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A networked control system (NCS) is a feedback control system that has its control loop physically connected via real-time communication networks. To meet the demands of `teleautomation', modularity, integrated diagnostics, quick maintenance and decentralization of control, NCSs have received remarkable attention worldwide during the past decade. Yet despite their distinct advantages, NCSs are suffering from network-induced constraints such as time delays and packet dropouts, which may degrade system performance. Therefore, the network-induced constraints should be incorporated into the control design and related studies.<p> For the problem of state estimation in a network environment, we present the strategy of simultaneous input and state estimation to compensate for the effects of unknown input missing. A sub-optimal algorithm is proposed, and the stability properties are proven by analyzing the solution of a Riccati-like equation.<p> Despite its importance, system identification in a network environment has been studied poorly before. To identify the parameters of a system in a network environment, we modify the classical Kalman filter to obtain an algorithm that is capable of handling missing output data caused by the network medium. Convergence properties of the algorithm are established under the stochastic framework.<p> We further develop an adaptive control scheme for networked systems. By employing the proposed output estimator and parameter estimator, the designed adaptive control can track the expected signal. Rigorous convergence analysis of the scheme is performed under the stochastic framework as well.

A networked control system (NCS) is a feedback control system that has its control loop physically connected via real-time communication networks. To meet the demands of `teleautomation', modularity, integrated diagnostics, quick maintenance and decentralization of control, NCSs have received remarkable attention worldwide during the past decade. Yet despite their distinct advantages, NCSs are suffering from network-induced constraints such as time delays and packet dropouts, which may degrade system performance. Therefore, the network-induced constraints should be incorporated into the control design and related studies.<p> For the problem of state estimation in a network environment, we present the strategy of simultaneous input and state estimation to compensate for the effects of unknown input missing. A sub-optimal algorithm is proposed, and the stability properties are proven by analyzing the solution of a Riccati-like equation.<p> Despite its importance, system identification in a network environment has been studied poorly before. To identify the parameters of a system in a network environment, we modify the classical Kalman filter to obtain an algorithm that is capable of handling missing output data caused by the network medium. Convergence properties of the algorithm are established under the stochastic framework.<p> We further develop an adaptive control scheme for networked systems. By employing the proposed output estimator and parameter estimator, the designed adaptive control can track the expected signal. Rigorous convergence analysis of the scheme is performed under the stochastic framework as well.

This thesis is concerned with the True Digital Control (TDC) design philosophy and its practical embodiment in the non-minimal state space (NMSS) approach to control design, for systems described by discrete time transfer function models in the backward shift operator. This yields Proportional-Integral-Plus (PIP) controllers that are particularly easy to implement in practice, since the state variables are defined only in terms of the sampled input and output signals. The basic PIP algorithm is extended and enhanced in various ways to form a more sophisticated Generalised PIP controller. This includes an investigation into the importance of structure in PIP control design, the development of a command input anticipation technique and the introduction of two stochastic formulations of the problem, namely Kalman Filtering and risk sensitive optimal control. Finally, the thesis discusses the relationship between PIP and predictive control, in particular Generalised Predictive Control (GPC) and the Smith Predictor. The power of the approach is illustrated by the design of PIP controllers for a number of difficult applications also described in the thesis, including the control of a large horticultural greenhouse at Silsoe Research Institute; the control of carbon dioxide in crop growth experiments; the control of a Statistical Traffic Model simulation of interurban traffic networks; and, finally, the control of the multivariable Shell heavy oil fractionator simulation.

The field of UAV systems is an active research area with potential for development and enhancement in various perspectives. This thesis investigates different issues related to the design, operation and control of UAV systems with a focus on the application side of each proposed solution where the implementation side and applicability of the proposed solutions are always considered with high priority. The thesis discusses unmodeled actuator dynamics and their effect on UAV systems when using feedback linearisation to linearize nonlinear models of UAVs. The analysis shows potential risk when implementing feedback linearisation and neglecting actuator dynamics even for first order actuator system. A solution algorithm of two stage feedback linearisation is proposed to handle actuator dynamics and linearize the main dynamics of the system. In the field of design and operation of UAVs, this thesis proposes a systematic design procedure for electric propulsion systems that are widely used in UAVs. The design procedure guides the designer step by step to achieve minimum propulsion system weight or maximum flight time or a trade off between the two factors from the supplied solution sets. On the navigation side, the thesis proposes a new indoor navigation system that is easy to implement and less costly compared with other indoor navigation systems. The proposed system can be classified under computer-vision based navigation systems, however, it needs less information and less computational capacity. The thesis also contributes to the structure design of UAV systems by producing a novel tri-rotor UAV platform. The proposed UAV is novel in structure and design and has a centralized control system that stabilizes and tracks both rotational and transitional motion of the vehicle simultaneously.

Liebherr mobile harbor cranes use electrical generators to provide electrical power for load attachment devices such as container spreaders or magnets. Upcoming exhaust and noise emission standards and energy saving considerations lead to a broad diesel engine speed range. The challenging design aspect is to ensure a constant speed of the asynchronous generator by the hydraulic drive system. In addition, electrical load profiles of inductive consumers usually have DT1 system characteristics with very small time constants. They evoke fast torque variations interfacing the hydraulic transmission. Liebherr mobile harbor cranes, see Figure 1, usually have a closed hydraulic circuit containing a hydraulic pump with a high displacement volume that is adjusted electronically in accordance to the current diesel engine speed. Regarding the energy saving aspects, a further minimization of the diesel engine speed leads to a larger pump size with increasing torque losses. Depending on the pressure setting, the volume flows can be reduced in constant pressure grids. Especially in part-load operation this results in better efficiency compared to closed hydraulic circuits by minimizing the displacement volume of hydraulic components. To obtain a stable generator speed, it is essential to adjust the displacement volume of the hydraulic unit for equalizing its input torque with the Figure 1: LHM 800 Group 10 - Mobile Hydraulics | Paper 10-5 199 generator load torque. In interaction with the software-based control architecture, the stability of the electrical frequency depends on the mass inertia of the generator drive and time constants of the embedded hydraulic actuators. The system model, represented by ODEs is established and verified with a hydraulic simulation software. On that basis, the design approach of a PI-state-controller is presented. Corresponding controller gains and state feedback parameters are determined by pole placement techniques. To conclude this investigation a comparison between the hydraulically closed circuit and the constant pressure grid is shown by simulation and measurement data.

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The non-local stabilization of nonlinear systems by output feedback is a challenging problem that remains the subject of continuing investigation in control theory. In this thesis we develop two globally asymptotically stabilizing output feedback algorithms for multivariable nonlinear systems. Our first result is an extension a well-known output feedback method to a class of nonlinear systems whose dynamics can be written as a collection of subsystems that are dynamically coupled through output-dependent nonlinear terms. We show that this method must be modified to accommodate the dynamic coupling by introducing additional nonlinear damping terms into each control input. Our second contribution involves the application of observer backstepping to systems in a restricted block-triangular observer form. In this form, the nonlinearities entering each subsystem are allowed to depend on the output associated with the subsystem, and all upper subsystem states, including unmeasured ones. The proposed algorithm is demonstrated on a magnetically levitated ball. / Controls