Active vibration control techniques for flexible space structures

Two proposed control system design techniques for active vibration control in flexible space structures are detailed. Control issues relevant only to flexible-body dynamics are addressed, whereas no attempt was made to integrate the flexible and rigid-body spacecraft dynamics. Both of the proposed approaches revealed encouraging results; however, further investigation of the interaction of the flexible and rigid-body dynamics is warranted

Model based fault diagnosis and prognosis of class of linear and nonlinear distributed parameter systems modeled by partial differential equations

With the rapid development of modern control systems, a significant number of industrial systems may suffer from component failures. An accurate yet faster fault prognosis and resilience can improve system availability and reduce unscheduled downtime. Therefore, in this dissertation, model-based prognosis and resilience control schemes have been developed for online prediction and accommodation of faults for distributed parameter systems (DPS). First, a novel fault detection, estimation and prediction framework is introduced utilizing a novel observer for a class of linear DPS with bounded disturbance by modeling the DPS as a set of partial differential equations. To relax the state measurability in DPS, filters are introduced to redesign the detection observer. Upon detecting a fault, an adaptive term is activated to estimate the multiplicative fault and a tuning law is derived to tune the fault parameter magnitude. Then based on this estimated fault parameter together with its failure limit, time-to-failure (TTF) is derived for prognosis. A novel fault accommodation scheme is developed to handle actuator and sensor faults with boundary measurements. Next, a fault isolation scheme is presented to differentiate actuator, sensor and state faults with a limited number of measurements for a class of linear and nonlinear DPS. Subsequently, actuator and sensor fault detection and prediction for a class of nonlinear DPS are considered with bounded disturbance by using a Luenberger observer. Finally, a novel resilient control scheme is proposed for nonlinear DPS once an actuator fault is detected by using an additional boundary measurement. In all the above methods, Lyapunov analysis is utilized to show the boundedness of the closed-loop signals during fault detection, prediction and resilience under mild assumptions --Abstract, page iv

DECENTRALIZED ROBUST NONLINEAR MODEL PREDICTIVE CONTROLLER FOR UNMANNED AERIAL SYSTEMS

The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1 A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2 A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3 An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4 A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible

Fault Diagnosis and Performance Recovery Based on the Dynamic Safety Margin

The complexity of modern industrial processes makes high dependability an essential demand for reducing production loss, avoiding equipment damage, and increasing human safety. A more dependable system is a system that has the ability to: 1) detect faults as fast as possible; 2) diagnose them accurately; 3) recover the system to the nominal performance as much as possible. Therefore, a robust Fault Detection and Isolation (FDI) and a Fault Tolerant Control (FTC) system design have attained increased attention during the last decades. This thesis focuses on the design of a robust model-based FDI system and a performance recovery controller based on a new performance index called Dynamic Safety Margin (DSM). The DSM index is used to measure the distance between a predefined safety boundary in the state space and the system state trajectory as it evolves. The DSM concept, its computation methods, and its relationship to the state constraints are addressed. The DSM can be used in different control system applications; some of them are highlighted in this work. Controller design based on DSM is especially useful for safety-critical systems to maintain a predefined margin of safety during the transient and in the presence of large disturbances. As a result, the application of DSM to controller design and adaptation is discussed in particular for model predictive control (MPC) and PID controller. Moreover, an FDI scheme based on the analysis of the DSM is proposed. Since it is difficult to isolate different types of faults using a single model, a multi-model approach is employed in this FDI scheme. The proposed FDI scheme is not restricted to a special type of fault. In some faulty situations, recovering the system performance to the nominal one cannot be fulfilled. As a result, reducing the output performance is necessary in order to increase the system availability. A framework of FTC system is proposed that combines the proposed FDI and the controllers design based on DSM, in particular MPC, with accepted degraded performance in order to generate a reliable FTC system. The DSM concept and its applications are illustrated using simulation examples. Finally, these applications are implemented in real-time for an experimental two-tank system. The results demonstrate the fruitfulness of the introduced approaches

Human centric situational awareness

Context awareness is an approach that has been receiving increasing focus in the past years. A context aware device can understand surrounding conditions and adapt its behavior accordingly to meet user demands. Mobile handheld devices offer a motivating platform for context aware applications as a result of their rapidly growing set of features and sensing abilities. This research aims at building a situational awareness model that utilizes multimodal sensor data provided through the various sensing capabilities available on a wide range of current handheld smart phones. The model will make use of seven different virtual and physical sensors commonly available on mobile devices, to gather a large set of parameters that identify the occurrence of a situation for one of five predefined context scenarios: In meeting, Driving, in party, In Theatre and Sleeping. As means of gathering the wisdom of the crowd and in an effort to reach a habitat sensitive awareness model, a survey was conducted to understand the user perception of each context situation. The data collected was used to build the inference engine of a prototype context awareness system utilizing context weights introduced in [39] and the confidence metric in [26] with some variation as a means for reasoning. The developed prototype\u27s results were benchmarked against two existing context awareness platforms Darwin Phones [17] and Smart Profile [11], where the prototype was able to acquire 5% and 7.6% higher accuracy levels than the two systems respectively while performing tasks of higher complexity. The detailed results and evaluation are highlighted further in section 6.4

Robust model predictive control for linear systems subject to norm-bounded model Uncertainties and Disturbances: An Implementation to industrial directional drilling system

Model Predictive Control (MPC) refers to a class of receding horizon algorithms in which the current control action is computed by solving online, at each sampling instant, a constrained optimization problem. MPC has been widely implemented within the industry, due to its ability to deal with multivariable processes and to explicitly consider any physical constraints within the optimal control problem in a straightforward manner. However, the presence of uncertainty, whether in the form of additive disturbances, state estimation error or plant-model mismatch, and the robust constraints satisfaction and stability, remain an active area of research. The family of predictive control algorithms, which explicitly take account of process uncertainties/disturbances whilst guaranteeing robust constraint satisfaction and performance is referred to as Robust MPC (RMPC) schemes. In this thesis, RMPC algorithms based on Linear Matrix Inequality (LMI) optimization are investigated, with the overall aim of improving robustness and control performance, while maintaining conservativeness and computation burden at low levels. Typically, the constrained RMPC problem with state-feedback parameterizations is nonlinear (and nonconvex) with a prohibitively high computational burden for online implementation. To remedy this issue, a novel approach is proposed to linearize the state-feedback RMPC problem, with minimal conservatism, through the use of semidefinite relaxation techniques and the Elimination Lemma. The proposed algorithm computes the state-feedback gain and perturbation online by solving an LMI optimization that, in comparison to other schemes in the literature is shown to have a substantially reduced computational burden without adversely affecting the tracking performance of the controller. In the case that only (noisy) output measurements are available, an output-feedback RMPC algorithm is also derived for norm-bounded uncertain systems. The novelty lies in the fact that, instead of using an offline estimation scheme or a fixed linear observer, the past input/output data is used within a Robust Moving Horizon Estimation (RMHE) scheme to compute (tight) bounds on the current state. These current state bounds are then used within the RMPC control algorithm. To reduce conservatism, the output-feedback control gain and control perturbation are both explicitly considered as decision variables in the online LMI optimization. Finally, the aforementioned robust control strategies are applied in an industrial directional drilling configuration and their performance is illustrated by simulations. A rotary steerable system (RSS) is a drilling technology that has been extensively studied over the last 20 years in hydrocarbon exploration and is used to drill complex curved borehole trajectories. RSSs are commonly treated as dynamic robotic actuator systems, driven by a reference signal and typically controlled by using a feedback loop control law. However, due to spatial delays, parametric uncertainties, and the presence of disturbances in such an unpredictable working environment, designing such control laws is not a straightforward process. Furthermore, due to their inherent delayed feedback, described by delay differential equations (DDE), directional drilling systems have the potential to become unstable given the requisite conditions. To address this problem, a simplified model described by ordinary differential equations (ODE) is first proposed, and then taking into account disturbances and system uncertainties that arise from design approximations, the proposed RMPC algorithm is used to automate the directional drilling system.Open Acces

Observer-Based Nonlinear Dynamic Inversion Adaptive Control with State Constraints

Hypersonic vehicle research and development has grown recently in the aerospace industry due to the powerful potential of operating a vehicle that flies at substantially higher speeds than typical aircraft. From a guidance, navigation and control perspective, hypersonic vehicles are particularly interesting due both to inherent vehicle complexities as well as practical concerns that only arise at high Mach numbers. Challenges inherent to the vehicle include nonlinearities, a wide range of operating conditions, high elasticity, high temperatures and parametric uncertainty. Although these challenges have by no means fully been explored in the literature, in the realm of control theory, they are somewhat common. Hypersonic vehicle control is difficult however, because in addition to these more traditional complexities a control designer must also deal with problems very specific to flying at high speeds such as: inlet unstart, overcoming sensing deficiencies at high speeds and creating an implementable digital control framework for a plant with extremely fast dynamics. This dissertation develops three novel theoretical approaches for addressing these challenges through advances in the nonlinear dynamic inversion adaptive control technique. Although hypersonic vehicle control is the motivation and often the application that the control algorithms in this dissertation are tested on, several of the theoretical developments apply to a general class of nonlinear continuous time systems. First, in order to address the problem of inlet unstart, two state constraint mechanisms which integrate into the nonlinear dynamic inversion adaptive control framework are presented. These state constraining control laws require full state feedback and are capable of restricting the outputs of nonlinear systems containing parameter uncertainty to specific regions of the state-space. The first state constraint mechanism achieves this objective using sliding mode control and the second uses bounding functions to smoothly adjust the control and adaptive laws and drive the states toward the origin when constraints are approached. Stability is proven using Lyapunov analysis and these techniques are demonstrated in a nonlinear simulation of a hypersonic vehicle. Second, an observer-based feedback controller is developed that allows for a nonlinear system to track a reference trajectory with bounded errors and without measuring multiple states. Again, the technique used is nonlinear dynamic inversion adaptive control, but because of uncertainty in the system state, it is not assumed that the nonlinear control effectiveness matrix can be canceled perfectly. A nonlinear observer is implemented to estimate the values of the unknown states. This observer allows for the closed-loop stability of the system to be proven through Lyapunov analysis. It is shown that parametric uncertainty can successfully be accounted for using an adaptive mechanism and that all tracking and estimation errors are uniformly ultimately bounded. Finally, a sampled-data nonlinear dynamic inversion adaptive control architecture is introduced. Despite the prevalence of digital controllers in practice, a nonlinear dynamic inversion adaptive control scheme in a sampled-data setting has not previously been developed. The method presented in this dissertation has the capability of extending the benefits of nonlinear dynamic inversion adaptive control - robust control of nonlinear systems with respect to model uncertainty - to more practical platforms