This thesis presents a data-driven approach to constrained control in the form of a subspace-based state-space system identification algorithm integrated into a model predictive controller. Generally this approach has been termed model-free predictive control in the literature. Previous research into this area focused on the system identification aspects resulting in an omission of many of the features that would make such a control strategy attractive to industry. These features include constraint handling, zero-offset setpoint tracking and non-stationary disturbance rejection. The link between non-stationary disturbance rejection in subspace-based state-space system identification and integral action in state-space based model predictive control was shown. Parameterization with Laguerre orthonormal functions was proposed for the reduction in computational load of the controller. Simulation studies were performed using three real-world systems demonstrating: identification capabilities in the presence of white noise and non-stationary disturbances; unconstrained and constrained control; and the benefits and costs of parameterization with Laguerre polynomials.

Minimum invasive surgery is based on bilateral teleoperation in which surgeon interacts with the master side to the slave side that is located at a distance. The synchronization in between the two ends is through a communication channel. The primary objective in the telesurgery is the position and force tracking providing the surgeon with high fidelity. The presence of time delays in the communication channels makes the realization more difficult, and sometimes it may even destabilize the system. The work focuses on a design of the force control system by using Model Predictive Control to compensate for the effects of the time delay related to the use of surgical arms. Another vital issue of minimum impact velocity during contact with the environment has been tried to achieve by using the prediction from the Model Predictive Control to prevent accidental tissue damage. This work also addresses a problem of the developing a simple delayed free predictive kinematic imaging to understand the type of behavior of the system during contact with the environment when no perception is available.

Flight control design for modern fighter aircraft is a challenging task. Aircraft are dynamical systems, which naturally contain a variety of constraints and nonlinearities such as, e.g., maximum permissible load factor, angle of attack and control surface deflections. Taking these limitations into account in the design of control systems is becoming increasingly important as the performance and complexity of the aircraft is constantly increasing. The aeronautical industry has traditionally applied feedforward, anti-windup or similar techniques and different ad hoc engineering solutions to handle constraints on the aircraft. However these approaches often rely on engineering experience and insight rather than a theoretical foundation, and can often require a tremendous amount of time to tune. In this thesis we investigate model predictive control as an alternative design tool to handle the constraints that arises in the flight control design. We derive a simple reference tracking MPC algorithm for linear systems that build on the dual mode formulation with guaranteed stability and low complexity suitable for implementation in real time safety critical systems. To reduce the computational burden of nonlinear model predictive control we propose a method to handle the nonlinear constraints, using a set of dynamically generated local inner polytopic approximations. The main benefit of the proposed method is that while computationally cheap it still can guarantee recursive feasibility and convergence. An alternative to deriving MPC algorithms with guaranteed stability properties is to analyze the closed loop stability, post design. Here we focus on deriving a tool based on Mixed Integer Linear Programming for analysis of the closed loop stability and robust stability of linear systems controlled with MPC controllers. To test the performance of model predictive control for a real world example we design and implement a standard MPC controller in the development simulator for the JAS 39 Gripen aircraft at Saab Aeronautics. This part of the thesis focuses on practical and tuning aspects of designing MPC controllers for fighter aircraft. Finally we have compared the MPC design with an alternative approach to maneuver limiting using a command governor.

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

Stochastic model predictive control (MPC) is a fascinating field for research and of increasing practical importance since optimal control techniques have been intensively investigated in modern control system design. With the development of computer technologies and communication networks, networked control systems (NCSs) or cyber-physical systems (CPSs) have become an interest of research due to the comprehensive integration of physical systems, such as sensors, actuators and plants, with intricate cyber components, possessing information communication and computation. In CPSs, advantages of low installation cost, high reliability, flexible modularity, improved efficiency, and greater autonomy can be obtained by the tight coordination of physical and cyber components. Several sectors, including robotics, transportation, health care, smart buildings, and smart grid, have witnessed the successful application of CPSs design. The integration of extensive cyber capability and physical plants with ubiquitous uncertainties also introduces concerns over communication efficiency, robustness and stability of the CPSs. Thus, to achieve satisfactory performance metrics of efficiency, robustness and stability, a detailed investigation into control synthesis of CPSs under the stochastic model predictive control framework is of importance. The stochastic model predictive control synthesis plays a vital role in CPSs design since the multivariable stochastic system subject to probabilistic constraints can be controlled in an optimized way. On the other hand, aperiodically sampled, or event-based, model predictive control has also been applied to CPSs extensively to improve communication efficiency. In this thesis, the control synthesis and analysis of aperiodically sampled stochastic model predictive control for CPSs is considered. Chapter 1 provides an introductory literature review of the current development of stochastic MPC, distributed stochastic MPC and event-based MPC. Chapter 2 presents a stochastic self-triggered model predictive control scheme for linear systems with additive uncertainty and with the states and inputs being subject to chance constraints. In the proposed control scheme, the succeeding sampling time instant and current control inputs are computed online by solving a formulated optimization problem. Chapter 3 discusses a stochastic self-triggered model predictive control algorithm with an adaptive prediction horizon. The communication cost is explicitly considered by adding a damping factor in the cost function. Sufficient conditions are provided to guarantee closed-loop chance constraints satisfactions. Furthermore, the recursive feasibility of the algorithm is analyzed, and the closed-loop system is shown to be stable. Chapter 4 proposes a distributed self-triggered stochastic MPC control scheme for CPSs under coupled chance constraints and additive disturbances. Based on the assumptions on stochastic disturbances, both local and coupled probabilistic constraints are transformed into the deterministic form using the tube-based method, and improved terminal constraints are constructed to guarantee the recursive feasibility of the control scheme. Theoretical analysis has shown that the overall closed-loop CPSs are quadratically stable. Numerical examples illustrate the efficacy of the proposed control method in terms of data transmission reductions. Chapter 5 concludes the thesis and suggests some promising directions for future research. / Graduate / 2022-01-15