Simultaneous Nonlinear Model Predictive Control and State Estimation: Theory and Applications

As computational power increases, online optimization is becoming a ubiquitous approach for solving control and estimation problems in both academia and industry. This widespread popularity of online optimization techniques is largely due to their abilities to solve complex problems in real time and to explicitly accommodate hard constraints. In this dissertation, we discuss an especially popular online optimization control technique called model predictive control (MPC). Specifically, we present a novel output-feedback approach to nonlinear MPC, which combines the problems of state estimation and control into a single min-max optimization. In this way, the control and estimation problems are solved simultaneously providing an output-feedback controller that is robust to worst-case system disturbances and noise. This min-max optimization is subject to the nonlinear system dynamics as well as constraints that come from practical considerations such as actuator limits. Furthermore, we introduce a novel primal-dual interior-point method that can be used to efficiently solve the min-max optimization problem numerically and present several examples showing that the method succeeds even for severely nonlinear and non-convex problems. Unlike other output-feedback nonlinear optimal control approaches that solve the estimation and control problems separately, this combined estimation and control approach facilitates straightforward analysis of the resulting constrained, nonlinear, closed-loop system and yields improved performance over other standard approaches. Under appropriate assumptions that encode controllability and observability of the nonlinear process to be controlled, we show that this approach ensures that the state of the closed-loop system remains bounded. Finally, we investigate the use of this approach in several applications including the coordination of multiple unmanned aerial vehicles for vision-based target tracking of a moving ground vehicle and feedback control of an artificial pancreas system for the treatment of Type 1 Diabetes. We discuss why this novel combined control and estimation approach is especially beneficial for these applications and show promising simulation results for the eventual implementation of this approach in real-life scenarios

A two-layer control architecture for operational management and hydroelectricity production maximization in inland waterways using model predictive control

This work presents the design of a combined control and state estimation approach to simultaneously maintain optimal water levels and maximize hydroelectricity generation in inland waterways using gates and ON/OFF pumps. The latter objective can be achieved by installing turbines within canal locks, which harness the energy generated during lock filling and draining operations. Hence, the two objectives are antagonistic in nature, as energy generation maximization results from optimizing the number of lock operations, which in turn causes unbalanced upstream and downstream water levels. To overcome this problem, a two-layer control architecture is proposed. The upper layer receives external information regarding the current tidal period, and determines control actions that maintain optimal navigation conditions and maximize energy production using model predictive control (MPC) and moving horizon estimation (MHE). This information is provided to the lower layer, in which a scheduling problem is solved to determine the activation instants of the pumps that minimize the error with respect to the optimal pumping references. The strategy is applied to a realistic case study, using a section of the inland waterways in northern France, which allows to showcase its efficacy.Peer ReviewedPostprint (author's final draft

Model predictive control and moving horizon estimation for water level regulation in inland waterways

This work regards the design of optimization techniques for the purposes of state estimation and control in the framework of inland waterways, often characterized by negligible bottom slopes and large time delays. The derived control-oriented model allows these issues to be handled in a suitable manner. Then, the analogous moving horizon estimation and model predictive control techniques are applied in a centralized manner to estimate the unmeasurable states and fulfill the operational goals, respectively. Finally, the performance of the methodology is tested in simulation by means of a realistic case study based on part of the inland waterways in the north of France. The results show that the proposed methodology is able to guarantee the navigability condition, as well as the other operational goals.Peer ReviewedPostprint (author's final draft

Graph Representation And Distributed Control Of Lumped And Distributed Parameter System Networks

University of Minnesota Ph.D. dissertation. 2019. Major: Chemical Engineering. Advisor: Prodromos Daoutidis. 1 computer file (PDF); 166 pages.Chemical plants are complex, integrated networks of individual process systems. The process system dynamics along with the interconnections among them make the task of controlling chemical plants challenging. Distributed control is a promising approach towards achieving plant-wide control of tightly integrated networks. The identification of sparsely interacting sub-networks in a given chemical network is key towards achieving superior performance from the distributed control structure. To this end, community detection algorithms have been adopted to determine the optimal decompositions for chemical networks by maximization of modularity. These algorithms are based on equation graph representations of the network. For lumped parameter system (LPS) networks, such representations are standard. Since chemical networks usually comprise lumped as well as distributed parameter systems (DPSs), this thesis aims at incorporating within the framework described above, the variables and topology of DPSs, to develop a unified framework to obtain optimal network decompositions (control structures) for distributed control. To this end, an equation graph representation for a generic DPS and a parameter which captures the strength of structural interactions among its variables analogous to relative degree in LPSs are proposed. A relationship is established between the length of the input-output path in the equation graph and the structural interaction parameter, which enables the incorporation of DPSs variables in the graph based community detection algorithms. Also, since in chemical networks, often the measurement of the entire state is not available and estimation of the unmeasured variables is a computationally expensive task, this thesis also addresses the problem of combined distributed state estimation and distributed control, using community detection for determining network decompositions for estimation as well as control

Modeling, Parameter Identification, and Degradation-Conscious Control of Polymer Electrolyte Membrane (PEM) Fuel Cells

Polymer electrolyte membrane (PEM) fuel cells are touted as zero-emission alternatives to internal combustion engines for automotive applications. However, high cost and durability issues have hindered their commercialization. Therefore, significant research efforts are underway to better understand the scientific aspects of PEM fuel cell operation and engineer its components for improved lifetime and reduced cost. Most of the research in this area has been focused on material development. However, as demonstrated by Toyota's fuel cell vehicle, intelligent control strategies may lead to significantly improved durability of the fuel cell stack even with existing materials. Therefore, it seems that the outstanding issues can be better resolved through a combination of improved materials and effective control strategies. Accordingly, this dissertation aims to develop a model-based control strategy to improve performance and durability of PEM fuel cell systems for automotive applications. To this end, the dissertation first develops a physics-based and computationally efficient model for online estimation purposes. The need for such a model arises from the fact that detailed information about the internal states of the cell is required to develop effective control strategies for improved performance and durability, and such information is rarely available from direct measurements. Therefore, a software sensor must be developed to provide the required signals for a control system. To this end, this work utilizes spatio-temporal decoupling of the underlying problem to develop a model that can estimate water and temperature distributions throughout an operating fuel cell in a computationally efficient manner. The model is shown to capture a variety of complex physical phenomena, while running at least an order of magnitude faster than real time for dynamically changing conditions. The model is also validated with extensive experimental measurements under different operating conditions that are of interest for automotive applications. Furthermore, the dissertation extensively explores the sensitivity of the model predictions to a variety of parameters. The sensitivity results are used to study the parameter identifiability problem in detail. The challenges associated with parameter identification in such a large-scale physics-based model are highlighted and a model parameterization framework is proposed to address them. The proposed framework consists of three main components: (1) selecting a subset of model parameters for identification, (2) optimally designing experiments that are maximally informative for parameter identification, and (3) designing a multi-step identification algorithm that ensures sufficient regularization of the inverse problem. These considerations are shown to lead to effective model parameterization with limited experimental measurements. Finally, the dissertation uses a version of the proposed model to develop a degradation-conscious model-predictive control (MPC) framework to enhance the performance and durability of PEM fuel cell systems. In particular, a reduced-order model is developed for control design, which is then successively linearized about the current operating point to enable use of linear control design techniques that offer significant computational advantages. A variety of constraints on system safety and durability are considered and simulation case studies are conducted to evaluate the framework's utility in maximizing performance while respecting the durability constraints. It is also shown that the linear MPC framework employed here can generate the optimal control commands faster than real time. Therefore, the proposed framework is expected to be implementable in practical applications and contribute to extending the lifetime of fuel cell systems.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155288/1/goshtasb_1.pd