This dissertation investigates the problem of developing verifiable stable control architectures for gas turbine engines. First, a nonlinear physics-based dynamic model of a twin spool turboshaft engine which drives a variable pitch propeller is developed. In this model, the dynamics of the engine are defined to be the two spool speeds, and the two control inputs to the system are fuel flow rate and prop pitch angle. Experimental results are used to verify the dynamic model of JetCat SPT5 turboshaft engine. Based on the experimental data, performance maps of the engine components including propeller, high pressure compressor, high pressure, and low pressure turbines are constructed. The engine numerical model is implemented using Matlab. Second, a stable gain scheduled controller is described and developed for a gas turbine engine that drives a variable pitch propeller. A stability proof is developed for a gain scheduled closed-loop system using global linearization and linear matrix inequality (LMI) techniques. Using convex optimization tools, a single quadratic Lyapunov function is computed for multiple linearizations near equilibrium and non-equilibrium points of the nonlinear closed-loop system. This approach guarantees stability of the closed-loop gas turbine engine system. To verify the stability of the closed-loop system on-line, an optimization problem is proposed which is solvable using convex optimization tools. Through simulations, we show the developed gain scheduled controller is capable to regulate a turboshaft engine for large thrust commands in a stable fashion with proper tracking performance. Third, a gain scheduled model reference adaptive control (GS-MRAC) concept for multi-input multi-output (MIMO) nonlinear plants with constraints on the control inputs is developed and described. Specifically, adaptive state feedback for the output tracking control problem of MIMO nonlinear systems is studied. Gain scheduled reference model system is used for generating desired state trajectories, and the stability of this reference model is also analyzed using convex optimization tools. This approach guarantees stability of the closed-loop gain scheduled gas turbine engine system, which is used as a gain scheduled reference model. An adaptive state feedback control scheme is developed and its stability is proven, in addition to transient and steady-state performance guarantees. The resulting closed-loop system is shown to have ultimately bounded solutions with a priori adjustable bounded tracking error. The results are then extended to GS-MRAC with constraints on the magnitudes of multiple control inputs. Sufficient conditions for uniform boundedness of the closed-loop system is derived. A semi-global stability result is proven with respect to the level of saturation for open-loop unstable plants, while the stability result is shown to be global for open-loop stable plants. Simulations are performed for three different models of the turboshaft engine, including the nominal engine model and two models where the engine is degraded. Through simulations, we show the developed GS-MRAC architecture can be used for the tracking problem of degraded turboshaft engine for large thrust commands with guaranteed stability. Finally, a decentralized linear parameter dependent representation of the engine model is developed, suitable for decentralized control of the engine with core and fan/prop subsystems. Control theoretic concepts for decentralized gain scheduled model reference adaptive control (D-GS-MRAC) systems is developed. For each subsystem, a linear parameter dependent model is available and a common Lyapunov matrix can be computed using convex optimization tools. With this control architecture, the two subsystems of the engine (i.e., engine core and engine prop/fan) can be controlled with independent controllers for large throttle commands in a decentralized manner. Based on this D-GS-MRAC architecture, a "plug and play" (PnP) technology concept for gas turbine engine control systems is investigated, which allows us to match different engine cores with different engine fans/propellers. With this plug and play engine control architecture, engine cores and fans/props could be used with their on-board subordinate controllers ready for integration into a functional propulsion system. Simulation results for three different models of the engine, including the nominal engine model, the model with a new prop, and the model with a new engine core, illustrate the possibility of PnP technology development for gas turbine engine control systems.

Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named composite model reference adaptive control is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of pseudo-control hedging techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

Neural network-based adaptive output feedback approaches that augment a linear control design are described in this thesis, and emphasis is placed on their real-time implementation with flexible systems. Two different control architectures that are robust to parametric uncertainties and unmodelled dynamics are presented. The unmodelled effects can consist of minimum phase internal dynamics of the system together with external disturbance process. Within this context, adaptive compensation for external disturbances is addressed. In the first approach, internal model-following control, adaptive elements are designed using feedback inversion. The effect of an actuator limit is treated using control hedging, and the effect of other actuation nonlinearities, such as dead zone and backlash, is mitigated by a disturbance observer-based control design. The effectiveness of the approach is illustrated through simulation and experimental testing with a three-disk torsional system, which is subjected to control voltage limit and stiction. While the internal model-following control is limited to minimum phase systems, the second approach, external model-following control, does not involve feedback linearization and can be applied to non-minimum phase systems. The unstable zero dynamics are assumed to have been modelled in the design of the existing linear controller. The laboratory tests for this method include a three-disk torsional pendulum, an inverted pendulum, and a flexible-base robot manipulator. The external model-following control architecture is further extended in three ways. The first extension is an approach for control of multivariable nonlinear systems. The second extension is a decentralized adaptive control approach for large-scale interconnected systems. The third extension is to make use of an adaptive observer to augment a linear observer-based controller. In this extension, augmenting terms for the adaptive observer can be used to achieve adaptation in both the observer and the controller. Simulations to illustrate these approaches include an inverted pendulum with its cart serially attached to two carts (one unmodelled), three spring-coupled inverted pendulums, and an inverted pendulum with its initial condition in a range in which a linear controller is destabilizing.

In light of the complex and highly uncertain nature of dynamical systems requiring controls, it is not surprising that reliable system models for many high performance engineering and life science applications are unavailable. In the face of such high levels of system uncertainty, robust controllers may unnecessarily sacrifice system performance whereas adaptive controllers are clearly appropriate since they can tolerate far greater system uncertainty levels to improve system performance. In this dissertation, we develop a Lyapunov-based direct adaptive and neural adaptive control framework that addresses parametric uncertainty, unstructured uncertainty, disturbance rejection, amplitude and rate saturation constraints, and digital implementation issues. Specifically, we consider the following research topics: direct adaptive control for nonlinear uncertain systems with exogenous disturbances; robust adaptive control for nonlinear uncertain systems; adaptive control for nonlinear uncertain systems with actuator amplitude and rate saturation constraints; adaptive reduced-order dynamic compensation for nonlinear uncertain systems; direct adaptive control for nonlinear matrix second-order dynamical systems with state-dependent uncertainty; adaptive control for nonnegative and compartmental dynamical systems with applications to general anesthesia; direct adaptive control of nonnegative and compartmental dynamical systems with time delay; adaptive control for nonlinear nonnegative and compartmental dynamical systems with applications to clinical pharmacology; neural network adaptive control for nonlinear nonnegative dynamical systems; passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems; neural network adaptive dynamic output feedback control for nonlinear nonnegative systems using tapped delay memory units; Lyapunov-based adaptive control framework for discrete-time nonlinear systems with exogenous disturbances; direct discrete-time adaptive control with guaranteed parameter error convergence; and hybrid adaptive control for nonlinear uncertain impulsive dynamical systems.

Model Reference Adaptive Control (MRAC) is a widely studied adaptive control methodology that aims to ensure that a nonlinear plant with significant modeling uncertainty behaves like a chosen reference model. MRAC methods attempt to achieve this by representing the modeling uncertainty as a weighted combination of known nonlinear functions, and using a weight update law that ensures weights take on values such that the effect of the uncertainty is mitigated. If the adaptive weights do arrive at an ideal value that best represent the uncertainty, significant performance and robustness gains can be realized. However, most MRAC adaptive laws use only instantaneous data for adaptation and can only guarantee that the weights arrive at these ideal values if and only if the plant states are Persistently Exciting (PE). The condition on PE reference input is restrictive and often infeasible to implement or monitor online. Consequently, parameter convergence cannot be guaranteed in practice for many adaptive control applications. Hence it is often observed that traditional adaptive controllers do not exhibit long-term-learning and global uncertainty parametrization. That is, they exhibit little performance gain even when the system tracks a repeated command. This thesis presents a novel approach to adaptive control that relies on using current and recorded data concurrently for adaptation. The thesis shows that for a concurrent learning adaptive controller, a verifiable condition on the linear independence of the recorded data is sufficient to guarantee that weights arrive at their ideal values even when the system states are not PE. The thesis also shows that the same condition can guarantee exponential tracking error and weight error convergence to zero, thereby allowing the adaptive controller to recover the desired transient response and robustness properties of the chosen reference models and to exhibit long-term-learning. This condition is found to be less restrictive and easier to verify online than the condition on persistently exciting exogenous input required by traditional adaptive laws that use only instantaneous data for adaptation. The concept is explored for several adaptive control architectures, including neuro-adaptive flight control, where a neural network is used as the adaptive element. The performance gains are justified theoretically using Lyapunov based arguments, and demonstrated experimentally through flight-testing on Unmanned Aerial Systems.

This thesis is concerned with the applicability of model reference adaptive control to the control of robot manipulators under a wide range of configuration and payload changes, and a comparison of the performance of this technique with that of the non-adaptive schemes. The dynamic equations of robot manipulators are highly nonlinear and are difficult to determine precisely. For these reasons there is an interest in applying adaptive control techniques to robot manipulators. In this work, the detailed performance of three adaptive controllers are studied and compared with that of a non-adaptive controller, namely, the computed torque control scheme. Computer simulation results show that the use of adaptive control improves the performance of the manipulator despite changes in the payload or in the manipulator configuration. Making use of these results, a rule-based controller is developed by dividing a given manipulation task into portions where a particular adaptive control scheme, based on a specific linearized subsystem model, performs best. This strategy of selecting the proper controller during each portion of the overall task yields a performance having the least deviation from the desired trajectory during the entire length of the task.

Plastic injection moulding machines are a positive example of the possibilities in terms of performance and energy efficiency of modern hydraulic drives technology. In addition to the performance and energy efficiency of the machines, the quality of the plastic mouldings and an easy to use machines control is the focus. To ensure a constant plastics part quality the set process parameters of the injection moulding machines are kept constant by appropriate closed loop control strategies today. Assuming a constant quality of the processed plastic raw material, this strategy is effective. If it comes to a qualitative variation in the processed plastics, which often leads to a change in viscosity of the plastics melt, keeping processing parameters constant will not lead to a constant quality of the moulded parts. The deviations in the plastics viscosity have such a great influence on the moulding process that the relevant process parameters have to be adjusted manually in many cases. Often the stroke of the reciprocating screw system has to be adapted to reach a constant filling volume of the cavity and therefore avoid burr formation or short shots. In this paper an approach for adaptive process control is introduced. This control loop is able to correct the set points of specific machines parameters online within the production cycle and therefore is able to avoid changes in the produced parts quality.

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

This dissertation addresses the problem of rigid-body attitude tracking control under three scenarios of high relevance to many aerospace guidance and control applications: adaptive attitude-tracking control law development for a spacecraft with time-varying inertia parameters, velocity-free attitude stabilization using only vector measurements for feedback, and smooth angular velocity observer design for attitude tracking in the absence of angular velocity measurements. Inertia matrix changes in spacecraft applications often occur due to fuel depletion or mass displacement in a flexible or deployable spacecraft. As such, an adaptive attitude control algorithm that delivers consistent performance when faced with uncertain time-varying inertia parameters is of significant interest. This dissertation presents a novel adaptive control algorithm that directly compensates for inertia variations that occur as either pure functions of the control input, or as functions of time and/or the state. Another important problem considered in this dissertation pertains to rigid-body attitude stabilization of a spacecraft when only a set of inertial sensor measurements are available for feedback. A novel gyro-free attitude stabilization solution is presented that directly utilizes unit vector measurements obtained from inertial sensors without relying on observers to reconstruct the spacecraft's attitude or angular velocity. As the third major contribution of this dissertation, the problem of attitude tracking control in the absence of angular velocity measurements is investigated through angular velocity observer (estimator) design. A new angular velocity observer is presented which is smoothed and ensures asymptotic convergence of the estimation errors irrespective of the initial true states of the spacecraft. The combined implementation of a separately designed proportional-derivative type controller using estimates generated by the observer results in global asymptotic stability of the overall closed-loop tracking error dynamics. Accordingly, a separation-type property is established for the rigid-body attitude dynamics, the first such result to the author's best knowledge, using a smooth (switching-free) observer formulation. / text

Dynamic Matrix Control (DMC) has proven to be a powerful tool for optimal regulation of chemical processes under constrained conditions. The internal model of this predictive controller is based on step response measurements at an average operating point. As the process moves away from this point, however, control becomes sub-optimal due to process non-linearity. If DMC is made adaptive, it can be expected to perform well even in the presence of uncertainties, non-linearities and time-vary ing process parameters. This project examines modelling and control issues for a complex multivariable industrial operator training plant, and develops and applies a method for adapting the controller on-line to account for non-linearity. A two-input/two-output sub-system of the Training Plant was considered. A special technique had to be developed to deal with the integrating nature of this system - that is, its production of ramp outputs for step inputs. The project included the commissioning of the process equipment and the addition of instrumentation and interfacing to a SCADA system which has been developed in the School of Chemical Engineering. / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2001.