A guidance, navigation, and control (GN&C) module is being designed and fabricated as part of a series of CubeSats being built by the Satellite Design Laboratory at the University of Texas. A spacecraft attitude control simulation environment called StarBox was created in order to perform trade studies and conduct performance analysis for the GN&C module. Navigation and control algorithms were tested using StarBox and then implemented onto an embedded flight computer. These algorithms were then tested in a hardware-in-the-loop simulation. In addition, the feasibility of utilizing advanced constrained attitude control algorithms was investigated by focusing on implementation in flight software. A mechanical and electrical design for the GN&C module was completed. A prototype system was set up on a bench-top for integrated testing. The analysis indicates that the system will satisfy the requirements of several CubeSat missions, including the current missions at the University of Texas known as Bevo2 and ARMADILLO. / text

This dissertation introduces the Explicit Reference Governor: a simple and systematic add-on control unit that provides constraint handling capabilities to any pre-stabilized nonlinear system by suitably manipulating its applied reference. The main innovation of the proposed framework is that constraint satisfaction is ensured without having to solve implicit equations. As a result, the Explicit Reference Governor is particularly well suited for applications with limited computational capabilities. The basic idea behind the scheme consists in manipulating the derivative of the applied reference so that, at any given time instant, the currently applied reference will not cause a violation of constraints anytime in the future. The theory behind the proposed framework is presented in general terms and is then detailed to provide specific design strategies. Possible extensions to ensure robustness are also proposed. In addition to introducing the general theory of the Explicit Reference Governor, the dissertation illustrates its step-by-step implementation on Unmanned Aerial Vehicles. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Research Doctorate - Doctor of Philosophy (PhD) / This thesis addresses the problem of robustness in model predictive control (MPC) of discrete-time systems. In contrast with most previous work on robust MPC, our main focus is on robustness in the face of both imperfect state information and dynamic model uncertainty. For linear discrete-time systems with model uncertainty described by sum quadratic constraints, we propose output-feedback MPC policies that: (i) treat soft constraints using quadratic penalty functions; (ii) respect hard constraints using 'tighter' constraints; and (iii) achieve robust closed-loop stability and non-zero setpoint tracking. Our two main tools are: (1) a new linear matrix inequality condition which parameterizes a class of quadratic MPC cost functions that all lead to robust closed-loop stability; and (2) a new parameterization of soft constraints which has the advantage of leading to optimization problems of prescribable size. The stability test we use for MPC design builds on well-known results from dissipativity theory which we tailor to the case of constrained discrete-time systems. The proposed robust MPC designs are shown to converge to well-known nominal MPC designs as the model uncertainty (description) goes to zero. Furthermore, the present approach to cost function selection is independently motivated by a novel result linking MPC and minimax optimal control theory. Specifically, we show that the considered class of MPC policies are the closed-loop optimal solutions of a particular class of minimax optimal control problems. In addition, for a class of nonlinear discrete-time systems with constraints and bounded disturbance inputs, we propose state-feedback MPC policies that input-to-state stabilize the system. Our two main tools in this last part of the thesis are: (1) a class of N-step affine state-feedback policies; and (2) a result that establishes equivalence between the latter class and an associated class of N-step affine disturbance-feedback policies. Our equivalence result generalizes a recent result in the literature for linear systems to the case when N is chosen to be less than the nonlinear system's 'input-state linear horizon'.

Most practical control problems must deal with constraints imposed by equipment limitations, safety considerations or environmental regulations. While it is often beneficial to maintain operation close to the limits in order to maximize profit or meet stringent product specifications, the violation of actuator constraints during normal operation can result in serious performance degradation (sometimes instability) and economic losses. This thesis is concerned with the development of control strategies for multivariable systems which systematically account for actuator constraints while guaranteeing closed-loop stability as well as graceful degradation of non-linear performance. A novel anti-windup structure is proposed which combines the efficiency of conventional anti-windup schemes with the optimality of model predictive control (MPC) algorithms. In particular, the classical internal model control (IMC) law is enhanced for optimal performance by incorporating an on-line optimization. The resulting control scheme offers both stability and performance guarantees with moderate computational expense. The proposed optimizing scheme has prospects for industrial applications as it can be implemented easily and efficiently on programmable logic controllers (PLC).

Un problème fondamental à résoudre en Automatique réside dans la commande des systèmes incertains qui présentent des contraintes sur les variables de l’entrée, de l’état ou la sortie. Ce problème peut être théoriquement résolu au moyen d’une commande optimale. Cependant la commande optimale par principe n’est pas une commande par retour d’état ou retour de sortie et offre seulement une trajectoire optimale le plus souvent par le biais d’une solution numérique.Par conséquent, dans la pratique, le problème peut être approché par de nombreuses méthodes, tels que”commande over-ride” et ”anti-windup”. Une autre solution, devenu populaire au cours des dernières décennies est la commande prédictive. Selon cette méthode, un problème de la commande optimale est résolu à chaque instant d’échantillonnage, et le composant du vecteur de commande destiné à l’échelon curant est appliquée. En dépit de la montée en puissance des architecture de calcul temps-réel, la commande prédictive est à l’heure actuelle principalement approprié lorsque l’ordre est faible, bien connu, et souvent pour des systèmes linéaires. La version robuste de la commande prédictive est conservatrice et compliquée à mettre en œuvre, tandis que la version explicite de la commande prédictive donnant une solution affine par morceaux implique une compartimentation de l’état-espace en cellules polyédrales, très compliquée.Dans cette thèse, une solution élégante et peu coûteuse en temps de calcul est présentée pour des systèmes linéaire, variant dans le temps ou incertains. Les développements se concentre sur les dynamiques en temps discret avec contraintes polyédriques sur l’entrée et l’état (ou la sortie) des vecteurs, dont les perturbations sont bornées. Cette solution est basée sur l’interpolation entre un correcteur pour la région extérieure qui respecte les contraintes sur l’entrée et de l’état, et un autre pour la région intérieure, ce dernier plus agressif, conçue par n’importe quelle méthode classique, ayant un ensemble robuste positivement invariant associé à l’intérieur des contraintes. Une simple fonction de Lyapunov est utilisée afin d’apporter la preuve de la stabilité en boucle fermée. / A fundamental problem in automatic control is the control of uncertain plants in the presence of input and state or output constraints. An elegant and theoretically most satisfying framework is represented by optimal control policies which, however, rarely gives an analytical feedback solution, and oftentimes builds on numerical solutions (approximations).Therefore, in practice, the problem has seen many ad-hoc solutions, such as override control, anti-windup, as well as modern techniques developed during the last decades usually based on state space models. One of the popular example is Model Predictive Control (MPC) where an optimal control problem is solved at each sampling instant, and the element of the control vector meant for the nearest sampling interval is applied. In spite of the increased computational power of control computers, MPC is at present mainly suitable for low-order, nominally linear systems. The robust version of MPC is conservative and computationally complicated, while the explicit version of MPC that gives an affine state feedback solution involves a very complicated division of the state space into polyhedral cells.In this thesis a novel and computationally cheap solution is presented for linear, time-varying or uncertain, discrete-time systems with polytopic bounded control and state (or output) vectors, with bounded disturbances. The approach is based on the interpolation between a stabilizing, outer controller that respects the control and state constraints, and an inner, more aggressive controller, designed by any method that has a robustly positively invariant set within the constraints. A simple Lyapunov function is used for the proof of closed loop stability.In contrast to MPC, the new interpolation based controller is not necessarily employing an optimization criterion inspired by performance. In its explicit form, the cell partitioning is simpler that the MPC counterpart. For the implicit version, the on-line computational demand can be restricted to the solution of one linear program or quadratic program. Several simulation examples are given, including uncertain linear systems with output feedback and disturbances. Some examples are compared with MPC. The control of a laboratory ball-and-plate system is also demonstrated. It is believed that the new controller might see wide-spread use in industry, including the automotive industry, also for the control of fast, high-order systems with constraints.

<p>The main contributions in this thesis are advances in parametric programming. The thesis is divided into three parts; theoretical advances, application areas and constrained control allocation. The first part deals with continuity properties and the structure of solutions to convex parametric quadratic and linear programs. The second part focuses on applications of parametric quadratic and linear programming in control theory. The third part deals with constrained control allocation and how parametric programming can be used to obtain explicit solutions to this problem.</p>

The main contributions in this thesis are advances in parametric programming. The thesis is divided into three parts; theoretical advances, application areas and constrained control allocation. The first part deals with continuity properties and the structure of solutions to convex parametric quadratic and linear programs. The second part focuses on applications of parametric quadratic and linear programming in control theory. The third part deals with constrained control allocation and how parametric programming can be used to obtain explicit solutions to this problem.

Complex helicopter models that include effects typically ignored in control models, such as an analytical formulation for fuselage aerodynamics, blade lead-lagging and flexibility, and tail rotor aerodynamics, are derived. The landing gear, horizontal tailplane, a fully articulated main rotor, main rotor downwash, and blade flapping are also modeled. The modeling process is motivated by the desire to build control oriented, physics based models that directly result in ordinary differential equations (ODE) models which are sufficiently rich in dynamics information. A physics based model simplification procedure, which is called new ordering scheme, is developed to reduce the number of terms in these large nonlinear ODE models, while retaining the same number of governing equations of motion. The resulting equations are trimmed and linearized around several flight conditions (i.e. straight level flight, level banked turn, and helical turn) using Maple and Matlab. The resulting trims and model modes are validated against available literature data. The linearized models are first used for the design of variance constrained controllers with inequality constraints on outputs or inputs, output variance constrained controllers (OVC) and input variance constrained controllers (IVC), respectively. The linearized helicopter models are also used for the design of online controllers which exploit the constrained model predictive control (MPC) theory. The ability of MPC to track highly constrained, heterogeneous discontinuous trajectories is examined. The performance and robustness of all these controllers (e.g. OVC, IVC, MPC) are thoroughly investigated with respect to several modeling uncertainties. Specifically, for robustness studies, variations in the flight conditions and helicopter inertial properties, as well as blade flexibility effects, are considered. Furthermore, the effectiveness of adaptive switching between controllers for the management of sensor failure during helicopter operations is studied using variance constrained controllers. Finally, the simultaneous design of the helicopter and control system is examined using simultaneous perturbation stochastic approximation in order to save active control energy. / Ph. D.

In this paper we examine the stabilization of LTI discrete-time systems with control input constraints in the form of saturation nonlinearities. This kind of constraint is usually introduced to simulate the effect of actuator limitations. Since global controllability can not be assumed in the presence of constrained control, the controllable regions and their characterizations are analyzed first. We present an efficient algorithm for finding controllable regions in terms of their boundary hyperplanes (inequality constraints). A previously open question about the exact number of irredundant boundary hyperplanes is also resolved here. The main result of this research is a time-optimal nonlinear controller which stabilizes the system on its controllable region. We give analgorithm for on-line computation of control which is also implementable for high-order systems. Simulation results show superior response even in the presence of disturbances.

In this paper we examine the stabilization of LTI discrete-time systems with control input constraints in the form of saturation nonlinearities. This kind of constraint is usually introduced to simulate the effect of actuator limitations. Since global controllability can not be assumed in the presence of constrained control, the controllable regions and their characterizations are analyzed first. We present an efficient algorithm for finding controllable regions in terms of their boundary hyperplanes (inequality constraints). A previously open question about the exact number of irredundant boundary hyperplanes is also resolved here. The main result of this research is a time-optimal nonlinear controller which stabilizes the system on its controllable region. We give analgorithm for on-line computation of control which is also implementable for high-order systems. Simulation results show superior response even in the presence of disturbances.