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An increasing number of applications ranging from multi-vehicle systems, large-scale process control systems, transportation systems to smart grids call for the development of cooperative control theory. Meanwhile, when designing the cooperative controller, the state and control constraints, ubiquitously existing in the physical system, have to be respected. Model predictive control (MPC) is one of a few techniques that can explicitly and systematically handle the state and control constraints. This dissertation studies the robust MPC and distributed MPC strategies, respectively. Specifically, the problems we investigate are: the robust MPC for linear or nonlinear systems, distributed MPC for constrained decoupled systems and distributed MPC for constrained nonlinear systems with coupled system dynamics. In the robust MPC controller design, three sub-problems are considered. Firstly, a computationally efficient multi-stage suboptimal MPC strategy is designed by exploiting the j-step admissible sets, where the j-step admissible set is the set of system states that can be steered to the maximum positively invariant set in j control steps. Secondly, for nonlinear systems with control constraints and external disturbances, a novel robust constrained MPC strategy is designed, where the cost function is in a non-squared form. Sufficient conditions for the recursive feasibility and robust stability are established, respectively. Finally, by exploiting the contracting dynamics of a certain type of nonlinear systems, a less conservative robust constrained MPC method is designed. Compared to robust MPC strategies based on Lipschitz continuity, the strategy employed has the following advantages: 1) it can tolerate larger disturbances; and 2) it is feasible for a larger prediction horizon and enlarges the feasible region accordingly. For the distributed MPC of constrained continuous-time nonlinear decoupled systems, the cooperation among each subsystems is realized by incorporating a coupling term in the cost function. To handle the effect of the disturbances, a robust control strategy is designed based on the two-layer invariant set. Provided that the initial state is feasible and the disturbance is bounded by a certain level, the recursive feasibility of the optimization is guaranteed by appropriately tuning the design parameters. Sufficient conditions are given ensuring that the states of each subsystem converge to the robust positively invariant set. Furthermore, a conceptually less conservative algorithm is proposed by exploiting the controllability set instead of the positively invariant set, which allows the adoption of a shorter prediction horizon and tolerates a larger disturbance level. For the distributed MPC of a large-scale system that consists of several dynamically coupled nonlinear systems with decoupled control constraints and disturbances, the dynamic couplings and the disturbances are accommodated through imposing new robustness constraints in the local optimizations. Relationships among, and design procedures for the parameters involved in the proposed distributed MPC are derived to guarantee the recursive feasibility and the robust stability of the overall system. It is shown that, for a given bound on the disturbances, the recursive feasibility is guaranteed if the sampling interval is properly chosen. / Graduate / 0548 / 0544 / 0546 / liuxiaotao1982@gmail.com

In line with Anglo American’s asset optimization and environmental policies, the coal burning flash dryer operations at its smelters have been identified for potential optimization by means of advanced process control. For this project, the process and related literature were studied in detail and a revised control philosophy, which includes modifications to the existing basic control structure as well as a hybrid rule and model-predictive advanced control layer, was developed, installed and tested on one of these flash drying operations. Since commissioning of the APC, the flash dryer’s average throughput has increased by more than 6 %, despite higher feed moistures. Furthermore, even though coal consumption has increased slightly, the operation efficiency has improved by almost 5 %. This was made possible by improving the stability of the drying column outlet temperature by approximately 40 %, which in turn enabled the selection of a more optimal setpoint. Recent data has shown that APC utilization now exceeds 95 %. This is indicative of a successful controller installation with good site acceptance. / Dissertation (MEng)--University of Pretoria, 2011. / Chemical Engineering / unrestricted

This dissertation forms part of the ongoing process at UP to model and control the electric arc furniture process. Previous work focused on modelling the furnace process from empirical thermodynamic principles as well as fitting the model to actual plant data. Automation of the process mainly focused on subsystems of the process, for example the electric subsystem and the off-gas subsystem. The modelling effort, especially the model fitting resulted in parameter values that are described with confidence intervals, which gives rise to uncertainty in the model, because the parameters can potentially lie anywhere in the confidence interval space. Robust model predictive control is used in this dissertation, because it can explicityly take the model uncertainty into account as part of the synthesis process. Nominal model predictive control – not taking model uncertainty into account – is also applied in order to determine if robust model predictive control provides any advantages over the nominal model predictive control. This dissertation uses the process model from previous wok together with robust model predictive control to determine the feasibility of automating the process with regards to the primary process variables. Possible hurdles that prevent practical implementation are identified and studied. / Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / MEng / unrestricted

This dissertation forms part of the ongoing process at UP to model and control the electric arc furniture process. Previous work focused on modelling the furnace process from empirical thermodynamic principles as well as fitting the model to actual plant data. Automation of the process mainly focused on subsystems of the process, for example the electric subsystem and the off-gas subsystem. The modelling effort, especially the model fitting resulted in parameter values that are described with confidence intervals, which gives rise to uncertainty in the model, because the parameters can potentially lie anywhere in the confidence interval space. Robust model predictive control is used in this dissertation, because it can explicityly take the model uncertainty into account as part of the synthesis process. Nominal model predictive control – not taking model uncertainty into account – is also applied in order to determine if robust model predictive control provides any advantages over the nominal model predictive control. This dissertation uses the process model from previous wok together with robust model predictive control to determine the feasibility of automating the process with regards to the primary process variables. Possible hurdles that prevent practical implementation are identified and studied. / Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / MEng / unrestricted

Please read the abstract in the section, 00front of this document / Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / MEng / unrestricted

Variable Horizon Model Predictive Control (VH-MPC) is a form of predictive control that includes the horizon length as a decision variable in the constrained optimisation problem solved at each iteration. It has been recently applied to completion problems, where the system state is to be steered to a closed set in finite time. The behaviour of the system once completion has occurred is not considered part of the control problem. This thesis is concerned with three aspects of robustness and optimality in VH-MPC completion problems. In particular, the thesis investigates robustness to well defined but unpredictable changes in system and controller parameters, robustness to bounded disturbances in the presence of certain input parameterisations to reduce computational complexity, and optimal robustness to bounded disturbances using tightened constraints. In the context of linear time invariant systems, new theoretical contributions and algorithms are developed. Firstly, changing dynamics, constraints and control objectives are addressed by introducing the notion of feasible contingencies. A novel algorithm is proposed that introduces extra prediction variables to ensure that anticipated new control objectives are always feasible, under changed system parameters. In addition, a modified constraint tightening formulation is introduced to provide robust completion in the presence of bounded disturbances. Different contingency scenarios are presented and numerical simulations demonstrate the formulation’s efficacy. Next, complexity reduction is considered, using a form of input parameterisation known as move blocking. After introducing a new notation for move blocking, algorithms are presented for designing a move-blocked VH-MPC controller. Constraints are tightened in a novel way for robustness, whilst ensuring that guarantees of recursive feasibility and finite-time completion are preserved. Simulations are used to illustrate the effect of an example blocking scheme on computation time, closed-loop cost, control inputs and state trajectories. Attention is now turned towards mitigating the effect of constraint tightening policies on a VH-MPC controller’s region of attraction. An optimisation problem is formulated to maximise the volume of an inner approximation to the region of attraction, parameterised in terms of the tightening policy. Alternative heuristic approaches are also proposed to deal with high state dimensions. Numerical examples show that the new technique produces substantially improved regions of attraction in comparison to other proposed approaches, and greatly reduces the maximum required prediction horizon length for a given application. Finally, a case study is presented to illustrate the application of the new theory developed in this thesis to a non-trivial example system. A simplified nonlinear surface excavation machine and material model is developed for this purpose. The model is stabilised with an inner-loop controller, following which a VH-MPC controller for autonomous trajectory generation is designed using a discretised, linearised model of the stabilised system. Realistic simulated trajectories are obtained from applying the controller to the stabilised system and incorporating the ideas developed in this thesis. These ideas improve the applicability and computational tractability of VH-MPC, for both traditional applications as well as those that go beyond the realm of vehicle manœuvring.

With numerous distinct advantages, quadrotors have found a wide range of applications, such as structural inspection, traffic control, search and rescue, agricultural surveillance, etc. To better serve applications in cluttered environment, quadrotors are further equipped with vision sensors to enhance their state sensing and environment perception capabilities. Moreover, visual information can also be used to guide the motion control of the quadrotor. This is referred to as visual servoing of quadrotor. In this thesis, we identify the challenging problems arising in the area of visual servoing of the quadrotor and propose effective control strategies to address these issues. The control objective considered in this thesis is to regulate the relative pose of the quadrotor to a ground target using a limited number of sensors, e.g., a monocular camera and an inertia measurement unit. The camera is attached underneath the center of the quadrotor and facing down. The ground target is a planar object consisting of multiple points. The image features are selected as image moments defined in a ``virtual image plane". These image features offer an image kinematics that is independent of the tilt motion of the quadrotor. This independence enables the separation of the high level visual servoing controller design from the low level attitude tracking control. A high-gain observer-based model predictive control (MPC) scheme is proposed in this thesis to address the image-based visual servoing of the quadrotor. The high-gain observer is designed to estimate the linear velocity of the quadrotor which is part of the system states. Due to a limited number of sensors on board, the linear velocity information is not directly measurable. The high-gain observer provides the estimates of the linear velocity and delivers them to the model predictive controller. On the other hand, the model predictive controller generates the desired thrust force and yaw rate to regulate the pose of the quadrotor relative to the ground target. By using the MPC controller, the tilt motion of the quadrotor can be effectively bounded so that the scene of the ground target is well maintained in the field of view of the camera. This requirement is referred to as visibility constraint. The satisfaction of visibility constraint is a prerequisite of visual servoing of the quadrotor. Simulation and experimental studies are performed to verify the effectiveness of the proposed control strategies. Moreover, image processing algorithms are developed to extract the image features from the captured images, as required by the experimental implementation. / Graduate / 2020-12-11

Cyber-physical systems (CPS), integrating advanced computation, communication, and control technologies with the physical process, are widely applied in industry applications such as smart production and manufacturing systems, robotic and automotive control systems, and smart grids. Due to possible exposure to unreliable networks and complex physical environments, CPSs may simultaneously face multiple cyber and physical issues including cyber threats (e.g., malicious cyber attacks) and resource constraints (e.g., limited networking resources and physical constraints). As one of the essential topics in designing efficient CPSs, the controller design for CPSs, aiming to achieve secure and resource-aware control objectives under such cyber and physical issues, is very significant yet challenging. Emphasizing optimality and system constraint handling, model predictive control (MPC) is one of the most widely used control paradigms, notably famous for its successful applications in chemical process industry. However, the conventional MPC methods are not specifically tailored to tackle cyber threats and resource constraints, thus the corresponding theory and tools to design the secure and resource-aware controller are lacking and need to be developed. This dissertation focuses on developing MPC-based methodologies to address the i) secure control problem and ii) resource-aware control problem for CPSs subject to cyber threats and resource constraints. In the resource-aware control problem of CPSs, the nonlinear system with additive disturbance is considered. By using an integral-type event-triggered mechanism and an improved robustness constraint, we propose an integral-type event-triggered MPC so that smaller sampling frequency and robustness to the additive disturbance can be obtained. The sufficient conditions for guaranteeing the recursive feasibility and the closed-loop stability are established. For the secure control problem of CPSs, two aspects are considered. Firstly, to achieve the secure control objective, we design a secure dual-mode MPC framework, including a modified initial feasible set and a new positively invariant set, for constrained linear systems subject to Denial-of-Service (DoS) attacks. The exponential stability of the closed-loop system is guaranteed under several conditions. Secondly, to deal with cyber threats and take advantage of the cloud-edge computing technology, we propose a model predictive control as a secure service (MPCaaSS) framework, consisting of a double-layer controller architecture and a secure data transmission protocol, for constrained linear systems in the presence of both cyber threats and external disturbances. The rigorous recursive feasibility and robust stability conditions are established. To simultaneously address the secure and resource-aware control problems, an event-triggered robust nonlinear MPC framework is proposed, where a new robustness constraint is introduced to deal with additive disturbances, and a packet transmission strategy is designed to tackle DoS attacks. Then, an event-triggered mechanism, which accommodates DoS attacks occurring in the communication network, is proposed to reduce the communication cost for resource-constrained CPSs. The recursive feasibility and the closed-loop stability in the sense of input-to-state practical stable (ISpS) are guaranteed under the established sufficient conditions. / Graduate

In the study of multi-agent systems (MASs), cooperative control is one of the most fundamental issues. As it covers a broad spectrum of applications in many industrial areas, there is a desire to design cooperative control protocols for different system and network setups. Motivated by this fact, in this thesis we focus on elaborating consensus protocol design, via model predictive control (MPC), under two different scenarios: (1) general constrained linear MASs with bounded additive disturbance; (2) linear MASs with input constraints underlying distributed communication networks. In Chapter 2, a tube-based robust MPC consensus protocol for constrained linear MASs is proposed. For undisturbed linear MASs without constraints, the results on designing a centralized linear consensus protocol are first developed by a suboptimal linear quadratic approach. In order to evaluate the control performance of the suboptimal consensus protocol, we use an infinite horizon linear quadratic objective function to penalize the disagreement among agents and the size of control inputs. Due to the non-convexity of the performance function, an optimal controller gain is difficult or even impossible to find, thus a suboptimal consensus protocol is derived. In the presence of disturbance, the original MASs may not maintain certain properties such as stability and cooperative performance. To this end, a tube-based robust MPC framework is introduced. When disturbance is involved, the original constraints in nominal prediction should be tightened so as to achieve robust constraint satisfaction, as the predicted states and the actual states are not necessarily the same. Moreover, the corresponding robust constraint sets can be determined offline, requiring no extra iterative online computation in implementation. In Chapter 3, a novel distributed MPC-based consensus protocol is proposed for general linear MASs with input constraints. For the linear MAS without constraints, a pre-stabilizing distributed linear consensus protocol is developed by an inverse optimal approach, such that the corresponding closed-loop system is asymptotically stable with respect to a consensus set. Implementing this pre-stabilizing controller in a distributed digital setting is however not possible, as it requires every local decision maker to continuously access the state of their neighbors simultaneously when updating the control input. To relax these requirements, the assumed neighboring state, instead of the actual state of neighbors, is used. In our distributed MPC scheme, each local controller minimizes a group of control variables to generate control input. Moreover, an additional state constraint is proposed to bound deviation between the actual and the assumed state. In this way, consistency is enforced between intended behaviors of an agent and what its neighbors believe it will behave. We later show that the closed-loop system converges to a neighboring set of the consensus set thanks to the bounded state deviation in prediction. In Chapter 4, conclusions are made and some research topics for future exploring are presented. / Graduate / 2021-03-31