Browsing by Subject "Distributed control"
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Item Graph Representation And Distributed Control Of Lumped And Distributed Parameter System Networks(2019-05) Moharir, ManjiriChemical 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.Item Maximum-stability Distributed Control in Traffic Networks(2021-05) Chen, RongshengThe max-pressure control is a distributed control algorithm that has the property of stabilizing the total queue length in the network theoretically. In spite of its good properties, some assumptions or requirements of the max-pressure control make it hard to be applied to traffic networks in reality: such as the data collection of queue length information for each movement and fixed route choices. Besides, traditional max-pressure control algorithms are only designed for signal-controlled intersections and are not applicable for signal-free intersections. Therefore, this thesis proposes max-pressure control algorithms and tests their performances in traffic networks while relaxing some of the assumptions used in existing studies. This thesis first explores mild assumptions for weight functions to incorporate alternative data sources in max-pressure control. This thesis also proposes an autonomous intersection management (AIM) algorithm considering pedestrians using the max-pressure control. Besides, the performance of max-pressure control is tested when road users' route choice is considered using dynamic traffic assignment, and a routing guidance algorithm is also developed to modify road users' route choices and to improve network efficiency.Item Methods Of Distributed And Nonlinear Process Control: Structuredness, Optimality And Intelligence(2020-05) Tang, WentaoChemical processes are intrinsically nonlinear and often integrated into large-scale networks, which are difficult to control effectively. The traditional challenges faced by process control, as well as the modern vision of transitioning industries into a smart manufacturing paradigm, requires the instillation of new perspectives and application of new methods to the control of chemical processes. The goal is to realize highly automated, efficient, well-performing and flexible control strategies for nonlinear, interconnected and uncertain systems. Motivated by this, in this thesis, the following three important aspects (objectives) for contemporary process control -- structuredness, optimality, and intelligence -- are discussed in the corresponding three parts. 1. For the control of process networks in a structured and distributed manner, a network-theoretic perspective is introduced, which suggests to find a decomposition of the problem according to the block structures in the network. Such a perspective is examined by sparse optimal control of Laplacian network dynamics. Community detection-based methods are proposed for input--output bipartite and variable-constraint network representations and applied to a benchmark chemical process. 2. For the optimality of control, we first derive a computationally efficient algorithm for nonconvex constrained distributed optimization with theoretically provable convergence properties -- ELLADA, which is applied to distributed nonlinear model predictive control of a benchmark process system. We derive bilevel optimization formulations for the Lyapunov stability analysis of nonlinear systems, and stochastic optimization for optimally designing the Lyapunov function, which can be further integrated with the optimal process design problem. 3. Towards a more intelligent diagram of process control, we first investigate an advantageous Lie-Sobolev nonlinear system identification scheme and its effect on nonlinear model-based control. For model-free data-driven control, we discuss a distributed implementation of the adaptive dynamic programming idea. For chemical processes where states are mostly unmeasurable, dissipativity learning control (DLC) is proposed as a suitable framework of input--output data-driven control, and applied to several nonlinear processes. Its theoretical foundations are also discussed.