Magnetic Bearing Systems have been receiving a great deal of research attention for the past decades. Its inherent nonlinearity and open-loop instability are challenges for controller design. This thesis investigates and designs model predictive control strategy for an experimental Active Magnetic Bearing (AMB) laboratory system. A host-target development environment of real-time control system with hardware in the loop (HIL) is implemented. In this thesis, both continuous and discrete time model predictive controllers are studied. In the first stage, local MPC controllers are applied to control the AMB system; and in the second stage, concept of supervisory controller design is then investigated and implemented. Contributions of the thesis can be summarized as follows; 1. A Discrete time Model Predictive Controller has been developed and applied to the active magnetic bearing system. 2. A Continuous time Model Predictive Controller has been developed and applied to the active magnetic bearing system. 3. A frequency domain identification method using FSF has been applied to pursue model identification with respect to local MPC and magnetic bearing system. 4. A supervisory control strategy has been applied to pursue a two stages model predictive control of active magnetic bearing system.

We review recent research into trajectory planning for autonomous overtaking to understand existing challenges. Then, the recently developed framework Learning Model Predictive Control (LMPC) is presented as a suitable method to iteratively improve an overtaking manoeuvre each time it is performed. We present recent extensions to the LMPC framework to make it applicable to overtaking. Furthermore, we also present two alternative modelling approaches with the intention of reducing computational complexity of the optimization problems solved by the controller. All proposed frameworks are built from scratch in Python3 and simulated for evaluation purposes. Optimization problems are modelled and solved using the Gurobi 9.0 Python API gurobipy. The results show that LMPC can be successfully applied to the overtaking problem, with improved performance at each iteration. However, the first proposed alternative modelling approach does not improve computational times as was the intention. The second one does but fails in other areas. / Vi går igenom ny forskning inom trajectory planning för autonom omkörning för att förstå de utmaningar som finns. Därefter föreslås ramverket Learning Model Predictive Control (LMPC) som en lämplig metod för att iterativt förbättra en omkörning vid varje utförande. Vi tar upp utvidgningar av LMPC-ramverket för att göra det applicerbart på omkörningsproblem. Dessutom presenterar vi också två alternativa modelleringar i syfte att minska optimeringsproblemens komplexitet. Alla tre angreppssätt har byggts från grunden i Python3 och simulerats i utvärderingssyfte. Optimeringsproblem har modellerats och lösts med programvaran Gurobi 9.0s python-API gurobipy. Resultaten visar att LMPC kan tillämpas framgångsrikt på omkörningsproblem, med förbättrat utförande vid varje iteration. Den första alternativa modelleringen minskar inte beräkningstiden vilket var dess syfte. Det gör däremot den andra alternativa modelleringen som dock fungerar sämre i andra avseenden.​

Repetitive Model Predictive Control (RMPC) incorporates the idea of Repetitive Control (RC) into Model Predictive Control (MPC) to take full advantage of the constraint handling, multivariable control features of MPC in periodic processes. The RMPC achieves perfect asymptotic tracking/rejection in periodic processes, provided that the period length used in the control formulation matches the actual period of the reference/disturbance exactly. Even a small mismatch between the actual period of process and the controller period can deteriorate the RMPC performance significantly. The period mismatch occurs either from an inaccurate estimation of actual frequency of disturbance due to resolution limit or from trying to force the controller period to be an integer multiple of sampling time. An extension of RMPC called Robust Repetitive Model Predictive Control (R-RMPC) is proposed for such cases where period length cannot be predetermined accurately, or where period is not an integer multiple of sampling time. This robust RMPC borrows the idea of using weighted, multiple memory loops in RC for robustness enhancement. The modified RMPC is more robust in the sense that small changes in period length do not diminish the tracking/rejection properties by much. Simulation results show that R-RMPC achieves significant improvement over the standard RMPC in case of a slight period mismatch. The effectiveness of this Robust RMPC is demonstrated by applying it to a mechanical motion tracking machine whose function is to follow a constant trajectory while rejecting periodic disturbances of an uncertain period.

Motivated by the goal of transition to a zero-carbon-emission-based economy for climate change mitigation, electrification opportunities are more promising in the transportation sector. Electric Vehicles (EVs) are at the forefront of the energy transition at an expanded rapid pace in the transportation sector. To enable and enhance the energy efficiency, advanced control and optimization will play an important role in EV systems and infrastructure. However, there are also some difficulties and limitations subject to the imperfection of management and control for EVs. Overall, to further the widespread adoption of EVs, the dissertation mainly includes two parts: 1) Power management for Plug-in Hybrid Electric Vehicles (PHEVs); 2) Charging control for Plug-in Electric Vehicles (PEVs). Chapter 2 deals with the power management and route planning problems for PHEVs, which aims to properly design the control algorithm to find the route that leads to the minimum energy consumption. Chapter 3 pays attention to the high workloads of the PEV in the electric power grids, which concentrates on studying a control algorithm leading to possible reductions in both computation and communication. Chapter 4 focuses on the charging control for PEVs, which explores how to improve the PEV charging efficiency while satisfying safety concerns. Chapter 5 modifies the results in Chapter 4 by taking battery capacity degradation into the optimization problem. This dissertation proceeds with Chapter 1 by reviewing the state-of-the-art control methods for PEVs and PHEVs. Chapter 2 studies a novel control scheme of route planning with power management for PHEVs. By considering the power management of PHEVs, we aim to find the route that leads to the minimum energy consumption. The scheme adopts a two-loop structure to achieve the control objective. Specifically, in the outer loop, the minimum energy consumption route is obtained by minimizing the difference between the value function of current round and the best value from all previous rounds. In the inner loop, the energy consumption index with respect to PHEV power management for each feasible route is trained with Reinforcement Learning (RL). Under the RL framework, a nonlinear approximator structure, which consists of an actor approximator and a critic approximator, is built to approximate control actions and energy consumption. In addition, the convergence of value function for PHEV power management in the inner loop and asymptotical stability of the closed-loop system are rigorously guaranteed. Chapter 3 investigates the self-triggered Model Predictive Control (MPC) with Integral Sliding Mode (ISM) method of a networked nonlinear continuous-time system subject to state and input constraints with additive disturbances and uncertainties. Compared with the standard MPC strategy, the proposed control scheme is designed for PEV charging to reduce the high communication loads caused by a large-scale population of vehicles under centralized charging control architecture. In the proposed scheme, the constrained optimization problem is solved aperiodically to generate control signals and the next execution time, leading to possible reductions in both computation and communication. The motivation of using ISM approach is to reject matched uncertainties. A self-triggered condition that involves a comparison between the cost function values with different execution periods is derived. Besides, the robust MPC with ISM control strategy is rigorously studied depending on the self-triggered scheme. Chapter 4 proposes a charging control algorithm for the valley-filling problem, while it meets individual charging requirements. We study a decentralized framework of PEV charging problem with a coordination task. An iterative learning-based model predictive charging control algorithm is developed to achieve the valley-filling performance. The design of the decentralized MPC meets individual charging requirements. The iterative learning method approximates the electricity price function and the system state sampled safe set to improve the accuracy of optimization problem calculations. The decentralized problem, in which the individual PEV minimizes its own charging cost, is formulated based on the sum of all power loads. Chapter 5 studies a modified charging control algorithm based on the previous charging control algorithm in Chapter 4. We propose a charging control algorithm for PEVs using a decentralized MPC framework supplemented by the iterative learning method. By considering the battery aging of PEVs, we aim to find the optimal charging rate that leads to valley-filling performance. The scheme adopts the iterative learning-based method to solve the optimal control problem with the battery aging model. Specifically, the sampled safe set and price function are updated accordingly as the iteration number increases. The battery aging model involves the cost function to approach the real charging scenario. In addition, the recursive feasibility of the proposed optimal control problem for PEV charging with battery aging and asymptotical stability of the closed-loop system are rigorously studied. Finally, in Chapter 6, the conclusions of the dissertation and some avenues for future potential research are presented. / Graduate / 2023-04-07

Accidental collisions involving wheeled industrial ground vehicles can be costly to repair, cause serious (even fatal) human injury, and lead to setbacks with tight operation schedules. Reduction of vehicle collisions carries numerous safety and financial incentives. In this work, an integrated collision avoidance package is developed to reduce the number of vehicle collisions. Utilizing feedback from on-board sensing devices, a model predictive control (MPC) algorithm predicts control options and paths, then disallows drivers to accelerate and/or induces braking of the vehicle if a collision is imminent. A prototype system is developed, implemented, and tested on an industrial vehicle to mitigate collisions with people and high-value equipment. Testing results show that control can be executed in real time by the proposed system, and that the proposed method is effective in preventing an industrial vehicle from hitting detected obstacles and entering restricted areas.

<p>Model Predictive Control (MPC) is traditionally designed assuming no model mismatch and tuned to provide acceptable behavior when mismatch occurs. This thesis extends the MPC design to account for explicit mismatch in the control and optimization of a wide range of uncertain dynamic systems with feedback, such as in process control and supply chain optimization.</p> <p>The major contribution of the thesis is the development of a new MPC method for robust performance, which offers a general framework to optimize the uncertain system behavior in the closed-loop subject to hard bounds on manipulated variables and soft bounds on controlled variables. This framework includes the explicit handling of correlated, time-varying or time-invariant, parametric uncertainty appearing externally (in demands and disturbances) and internally (in plant/model mismatch) to the control system. In addition, the uncertainty in state estimation is accounted for in the controller.</p> <p> For efficient and reliable real-time solution, the bilevel stochastic optimization formulation of the robust MPC method is approximated by a limited number of (convex) Second Order Cone Programming (SOCP) problems with an industry-proven heuristic and the classical chance-constrained programming technique. A closed-loop uncertainty characterization method is also developed which improves real-time tractability by performing intensive calculations off-line.</p> <p>The new robust MPC method is extended for process control problems by integrating a robust steady-state optimization method addressing closed-loop uncertainty. In addition, the objective function for trajectory optimization can be formulated as nominal or expected dynamic performance. Finally, the method is formulated in deviation variables to correctly estimate time-invariant uncertainty.</p> <p>The new robust MPC method is also tailored for supply chain optimization, which is demonstrated through a typical industrial supply chain optimization problem. The robust MPC optimizes scenario-specific safety stock levels while satisfying customer demands for time-varying systems with uncertainty in demand, manufacturing and transportation. Complexity analysis and computational study results demonstrate that the robust MPC solution times increase with system scale moderately, and the method does not suffer from the curse of dimensionality.</p> / Thesis / Doctor of Philosophy (PhD)

<p> This thesis studies critical zones of automated vehicles. The goal is for the automated vehicle to complete a car-following or lane change maneuver without collision. For instance, the automated vehicle should be able to indicate its interest in changing lanes and plan how the maneuver will occur by using model predictive control theory, in addition to the autonomous vehicle toolbox in Matlab. A test bench (that includes a scenario creator, motion logic and planner, sensors, and radars) is created and used to calculate the parameters of a critical zone. After a trajectory has been planned, the automated vehicle then attempts the car following or lane change while constantly ensuring its safety to continue on this path. If at any point, the lead vehicle brakes or a trailing vehicle accelerates, the automated vehicle makes the decision to either brake, accelerate, or abandon the lane change. </p>

In this thesis I propose methods and strategies for the design of advanced model predictive control designs. The contributions are in the areas of data-driven model based MPC, model monitoring and explicit incorporation of closed-loop response considerations in the MPC, while handling issues such as plant-model mismatch, constraints and uncertainty. In the initial phase of this research, I address the problem of handling plant-model mismatch by designing a subspace identification based MPC framework that includes model monitoring and closed-loop identification components. In contrast to performance monitoring based approaches, the validity of the underlying model is monitored by proposing two indexes that compare model predictions with measured past output. In the event that the model monitoring threshold is breached, a new model is identified using an adapted closed-loop subspace identification method. To retain the knowledge of the nominal system dynamics, the proposed approach uses the past training data and current input, output and set-point as the training data for re-identification. A model validity mechanism then checks if the new model predictions are better than the existing model, and if they are, then the new model is utilized within the MPC. Next, the proposed MPC with re-identification method is extended to batch processes. To this end, I first utilize a subspace-based model identification approach for batch processes to be used in model predictive control. A model performance index is developed for batch process, then in the case of poor prediction, re-identification is triggered to identify a new model. In order to emphasize on the recent batch data, the identification is developed in order to increase the contribution of the current data. In another direction, the stability of data driven predictive control is addressed. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI) model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. Finally, I address the problem of control of nonlinear systems to deliver a prescribed closed-loop behavior. In particular, the framework allows for the practitioner to first specify the nature and specifics of the desired closed-loop behavior (e.g., first order with smallest time constant, second order with no more than a certain percentage overshoot, etc.). An optimization based formulation then computes the control action to deliver the best attainable closed loop behavior. To decouple the problems of determining the best attainable behavior and tracking it as closely as possible, the optimization problem is posed and solved in two tiers. In the first tier, the focus is on determining the best closed-loop behavior attainable, subject to stability and tracking constraints. In the second tier, the inputs are tweaked to possibly improve the tracking of the optimal output trajectories given by the first tier. The effectiveness of all of the proposed methods are illustrated through simulations on nonlinear systems. / Dissertation / Doctor of Philosophy (PhD)

This dissertation deals with some computational and analytic challenges for dynamic process operations using first-principles models. For processes with significant spatial variations, spatially distributed first-principles models can provide accurate physical descriptions, which are crucial for offline dynamic simulation and optimization. However, the large amount of time required to solve these detailed models limits their use for online applications such as nonlinear model predictive control (NMPC). To cope with the computational challenge, we develop computationally efficient and accurate dynamic reduced order models which are tractable for NMPC using temporal and spatial model reduction techniques. Then we introduce an input and state blocking strategy for NMPC to further enhance computational efficiency. To improve the overall economic performance of process systems, one promising solution is to use economic NMPC which directly optimizes the economic performance based on first-principles dynamic models. However, complex process models bring challenges for the analysis and design of stable economic NMPC controllers. To solve this issue, we develop a simple and less conservative regularization strategy with focuses on a reduced set of states to design stable economic NMPC controllers. In this thesis, we study the operation problems of a solid sorbent-based CO2 capture system with bubbling fluidized bed (BFB) reactors as key components, which are described by a large-scale nonlinear system of partial-differential algebraic equations. By integrating dynamic reduced models and blocking strategy, the computational cost of NMPC can be reduced by an order of magnitude, with almost no compromise in control performance. In addition, a sensitivity based fast NMPC algorithm is utilized to enable the online control of the BFB reactor. For economic NMPC study, compared with full space regularization, the reduced regularization strategy is simpler to implement and lead to less conservative regularization weights. We analyze the stability properties of the reduced regularization strategy and demonstrate its performance in the economic NMPC case study for the CO2 capture system.

Increased safety and productivity are driving factors for the trend in the mining industry where equipment and machines increasingly get automated. An example is the load-haul-dump vehicle, which is a machine that is used for transport of ore in underground mines. The cyclic load-haul-dump process is well suited for automation and automated loaders are commercially available today. Recent advances in autonomous driving have raised questions if there are efficiency gains that can be made by improving the path following algorithms that are used in the control. The aim of this thesis is to investigate the usage of model predictive control for path following for center-articulated mining vehicles. Two path following nonlinear model predictive controllers are designed and implemented. One controller is based on an error dynamics model, formulated as a regulation problem and implemented with the open source NMPC-library GRAMPC. The second controller is based on a kinematic model, formulated as a reference tracking NMPC problem and implemented using the embedded-MPC software tool FORCESPRO. The controllers are simulated on the same hardware that is used in real load-haul-dump vehicles, in a simulation environment provided by Epiroc Rock Drills AB. The results from the simulations show that both controllers can successfully follow a path, with a similar level of path error and less aggressive control actions compared to the current path following controller. The implemented controllers perform the control computations within a range of milliseconds on the embedded hardware, which is fast enough for real-time operation of the load-haul-dump vehicle.