As vehicles become electrified and more intelligent in terms of sensing, actuation and processing; a number of interesting possibilities arise in controlling vehicle dynamics and driving behavior. Over-actuation with inwheel motors, all wheel steering and active camber is one such possibility, and can facilitate control combinations that push boundaries in energy consumption and safety. Optimal control can be used to investigate the best combinations of control inputs to an over-actuated system. In Part 1, a literature study is performed on the state of art in the field of optimal control, highlighting the strengths and weaknesses of different methods and their applicability to a vehicular system. Out of these methods, Dynamic Programming and Model Predictive Control are of particular interest. Prior work in overactuation, as well as control for reducing tire energy dissipation is studied, and utilized to frame the dynamics, constraints and objective of an optimal control problem. In Part 2, an optimal control problem representing the lateral dynamics of an over-actuated vehicle is formulated, and solved for different objectives using Dynamic Programming. Simulations are performed for standard driving maneuvers, performance parameters are defined, and a system design study is conducted. Objectives include minimizing tire cornering resistance (saving energy) and maintaining the reference vehicle trajectory (ensuring safety), and optimal combinations of input steering and camber angles are derived as a performance benchmark. Following this, Model Predictive Control is used to design an online controller that follows the optimal vehicle state, and studies are performed to assess the suitability of MPC to over-actuation. Simulation models are also expanded to include non-linear tires. Finally, vehicle implementation is considered on the KTH Research Concept Vehicle (RCV) and four vehicle-implementable control cases are presented. To conclude, this thesis project uses methods in optimal control to find candidate solutions to improve vehicle performance thanks to over-actuation. Extensive vehicle tests are needed for a clear indication of the energy saving achievable, but simulations show promising performance improvements for vehicles overactuated with all-wheel steering and active camber.

<p>In this thesis, a human-in-the-loop control system is presented to assist an operator in teleoperation of a mobile robot. In a conventional teleoperation paradigm, the human operator would directly navigate the robot without any assistance which may result in poor performance in complex and unknown task environments due to inadequacy of visual feedback. The proposed method in this thesis builds on an earlier general control framework that systematically combines teleoperation and autonomous control subtasks. In this approach, the operator controls the mobile robot (slave) using a force-feedback haptic interface (master). Teleoperation control commands coordinate master and slave robots while an autonomous control subtask helps the operator avoid collisions with obstacles in the robot task environment by providing corrective force feedback. The autonomous collision avoidance is based on a Model Predictive Control (MPC) philosophy. The autonomous subtask control commands are generated by formulating and solving a constrained optimization problem over a rolling horizon window of time into the future using system models to predict the operator force and robot motion. The goal of the optimization is to prevent collisions within the prediction horizon by applying corrective force feedback, while minimizing interference with the operator teleoperation actions. It is assumed that the obstacles are stationary and sonar sensors mounted on the mobile robot measure the obstacle distances relative to the robot. Two formulation of MPC-based collision avoidance are proposed. The first formulation directly incorporates raw observation points as constraints in the MPC optimization problem. The second formulation relies on a line segment representation of the task environment. This thesis employs the well-known Hough transform method to effectively transform the raw sensor data into line segments. The extracted line segments constitute a compact model for the environment that is used in the formulation of collision constraints. The effectiveness of the proposed model-predictive control obstacle avoidance schemes is demonstrated in teleoperation experiments where the master robot is a 3DOF haptic interface and the slave is a P3-DX mobile robot equipped with eight (8) sonar sensors at the front.</p> / Master of Applied Science (MASc)

This thesis focuses on utilizing the persistent voltage vector injections by finite control set model predictive control (FCSMPC) to enable simultaneous estimations of both position and parameters in order to realize robust sensorless interior permanent magnet synchronous machine (IPMSM) drives valid at the entire operating region including no-load standstill without any additional signal injection and switchover. The system (here, IPMSM) needs to meet certain observability conditions to identify the parameters and position. Moreover, each combination of the parameters and/or position involves different observability requirements which cannot be accomplished at every operating point. In particular, meeting the observability for parameters and position at no-load standstill is more challenging. This is overcome by generating persistent excitation in the system with high-frequency signal injection. The FCSMPC scheme inherently features the persistent excitation with voltage vector injection and hence no additional signal injection is required. Moreover, the persistent excitation always exists for FCSMPC except at the standstill where the control applies the null vectors when the reference currents are zero. However, introducing a small negative d axis current at the standstill would be sufficient to overcome this situation.The parameter estimations are investigated at first in this thesis. The observability is analyzed for the combinations of two, three and four parameters and experimentally validated by online identification based on recursive least square (RLS) based adaptive observer. The worst case operating points concerning observability are identified and experimentally proved that the online identification of all the parameter combinations could be accomplished with persistent excitation by FCMPC. Moreover, the effect of estimation error in one parameter on the other known as parameter coupling is reduced with the proposed decoupling technique. The persistent voltage vector injections by FCSMPC help to meet the observability conditions for estimating the position, especially at low speeds. However, the arbitrary nature of the switching ripples and absence of PWM modulator void the possibility of applying the standard demodulation based techniques for FCSMPC. Consequently, a nonlinear optimization based observer is proposed to estimate both the position and speed, and experimentally validated from standstill to maximum speed. Furthermore, a compensator is also proposed that prevents converging to saddle and symmetrical ( ambiguity) solutions. The robustness analysis of the proposed nonlinear optimization based observer shows that estimating the position without co-estimating the speed is more robust and the main influencing parameters on the accuracy of the position estimation are d and q inductances. Subsequently, the proposed nonlinear optimization based observer is extended to simultaneously estimate the position, d and q inductances. The experimental results show the substantial improvements in response time, and reduction in both steady and transient state position errors. In summary, this thesis presents the significance of persistent voltage vector injections in estimating both parameter and position, and also shows that nonlinear optimization based technique is an ideal candidate for robust sensorless FCSMPC. / Thesis / Doctor of Philosophy (PhD)

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)

This thesis mainly focuses on parallel hybrid electric vehicle energy management problems considering fueling control and air path dynamics of a diesel engine. It aims to explore the concealed fuel-saving potentials in conventional energy management strategies, by employing detailed engine models. The contributions of this study lie on the following aspects: 1) Fueling control consists of fuel injection mass and timing control. By properly selecting combinations of fueling control variables and torque split ratio, engine efficiency is increased and the HEV fuel consumption is further reduced. 2) A transient engine model considering air path dynamics is applied to more accurately predict engine torque. A model predictive control based energy management strategy is developed and solved by dynamic programming. The fuel efficiency is improved, comparing the proposed strategy to those that ignore the engine transients. 3) A novel adaptive control-step learning model predictive control scheme is proposed and implemented in HEV energy management design. It reveals a trade-off between control accuracy and computational efficiency for the MPC based strategies, and demonstrates a good adaptability to the variation of driving cycle while maintaining low computational burden. 4) Two methods are presented to deal with the conjunction between consecutive functions in the piece-wise linearization for the energy management problem. One of them shows a fairly close performance with the original nonlinear method, but much less computing time. / Thesis / Doctor of Philosophy (PhD)

no / A milk pasteurization process is a nonlinear process and multivariable interacting system. This makes it difficultly to control by the conventional on-off controllers. Even if the on-off controller can managed the milk temperatures in the holding tube and the cooling stage of the plate pasteurizer according to the plant's requirements, the dynamic profiles of the milk temperature are oscillating around a desired value. Consequently, this work is aimed at improving the control performance by a multi-variables control approach with model predictive control (MPC). The proposed algorithm was tested in the case of set point tracking under nominal condition gathered by the real observation. To compare the performance of the MPC controller, a model-based control approach of generic model control (GMC) coupled with cascade control strategy is taken into account. The simulation results demonstrated that a proposed control algorithm performed well in keeping both the milk and water temperatures at the desired set points without any oscillation and overshoot. Because of the predictive control strategy, the control response for MPC was less drastic control action compared to the GMC.

No / A novel threefold optimization algorithm is proposed to simultaneously solve the nonlinear model predictive control and dynamic real-time optimization for batch processes while optimizing the batch operation time. Object-oriented programming and parallel computing are exploited to make the algorithm effective to handle industrial cases. A well-known literature case is selected to validate the algorithm.

This dissertation aims to realize the goal of deploying legged robots that cooperatively walk to transport objects in complex environments. More than half of the Earth's continent is unreachable to wheeled vehicles---this motivates the deployment of collaborative legged robots to enable the accessibility of these environments and thus bring robots into the real world. Although significant theoretical and technological advances have allowed the development of distributed controllers for complex robot systems, existing approaches are tailored to the modeling and control of multi-agent systems composed of collaborative robotic arms, multi-fingered robot hands, aerial vehicles, and ground vehicles, but not collaborative legged agents. Legged robots are inherently unstable, unlike most of the systems where these algorithms have been deployed. Models of cooperative legged robots are further described by high-dimensional, underactuated, and complex hybrid dynamical systems, which complicate the design of control algorithms for coordination and motion control. There is a fundamental gap in knowledge of control algorithms for safe motion control of these inherently unstable hybrid dynamical systems, especially in the context of collaborative work. The overarching goal of this dissertation is to create a formal foundation based on scalable optimization and robust and nonlinear control to develop distributed and hierarchical feedback control algorithms for cooperative legged robots to transport objects in complex environments. We first develop a hierarchical nonlinear control algorithm, based on model predictive control (MPC), quadratic programming (QP), and virtual constraints, to generate and stabilize locomotion patterns in a real-time manner for dynamical models of single-agent quadrupedal robots. The higher level of the proposed control scheme is developed based on an event-based MPC that computes the optimal center of mass (COM) trajectories for a reduced-order linear inverted pendulum (LIP) model subject to the feasibility of the net ground reaction force (GRF). QP-based virtual constraint controllers are developed at the lower level of the proposed control scheme to impose the full-order dynamics to track the optimal trajectories while having all individual GRFs in the friction cone. The analytical results are numerically verified to demonstrate stable and robust locomotion of a 22 degree of freedom (DOF) quadrupedal robot, in the presence of payloads, external disturbances, and ground height variations. We then present a hierarchical nonlinear control algorithm for the real-time planning and control of cooperative locomotion of legged robots that collaboratively carry objects. An innovative network of reduced-order models subject to holonomic constraints, referred to as interconnected LIP dynamics, is presented to study quasi-statically stable cooperative locomotion. The higher level of the proposed algorithm employs a supervisory controller, based on event-based MPC, to effectively compute the optimal reduced-order trajectories for the interconnected LIP dynamics. The lower level of the proposed algorithm employs distributed nonlinear controllers to reduce the gap between reduced- and full-order complex models of cooperative locomotion. We numerically investigate the effectiveness of the proposed control algorithm via full-order simulations of a team of collaborative quadrupedal robots, each with a total of 22 DOFs. The dissertation also investigates the robustness of the proposed control algorithm against uncertainties in the payload mass and changes in the ground height profile. Finally, we present a layered control approach for real-time trajectory planning and control of dynamically stable cooperative locomotion by two holonomically constrained quadrupedal robots. An innovative and interconnected network of reduced-order models, based on the single rigid body (SRB) dynamics, is developed for trajectory planning purposes. At the higher level of the control scheme, two different MPC algorithms are proposed to address the optimal control problem of the interconnected SRB dynamics: centralized and distributed MPCs. The MPCs compute the reduced-order states, GRFs, and interaction wrenches between the agents. The distributed MPC assumes two local QPs that share their optimal solutions according to a one-step communication delay and an agreement protocol. At the lower level of the control scheme, distributed nonlinear controllers are employed to impose the full-order dynamics to track the prescribed and optimal reduced-order trajectories and GRFs. The effectiveness of the proposed layered control approach is verified with extensive numerical simulations and experiments for the blind, robust, and cooperative locomotion of two holonomically constrained A1 robots with different payloads on different terrains and in the presence of external disturbances. It is shown that the distributed MPC has a performance similar to that of the centralized MPC, while the computation time is reduced significantly. / Doctor of Philosophy / Future cities will include a complex and interconnected network of collaborative robots that cooperatively work with each other and people to support human societies. Human-centered communities, including factories, offices, and homes, are developed for humans who are bipedal walkers capable of stepping over gaps, walking up/down stairs, and climbing ladders. One of the most challenging problems in deploying the next generation of collaborative robots is maneuvering in those complex environments. Although significant theoretical and technological advances have allowed the development of distributed controllers for motion control of multi-agent robotic systems, existing approaches do not address the collaborative locomotion problem of legged robots. Legged robots are inherently unstable with nonlinear and hybrid natures, unlike most systems where these algorithms have been deployed. Furthermore, the evolution of legged collaborative robot teams that cooperatively manipulate objects can be represented by high-dimensional and complex dynamical systems, complicating the design of control algorithms for coordination and motion control. This dissertation aims to establish a formal foundation based on nonlinear control and optimization theory to develop hierarchical feedback control algorithms for effective motion control of legged robots. The proposed layered control algorithms are developed based on interconnected reduced-order models. At the high level, we formulate cooperative locomotion as an optimal control problem of the reduced-order models to generate optimal trajectories. To realize the generated optimal trajectories, nonlinear controllers at the low level of the hierarchy impose the full-order models to track the trajectories while sustaining stability. The effectiveness of the proposed layered control approach is verified with extensive numerical simulations and experiments for the blind and stable cooperative locomotion of legged robots with different payloads on different terrains and subject to external disturbances. The proposed architecture's robustness is shown under various indoor and outdoor conditions, including landscapes with randomly placed wood blocks, slippery surfaces, gravel, grass, and mulch.

yes / Batch reactive distillation is aimed at achieving a high purity product, therefore, there is a great deal to find an optimal operating condition and effective control strategy to obtain maximum of the high purity product. An off-line dynamic optimization is first performed with an objective function to provide optimal product composition for the batch reactive distillation: maximum productivity. An inferential state estimator (an extended Kalman filter, EKF) based on simplified mathematical models and on-line temperature measurements, is incorporated to estimate the compositions in the reflux drum and the reboiler. Model Predictive Control (MPC) has been implemented to provide tracking of the desired product compositions subject to simplified model equations. Simulation results demonstrate that the inferential state estimation can provide good estimates of compositions. Therefore, the control performance of the MPC with the inferential state is better than that of PID. In addition, in the presence of unknown/uncertain parameters (forward reaction rate constant), the estimator is still able to provide accurate concentrations. As a result, the MPC with the inferential state is still robust and applicable in real plants.

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