedal robots collaborating in uncertain environments. Quadrupedal robots are high-dimensional, complex systems that are inherently unstable, posing significant challenges in designing predictive control laws. Template models offer a solution by providing a bridging layer of reduced-order models with fewer state variables and linearized dynamics. However, this approach compromises the agility and full potential of these sophisticated machines, as template models may fail to capture the intricate nonlinear dynamics of quadrupedal robots. Furthermore, in multi-robot systems (MRS) where numerous robots operate concurrently, it becomes crucial to develop strategies embedding collision safety mechanisms. One approach involves embedding Euclidean distance constraints in the predictive control formulation. While effective, this method significantly complicates the optimal control (OC) problem and increases computational overhead. To mitigate these challenges, this dissertation explores hierarchical and distributed control frameworks, focusing on developing real-time feasible controllers that guarantee collision avoidance while preserving the agility of these hardware platforms by utilizing fully nonlinear template models. In particular, this research investigates a multi-layered framework consisting of potential fields at the high-level layer, a distributed nonlinear model predictive control (DNMPC) based middle-level layer responsible for uncertainty mitigation, and full-order nonlinear controllers at the low-level layer. Additionally, the latter part of this dissertation examines the integration of safety-ensuring control barrier functions (CBFs) into the nonlinear model predictive control (NMPC) layer, thereby providing rigorous mathematical guarantees for collision avoidance. The crux of this research lies in addressing the following questions: How do we design layered control frameworks to guarantee optimal gait planning and collision avoidance while maintaining computational tractability? How do we mitigate uncertainty in the environment in real-time using safety-critical control algorithms? / Doctor of Philosophy / This dissertation investigates advanced control strategies for coordinating teams of quadrupedal robots in dynamic and uncertain environments. Quadrupedal robots present significant challenges in control and stability due to their complex, high-dimensional nature and inherent instability. Current approaches often employ simplified models for control, which, while computationally efficient, fail to fully capture the intricate dynamics of these sophisticated machines, thus limiting their agility and potential. Furthermore, in multi-robot systems, ensuring collision avoidance is a practically integral part of control. Conventional methods, including Euclidean distance constraints for collision avoidance, prove effective but substantially increase computational demands and complicate the optimal control problem. To address these challenges, this research explores a hierarchical, distributed control framework designed to guarantee collision-free navigation while maximizing the agility of quadrupedal platforms through the use of comprehensive nonlinear models. The proposed framework consists of three primary layers: a high-level layer utilizing potential fields for global path planning, a middle layer employing distributed nonlinear model predictive control for local navigation and uncertainty mitigation, and a low-level layer implementing full-order nonlinear controllers for precise motion execution. Additionally, this work examines the integration of control barrier functions into the predictive control layer, providing mathematical guarantees for collision avoidance. The core objectives of this research are twofold: first, to develop layered control frameworks that ensure optimal gait planning and collision avoidance while maintaining computational feasibility; and second, to create real-time algorithms capable of mitigating environmental uncertainties using safety-critical control methods. By addressing these fundamental questions, this dissertation aims to advance the field of multi-agent quadrupedal robotics, enhancing the capability of robotic teams to operate effectively in complex, unpredictable environments. The potential applications of this research extend to critical areas such as search and rescue operations, environmental monitoring, and exploration of hazardous terrains.

This thesis describes the theory of model predictive control and application of the theory to synchronous drives. It shows explicit and on-line solutions and compares the results with classical vector control structure.

Les entraînements électriques polyphasés ont acquis une importance particulière ces derniers temps pour leur utilisation dans des applications où la fiabilité présente un intérêt pour des raisons économiques et de sécurité. Cette thèse se centre sur le développement de techniques de commande en mode instantané pour contrôler de manière optimale les machines polyphasées, en analysant leur tolérance dans différentes conditions de fonctionnement, telles que lors de l’atteinte de limites électriques (limites de tension, de courant et de niveau maximum de magnétisation) ou de défauts de type phase ouverte. Tout d’abord, la technique DTC est proposée pour gérer le cas de défaut de type phase ouverte dans la machine polyphasée. Une comparaison de la tolérance à la défaillance des commandes de type DTC par rapport à d’autres techniques de commande est réalisée, permettant une conclusion sur les forces et les faiblesses des méthodes analysées. Enfin, un contrôleur de courant optimal est développé utilisant des techniques MPC permettant une utilisation optimale de la capacité de couple du système en cas de limitations électriques. Des résultats de simulation et des validations expérimentales sont effectués pour corroborer les approches initiales, en utilisant des cas particuliers d’entraînements pentaphasés commandés avec différents sous-espaces de commande dans le domaine fréquentiel. / Multiphase drives have gained special relevance in recent times for their use in applications where reliability is of interest for economical and safety reasons. This Thesis focuses on the development of direct control techniques to optimally control multiphase machines, analyzing their tolerance to different limit operating conditions, such as electrical constraints (voltage, current and magnetization level limits) or failure situations such as an open-phase fault. First, the DTC technique is proposed to manage the open-phase fault operation of the multiphase machine. A comparison of the fault-tolerant capability of DTC with other control techniques is carried out, to conclude the strengths and weaknesses of the analyzed methods facing this limit operation. Finally, an optimal current controller is developed using MPC techniques that allows the optimal utilization of the system’s torque capability under electrical limitations. Simulation results and experimental validations are obtained to corroborate the initial approaches, through the use of particular cases of five-phase drives controlled using different frequency-domain control subspaces.