International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / Navigation is the means by which a craft is given guidance from one known location to another. Since the global positioning system (GPS) is very accurate positioning system, a personal navigation system based on GPS is very effective. From the user point of view, the function of this system is to provide real-time positioning and timing data to the user. The system consists of 6-channel GPS oncore receiver, a system controller & processor (SC&P) card, a programmable liquid crystal display (LCD) and a keyboard. The 6-channel GPS OEM card receives GPS signal from six different satellites at a time. After processing the received GPS signal, it gives the result & status message to its output port in a typical data format. The system controller & processor card receives this message from the GPS OEM card and extracts the useful positioning & timing information in binary form. After that it processes the data and displays it on the LCD display. The keyboard has used to select the desired positioning & timing information on the display.

Current efficiency measurements of variable hydraulic axial piston pumps are performed with the displacement system locked at maximum volume, thus without the controller. Therefore, the controller’s effect on the efficiency is not quantified at state of the art measurements. Former research on control systems mainly focused on the dynamic behaviour. This paper aims to quantify the losses in the displacement and control system and to research the dependencies of those. Therefore, a test rig is built up at IFAS to measure the control power of displacement controlled pumps. Furthermore, a simulation tool is developed to increase the understanding of the loss mechanisms of the investigated control systems. In conclusion, the paper shows the potential of efficiency improvements for displacement controlled pumps.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2006. / The distributed control system network involves analog data acquisition nodes interconnected through CAN and USB protocol interfaces to form a network. The network is designed to be generically applicable to various control problems. This network of controllers was specifically utilised on a scaled-down electrostatic roll separator plant used in the mineral processing industry. A SISO and a MIMO regulator was designed to demonstrate the regulation of plant parameters. The MIMO regulator was employed in a scheme that optimises the plant yield automatically. Analog data acquisition nodes were designed and built especially for the purposes of this project. These nodes were installed on the electrostatic roll separator plant. PC based application software was written so that plant ID experiments could be performed. SISO and MIMO regulators along with a yield optimising scheme was designed and implemented in the application software. Both SISO and MIMO regulators successfully regulated plant outputs. The nonconducting mineral product grade was regulated by the SISO regulator. The non-conducting mineral product grade and conductor mineral mass flow was regulated by the MIMO regulator. The yield optimiser successfully employed the MIMO regulator to optimise the plant yield automatically.

The main purpose of this thesis was to develop a printed circuit board for Autoliv Electronics AB. This circuit board should be placed in their test equipment to support some of their camera vision systems used in cars. The main task was to combine the existing hardware into one module. To be able to achieve this, the most important factors in designing a printed circuit board was considered. A satisfying power distribution network is the most crucial one. This was accomplished by using decoupling capacitors to achieve low enough impedance for all circuits. Calculations and simulations were executed for all integrated circuits to find the correct size and numbers of capacitors. The impedance of the circuit board was tested with a network analyzer to confirm that the impedance were low enough, which was the case. System functionality was never tested completely, due to delivery problems with some external equipment.

<p>This report presents the work carried out aiming towardsdistributed control of electric drives through a networkcommunication medium with temporal constraints, i.e, Ethernet.A general analysis on time delayed systems is carried out,using state space representation of systems in the discretetime domain. The effect of input time delays is identified andis used in the preceding controller designs. The main hardwareapplication focused in this study is a Brushless DC servomotor, whose speed control loop is closed via a 10 MbpsSwitched Ethernet network. The speed control loop, which isapproximately a decade slower than the current control loop, isopened and interfaced to the network at the sensor/actuatornode. It is closed at the speed controller end at another nodein the same local area network (LAN) forming a distributedcontrol system (DCS).</p><p>The Proportional Integral (PI) classical controller designtechnique with ample changes in parameter tuning suitable fortime delayed systems is used. Then the standard Smith Predictoris tested, modified with the algebraic design techniqueCoefficient Diagram Method (CDM), which increases the systemdegrees of freedom. Constant control delay is assumed in thelatter designs despite the slight stochastic nature in thetiming data observations. Hence the poor transient performanceof the system is the price for the robustness inherited to thespeed controllers at the design stage. The controllability andobservability of the DCS may be lost, depending on the range inwhich the control delay is varying. However a state feedbackcontroller deploying on-line delay data, obtained by means ofsynchronizing the sensor node and controller node systemclocks, results in an effective compensation scheme for thenetwork induced delays. Hence the full state feedbackcontroller makes he distributed system transient performanceacceptable for servo applications with the help of poleplacement controller design.</p><p>Further, speed synchronizing controllers have been designedsuch that a speed fluctuation caused by a mechanical loadtorque disturbance on one motor is followed effectively by anyother specified motor in the distributed control network with aminimum tracking or synchronizing error. This type ofperformance is often demanded in many industrial applicationssuch as printing, paper, bagging, pick and place and materialcutting.</p><p><b>Keywords:</b>Brushless DC Motor, Control Delay, DistributedMotion Control Systems, Proportional Integral Controller, SmithPredictor, Speed Synchronization, State Feedback Controller,Stochastic Systems, Switched-Ethernet, Synchronizing Error,Time Delayed Systems, Tracking Error</p>

This thesis presents the development of self adaptive flight controller for an unmanned helicopter system under hovering manoeuvre. The neural network (NN) based model predictive control (MPC) approach is utilised in this work. We use this controller due to its ability to handle system constraints and the time varying nature of the helicopter dynamics. The non-linear NN based MPC controller is known to produce slow solution convergence due to high computation demand in the optimisation process. To solve this problem, the automatic flight controller system is designed using the NN based approximate predictive control (NNAPC) approach that relies on extraction of linear models from the non-linear NN model at each time step. The sequence of control input is generated using the prediction from the linearised model and the optimisation routine of MPC subject to the imposed hard constraints. In this project, the optimisation of the MPC objective criterion is implemented using simple and fast computation of the Hildreth's Quadratic Programming (QP) procedure. The system identification of the helicopter dynamics is typically performed using the time regression network (NNARX) with the input variables. Their time lags are fed into a static feed-forward network such as the multi-layered perceptron (MLP) network. NN based modelling that uses the NNARX structure to represent a dynamical system usually requires a priori knowledge about the model order of the system. Low model order assumption generally leads to deterioration of model prediction accuracy. Furthermore, massive amount of weights in the standard NNARX model can result in an increased NN training time and limit the application of the NNARX model in a real-time application. In this thesis, three types of NN architectures are considered to represent the time regression network: the multi-layered perceptron (MLP), the hybrid multi-layered perceptron (HMLP) and the modified Elman network. The latter two architectures are introduced to improve the training time and the convergence rate of the NN model. The model structures for the proposed architecture are selected using the proposed Lipschitz coefficient and k-cross validation methods to determine the best network configuration that guarantees good generalisation performance for model prediction. Most NN based modelling techniques attempt to model the time varying dynamics of a helicopter system using the off-line modelling approach which are incapable of representing the entire operating points of the flight envelope very well. Past research works attempt to update the NN model during flight using the mini-batch Levenberg-Marquardt (LM) training. However, due to the limited processing power available in the real-time processor, such approaches can only be employed to relatively small networks and they are limited to model uncoupled helicopter dynamics. In order to accommodate the time-varying properties of helicopter dynamics which change frequently during flight, a recursive Gauss-Newton (rGN) algorithm is developed to properly track the dynamics of the system under consideration. It is found that the predicted response from the off-line trained neural network model is suitable for modelling the UAS helicopter dynamics correctly. The model structure of the MLP network can be identified correctly using the proposed validation methods. Further comparison with model structure selection from previous studies shows that the identified model structure using the proposed validation methods offers improvements in terms of generalisation error. Moreover, the minimum number of neurons to be included in the model can be easily determined using the proposed cross validation method. The HMLP and modified Elman networks are proposed in this work to reduce the total number of weights used in the standard MLP network. Reduction in the total number of weights in the network structure contributes significantly to the reduction in the computation time needed to train the NN model. Based on the validation test results, the model structure of the HMLP and modified Elman networks are found to be much smaller than the standard MLP network. Although the total number of weights for both of the HMLP and modified Elman networks are lower than the MLP network, the prediction performance of both of the NN models are on par with the prediction quality of the MLP network. The identification results further indicate that the rGN algorithm is more adaptive to the changes in dynamic properties, although the generalisation error of repeated rGN is slightly higher than the off-line LM method. The rGN method is found capable of producing satisfactory prediction accuracy even though the model structure is not accurately defined. The recursive method presented here in this work is suitable to model the UAS helicopter in real time within the control sampling time and computational resource constraints. Moreover, the implementation of proposed network architectures such as the HMLP and modified Elman networks is found to improve the learning rate of NN prediction. These positive findings inspire the implementation of the real time recursive learning of NN models for the proposed MPC controller. The proposed system identification and hovering control of the unmanned helicopter system are validated in a 6 degree of freedom (DOF) safety test rig. The experimental results confirm the effectiveness and the robustness of the proposed controller under disturbances and parameter changes of the dynamic system.

In Automotive Industries, to be confident regarding the success of a planned operation, performing accurate methods in order to detect abnormal operating conditions, known as faults, is crucial. An effective method for diagnosis and fault recognition ensures the safety of the operation, reduces manufacturing cost and any other potential impacts. In addition, mobile solutions have been widely adopted among automotive manufactures during recent years and they have taken full advantage of mobile strategies. Accordingly, it is necessary for there to be a future-proof plan to control the diagnostic operations in advance. In this thesis, the immediate objective has been to offer a future-proof and user-friendly solution to assist engineers and service technicians in the monitoring, detecting, and diagnosing of faults on Toyota/BT/CESAB branded trucks. A mobile cross-platform framework is used to develop the diagnostic mobile solution which is not only able to be deployed on Android and iOS mobile platforms, but also provides wireless communication between truck machines and mobile devices through Bluetooth and Wi-Fi ad hoc technologies. The diagnostic mobile tool is capable of processing real-time controller area network messages and visualizing the condition of different sensors in a more user-friendly way through rich hybrid and client-side web user interfaces. The experience of evaluating a cross-platform diagnostic tool on different mobile operating systems proved that cross-platform mobile development methodology can be a reliable technique for developing projects that essentially require real-time data processing. In addition, it indicates that Apple iOS offers a better runtime performance than Google Android for the current tool.

Mathematical models of physical systems are pervasive in engineering. These models can be used to analyze properties of the system, to simulate the system, or synthesize controllers. However, many of these models are too complex or too large for standard analysis and synthesis methods to be applicable. Hence, there is a need to reduce the complexity of models. In this thesis, techniques for reducing complexity of large linear time-invariant (lti) state-space models and linear parameter-varying (lpv) models are presented. Additionally, a method for synthesizing controllers is also presented. The methods in this thesis all revolve around a system theoretical measure called the H2-norm, and the minimization of this norm using nonlinear optimization. Since the optimization problems rapidly grow large, significant effort is spent on understanding and exploiting the inherent structures available in the problems to reduce the computational complexity when performing the optimization. The first part of the thesis addresses the classical model-reduction problem of lti state-space models. Various H2 problems are formulated and solved using the proposed structure-exploiting nonlinear optimization technique. The standard problem formulation is extended to incorporate also frequency-weighted problems and norms defined on finite frequency intervals, both for continuous and discrete-time models. Additionally, a regularization-based method to account for uncertainty in data is explored. Several examples reveal that the method is highly competitive with alternative approaches. Techniques for finding lpv models from data, and reducing the complexity of lpv models are presented. The basic ideas introduced in the first part of the thesis are extended to the lpv case, once again covering a range of different setups. lpv models are commonly used for analysis and synthesis of controllers, but the efficiency of these methods depends highly on a particular algebraic structure in the lpv models. A method to account for and derive models suitable for controller synthesis is proposed. Many of the methods are thoroughly tested on a realistic modeling problem arising in the design and flight clearance of an Airbus aircraft model. Finally, output-feedback H2 controller synthesis for lpv models is addressed by generalizing the ideas and methods used for modeling. One of the ideas here is to skip the lpv modeling phase before creating the controller, and instead synthesize the controller directly from the data, which classically would have been used to generate a model to be used in the controller synthesis problem. The method specializes to standard output-feedback H2 controller synthesis in the lti case, and favorable comparisons with alternative state-of-the-art implementations are presented.

In this thesis, a cognitive neural predictive controller system has been designed to guide a nonholonomic wheeled mobile robot during continuous and non-continuous trajectory tracking and to navigate through static obstacles with collision-free and minimum tracking error. The structure of the controller consists of two layers; the first layer is a neural network system that controls the mobile robot actuators in order to track a desired path. The second layer of the controller is cognitive layer that collects information from the environment and plans the optimal path. In addition to this, it detects if there is any obstacle in the path so it can be avoided by re-planning the trajectory using particle swarm optimisation (PSO) technique. Two neural networks models are used: the first model is modified Elman recurrent neural network model that describes the kinematic and dynamic model of the mobile robot and it is trained off-line and on-line stages to guarantee that the outputs of the model will accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The second model is feedforward multi-layer perceptron neural network that describes a feedforward neural controller and it is trained off-line and its weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index predictive optimisation algorithm for N step-ahead prediction in order to find the optimal torque action in the transient to stabilise the tracking error of the mobile robot system when the trajectory of the robot is drifted from the desired path during transient state. Three controller methodologies were developed: the first is the feedback neural controller; the second is the nonlinear PID neural feedback controller and the third is nonlinear inverse dynamic neural feedback controller, based on the back-stepping method and Lyapunov criterion. The main advantages of the presented approaches are to plan an optimal path for itself avoiding obstructions by using intelligent (PSO) technique as well as the analytically derived control law, which has significantly high computational accuracy with predictive optimisation technique to obtain the optimal torques control action and lead to minimum tracking error of the mobile robot for different types of trajectories. The proposed control algorithm has been applied to monitor a nonholonomic wheeled mobile robot, has demonstrated the capability of tracking different trajectories with continuous gradients (lemniscates and circular) or non-continuous gradients (square) with bounded external disturbances and static obstacles. Simulations results and experimental work showed the effectiveness of the proposed cognitive neural predictive control algorithm; this is demonstrated by the minimised tracking error to less than (1 cm) and obtained smoothness of the torque control signal less than maximum torque (0.236 N.m), especially when external disturbances are applied and navigating through static obstacles. Results show that the five steps-ahead prediction algorithm has better performance compared to one step-ahead for all the control methodologies because of a more complex control structure and taking into account future values of the desired one, not only the current value, as with one step-ahead method. The mean-square error method is used for each component of the state error vector to compare between each of the performance control methodologies in order to give better control results.

Previous research of the controller role is extensive and has been studied in several sectors, whichprovides a wide range of definitions of the controller role. These definitions have contributed to an ambiguous controller role in regards to what work assignments are most important and to whom the controller should report. This thesis aims to provide an in-depth understanding of the controller role based on work assignments within a decentralised organisation working with complex financial products. This thesis also contributes to an understanding of how controllers perceive that their role is affected by their work with handling information and their report relations. This has generated three research questions: What role does the controller have in a Swedish universal bank based on work assignments? What are the eventual differences in the controller role depending on department in the organisation? How do the controllers perceive that their role is affected by their work with handling information and by their report relations? Delimitation was made to analyse controllers at various levels in Handelsbanken. In order to create an understanding of the controller role in this context, an abductive approach as been used in order to combine existing theories with empirical findings. Based on a qualitative approach, triangulation was chosen to combine assembled empirical data with semi- structured interviews. The result of this study implies that controllers mainly lean towards the role as a Business partner as they work as a support function to provide local or higher managers with relevant analysis for decision-making. Based on work processes with information, the controllers lean towards an Analyst and Coach as they generally handle all business related information. From this case study, controllers in decentralised organisations possess the role as a Specialist as they  are  situated in separate departments with a clear focus. Results also show that more automated work assignments due to technological development do not increase the controllers’ opportunity to dedicate more time on analysis. Instead, increased information flows require controllers to allocate resources towards assembling information. In terms of report relations, close adherence towards the local managers does not affect the controller’s objective reporting to higher management and the controller can arguably bemore independent within their report relations than what is described by literature.