The author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases traffic safety and the capacity of pre-existing road infrastructure. Service robotics may also benefit from the cost savings and flexibility offered by distance and tracking control which enables a number of robots to cooperate together in order to achieve a task beyond the capabilities ofjust one robot. Using a distance and tracking control scheme an intelligent leader robot may guide a number of less intelligent (and therefore less costly and less complex) followers to a work-site to perform a task. The author's approach to the distance and tracking control problem consisted of two separate solutions - an initial solution used as a starting point and learning experience and a second, more robust, fuzzy control-based solution. This thesis briefly describes the initial solution, but places a greater emphasis on the second solution. The reason for this is that the fuzzy control-based solution offers significant improvement on the initial solution and was developed based on conclusions drawn from the initial solution. Most implementations of distance and tracking control, sometimes referred to as Intelligent Cruise Control (ICC) or platooning, are limited to longitudinal distance control only. The leader tracking control is performed either implicitly by a separate lane-following control system or by human drivers. The fuzzy control-based solution offered in this thesis performs both distance and tracking control of an autonomous follower vehicle with respect to a leader vehicle in front of it. It represents a simple and cost effective solution to the requirements of autonomous vehicles operating in ITS schemes - particularly close formation platooning. The follower tracks a laser signal emitted by the leader and monitors the distance to the follower at the same time using ultrasonic ranging techniques. The follower uses the data obtained from these measuring techniques as inputs to a fuzzy controller algorithm to adjust its distance and alignment with respect to the leader. Other systems employed on road vehicles utilise video-based leader tracking, or a range of lane-following methods such as magnetometer or video-based methods. Typically these methods are disadvantaged by substantial unit and/or infrastructure costs associated with their deployment. The limitations associated with the solutions presented in this thesis arise in curved trajectories at larger longitudinal distance separations between vehicles. The effects of these limitations on road vehicles has yet to be fully quantified, however it is thought that these effects would not disadvantage its use in close formation platooning. The fuzzy control-based distance and tracking control solution features two inputs, which are the distance and alignment of the follower with respect to the leader. The fuzzy controller asserts two outputs, which are left and right wheel velocities to control the speed and trajectory of a differential drive vehicle. Each of the input and output fuzzy membership functions has seven terms based around lambda, Z-type and S-type functions. The fuzzy rule base consists of 49 rules and the fuzzy inference stage is based on the MAX/MIN method. A Centre of Maximum (CoM) def'uzzification method is used to provide the two crisp valued outputs to the vehicle motion control. The methods chosen for the fuzzy control of distance and tracking for autonomous vehicles were selected based on a compromise between their computational complexity and performance characteristics. This compromise was necessary in order to implement the chosen controller structure on pre-existing hardware test beds based on an 8-bit microcontrollers with limited memory and processing resources. Overall the fuzzy control-based solution presented in this thesis effectively solves the distance and tracking control problem. The solution was applied to differential drive hardware test-beds and was tested to verify performance. The solution was thoroughly tested in both the simulation environment and on hardware test-beds. Several issues are identified in this thesis regarding the application of the solution to other platforms and road vehicle use. The solution will be shown to be directly portable to service robotics applications and, with minor modifications, applicable to road vehicle close-formation platooning.

Mobile robots are commonly used to achieve tasks involving tracking a desired trajectory and following a predefined path in different types of terrains that have different surface characteristics. A mobile robot can perform the same navigation task task over different surfaces if the tracking performance and accuracy are not essential. However, if the tracking performance is the main objective, due to changing the characteristics of wheel-ground interaction, a single set of controller parameters or an equation of motion might be easily failing to guarantee a desired performance and accuracy. The interaction occurring between the wheels and ground can be integrated into the system model so that the performance of the mobile robot can be enhanced on various surfaces. This modeling approach related to wheel-ground interaction can also be incorporated into the motion controller. In this thesis study, modeling studies for a two wheeled differential drive mobile robot and a steerable four-wheeled robot vehicle are carried out. A strategy to achieve better tracking performance for a differential drive mobile robot is developed by introducing a procedure including the effects of external wheel forces / i.e, traction, rolling and lateral. A new methodology to represent the effects of lateral wheel force is proposed. An estimation procedure to estimate the parameters of external wheel forces is also introduced. Moreover, a modeling study that is related to show the effects of surface inclination on tracking performance is performed and the system model of the differential drive mobile robot is updated accordingly. In order to accomplish better trajectory tracking performance and accuracy for a steerable four-wheeled mobile robot, a modeling work that includes a desired trajectory generator and trajectory tracking controller is implemented. The slippage is defined via the slip velocities of steerable front and motorized rear wheels of the mobile robot. These slip velocities are obtained by using the proposed slippage estimation procedure. The estimated slippage information is then comprised into the system model so as to increase the performance and accuracy of the trajectory tracking tasks. All the modeling studies proposed in this study are tested by using simulations and verified on experimental platforms.

A field-oriented control design and detailed analysis of a VSI-fed PMSM drive system with active load is done through simulations of the system model, using modern simulation software packages. A new control method for the speed tracking control based on the estimation of the load torque profile is proposed. A new, multilevel modeling approach for creating simulation models of power electronic circuits is developed for easier analysis and faster simulations. It is based on a modular approach wherein each module can be modeled at any level of complexity, while maintaining full compatibility of the modules. The new approach is applied to modeling of the VSI-fed PMSM drive system. The three-phase VSI-fed PMSM drive system model that is developed and experimentally verified is analyzed in the application of a starter/generator, where the load changes dynamically with motor speed. As a result, a detailed analysis of the field-oriented control design of a two stage cascade digital controller is presented, with an emphasis on the new method for the speed control, large-signal and small-signal analyses of several most popular flux-weakening strategies, and sampling delay effects on the system stability. / Master of Science

The automation of hydraulic mobile machinery, such as excavators, requires high performance control solutions. In hydraulics, this means fast and accurate force, velocity and position control of hydraulic cylinder. Especially the force control is known to be difficult with traditional servo valves. Fast digital hydraulic valves together with modern control solutions can overcome this problem. This paper uses a new force control solution, which is based on the fast digital hydraulic valves and model based control principle. The control solution is applied in a heavy axis mimicking dynamics of mobile machine booms. Experimental results show good force, velocity and position tracking performance with varying load masses. The slow velocity performance is also much improved when compared to the earlier results.

Tracking control algorithms often target the convergence of a tracking error. However, this can be at the expense of other important system characteristics, such as the control effort used to annihilate the tracking error, transient response, or steady-state characteristics, for example. Furthermore, most tracking control methods assume prior knowledge of the system dynamics, which is not always a realistic assumption, especially in the case of highly complex systems. In this thesis, a model-free optimized tracking control architectural heuristic is proposed. The suggested feedback system is composed of two control loops. The first is the tracking loop. It focuses on the convergence of the tracking error. It is implemented using two different model-free control algorithms for comparison purpose: Reinforcement Learning (RL) and the Nonlinear Threshold Accepting (NLTA) technique. The RL scheme reformulates the tracking error combinations into a form of Markov-Decision-Process (MDP) and applies Q-Learning to build the best tracking control policy for the dynamic system under consideration. On the other hand, the NLTA algorithm is applied to tune the gains of a PID controller. The second control loop is in the form of a nonlinear state feedback loop. It is implemented using a feedforward artificial neural network (ANN) to optimize a system-wide cost function which can be flexible enough to encompass a set of desired design requirements pertaining to the targeted system behavior. This may include, for instance, the target overshoot, settling time, rise time, etc. The proposed architectural heuristic provides a model-free framework to tackle such control problems, in the sense that the plant's dynamic model is not required to be known in advance. Yet, at least a subset of the stability region of the optimized gains has to be known in advance so that it can provide a search space for the optimization algorithms. Simulation results on two dynamic systems demonstrate the superiority of the proposed control scheme.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第18406号 / 情博第521号 / 新制||情||92(附属図書館) / 31264 / 京都大学大学院情報学研究科数理工学専攻 / (主査)教授 太田 快人, 教授 梅野 健, 教授 大塚 敏之 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This thesis provides strategies for multiple vehicles to explore unknown environments in a cooperative and systematic manner. These strategies are called Simultaneous Cooperative Exploration and Networking (SCENT) strategies. As the basis for development of SCENT strategies, we first tackle the motion control and planning for one vehicle with range sensors. In particular, we develop the curve-tracking controllers for autonomous vehicles with rigidly mounted range sensors, and a provably complete exploration strategy is proposed so that one vehicle with range sensors builds a topological map of an environment. The SCENT algorithms introduced in this thesis extend the exploration strategy for one vehicle to multiple vehicles. The enabling idea of the SCENT algorithms is to construct a topological map of the environment, which is considered completely explored if the map corresponds to a complete Voronoi diagram of the environment. To achieve this, each vehicle explores its local area by incrementally expanding the already visited areas of the environment. At the same time, every vehicle deploys communication devices at selected locations and, as a result, a communication network is created concurrently with a topological map. This additional network allows the vehicles to share information in a distributed manner resulting in an efficient exploration of the workspace. The efficiency of the proposed SCENT algorithms is verified through theoretical investigations as well as experiments using mobile robots. Moreover, the resulting networks and the topological maps are used to solve coordinated multi-robot tasks, such as capturing intruders.

Batch reactor is an essential unit operation in almost all batch-processing industries. Different types of reaction schemes (such as series, parallel and complex) and different order of model complexity (short-cut, detailed, etc. ) result in different sets of model equations and computer coding of all possible sets of model equations is cumbersome and time consuming. In this work, therefore, a general computer program (GBRM - General Batch Reactor Model) is developed to generate all possible sets of equations automatically and as required. GBRM is tested for different types of reaction schemes and for different order of model complexity and its flexibility is demonstrated. The above GBRM computer program is lodged with Dr. I. M. Mujtaba. One of the challenges in batch reactors is to ensure desired performance of individual batch reactor operations. Depending on the requirement and the objective of the process, optimisation in batch reactors leads to different types of optimisation problems such as maximum conversion, minimum time and maximum profit problem. The reactor temperature, jacket temperature and jacket flow rate are the main control variables governing the process and these are optimised to ensure maximum benefit. In this work, an extensive study on mainly conventional batch reactor optimisation is carried out using GBRM coupled with efficient DAEs (Differential and Algebraic Equations) solver, CVP (Control Vector Parameterisation) technique and SQP (Successive Quadratic Programming) based optimisation technique. The safety, environment and product quality issues are embedded in the optimisation problem formulations in terms of constraints. A new approach for solving optimisation problem with safety constraint is introduced. All types of optimisation problems mentioned above are solved off-line, which results to optimal operating policies. The off-line optimal operating policies obtained above are then implemented as set points to be tracked on-line and various types of advanced controllers are designed for this purpose. Both constant and dynamic set points tracking are considered in designing the controllers. Here, neural networks are used in designing Direct Inverse and Inverse-Model-Based Control (IMBC) strategies. In addition, the Generic Model Control (GMC) coupled with on-line neural network heat release estimator (GMC-NN) is also designed to track the optimal set points. For comparison purpose, conventional Dual Mode (DM) strategy with PI and PID controllers is also designed. Robustness tests for all types of controllers are carried out to find the best controller. The results demonstrate the robustness of GMC-NN controller and promise neural controllers as potential robust controllers for future. Finally, an integrated framework (BATCH REACT) for modelling, simulation, optimisation and control of batch reactors is proposed.

The digitization of classical control systems presents a number of challenges and opportunities with respect to the miniaturization, distribution, reliability verification and obsolescence of both the controller and the underlying system under control. A method for the design of proportional-integral-derivative (PID) compensators realized in the form of all-digital components is presented. All-digital refers to a system implementation that is realizable with a wide range of digital logic components including discrete digital logic elements and programmable logic devices (PLDs) such as field-programmable gate arrays. The proportional, integral and derivative components of the classical PID control law were re-envisioned in terms of frequency of occurrences or counts for adaptation to combinatorial and sequential digital logic. Modification of the control scheme around this newly formed representation of system error enables the development of a PID-like FPGA-based or PLD-based controller. Details of the design of an all-digital PID-like controller including abstract, causal block diagrams and a MATLAB® and Simulink® based implementation are presented. The compensator was simulated in a velocity tracking DC motor control application and was found to perform comparably to that of a classical PID based control. Methods for assessing the resultant stability of an all-digital PID compensated system under control are discussed.

The lungs are particularly vulnerable to acute, critical illness. Respiratory failure can result not only from primary lung pathology, such as pneumonia, but also as a secondary consequence of heart failure or inflammatory illness, such as sepsis or trauma. When this occurs, it is essential to support patients with mechanical ventilation while the fundamental disease process is addressed. The goal of mechanical ventilation is to ensure adequate ventilation, which involves a magnitude of gas exchange that leads to the desired blood level of carbon dioxide, and adequate oxygenation that ensures organ function. Achieving these goals is complicated by the fact that mechanical ventilation can actually cause acute lung injury, either by inflating the lungs to excessive volumes or by using excessive pressures to inflate the lungs. Thus, the challenge to mechanical ventilation is to produce the desired blood levels of carbon dioxide and oxygen without causing further acute lung injury. In this research, we develop an analysis and control synthesis framework for a pressure-limited respirator and lung mechanics system using compartment models. Specifically, a general mathematical model is developed for the dynamic behavior of a multicompartment respiratory system. Then, based on this multicompartment model, an optimal respiratory pattern is characterized using classical calculus of variations minimization techniques for inspiratory and expiratory breathing cycles. Furthermore, model predictive controller frameworks are designed to track the given optimal respiratory air flow pattern while satisfying control input amplitude and rate constrains.