Mode Switching Control Using Lane Keeping Assist and Waypoints Tracking for Autonomous Driving in a City Environment

This paper proposes a mode switching supervisory controller for autonomous vehicles. The supervisory controller selects the most appropriate controller based on safety constraints and on the vehicle location with respect to junctions. Autonomous steering, throttle and deceleration control inputs are used to perform variable speed lane keeping assist, standard or emergency braking and to manage junctions, including roundabouts. Adaptive model predictive control with lane keeping assist is performed on the main roads and a linear pure pursuit inspired controller is applied using waypoints at road junctions where lane keeping assist sensors present a safety risk. A multi-stage rule based autonomous braking algorithm performs stop, restart and emergency braking maneuvers. The controllers are implemented in MATLAB® and Simulink™ and are demonstrated using the Automatic Driving Toolbox™ environment. Numerical simulations of autonomous driving scenarios demonstrate the efficiency of the lane keeping assist mode on roads with curvature and the ability to accurately track waypoints at cross intersections and roundabouts using a simpler pure pursuit inspired mode. The ego vehicle also autonomously stops in time at signaled intersections or to avoid collision with other road users

Modelling and optimal control of nanopositioning piezo stage

Nanopositioners have a wide variety of applications in many fields, such as micro and nano manufacturing, medical research and study of micro and nano material properties. They are mainly used to induce forces and movements in micrometer and nanometer range. They are used as a part of special equipment\u27s like atomic force microscope and scanning probe microscope which are widely used for the study of microscale or nanoscale material properties. Research efforts on nanopositioners can be broadly classified into two categories, modelling and control. Modelling of nanopositioners involve modelling of both their linear dynamics as well as their nonliner dynamics like creep and hysteresis. Many efforts are being made in understanding and control of these nonlinearities. Optimal control is one of the most widely used approach for a nanopositioner in order to achieve high speed high precision control. To address the modelling issue of creep nonlinearity, traditionally, approximate linear models or logarithmic models were used. Unlike creep, hysteresis nonlinearity is quite complex to model. Hence many efforts were made to understand and mathematically formulate hysteresis, the most popular of hysteresis models are Bouc-Wen models, Prandtl-Ishlinskii models, Duhem models etc. the problem with these models is that they are hard to linearize or invert for the purpose of control, especially if they are used for the control of wide range of frequency profiles. In the last decade numerous efforts were made in modelling the nonlinear behavior of the nanopositioners using neural networks. Due to the inherent nonlinearities the optimal control of a nanopositioner is difficult. Recently many tools were developed for the nonlinear optimal control using neural network models. Model Predictive control(MPC) is one of the most widely used optimal control techniques. The main advantage of MPC are that it provides essential tools to apply constraint to the control problem. Many techniques were developed in past for linear MPC control and nonlinear MPC control using neural networks. Due to this advantages, in this work we are using MPC for the optimal control of nanopositioners. Since nanopositioners are involved in high speed operations in sub-micrometer ranges, using purely physics-based models to formulate the dynamics may not result in accurate models. Consequently, purely data driven models or hybrid models (data+physics) are widely utilized. In the methods proposed in this work we make use of purely data driven models. The advantage of using data driven models is that they can be built without the prior knowledge of the internal physics of a system. After a data driven model is built it can latter be analyzed to understand the internal physics of a system. In this work we present the use of traditional linear methods for the purpose of modelling and control of nonlinear behavior of nanopositioners. A way to model nonlinear behavior using linear methods is by using adaptive linear models whose parameters depend on the operating point i.e., they are time varying linear parameters. In this work we have investigated two approaches in solving the nonlinear control problem of a nanopositioner. In the first method we make use of concept of cascade control where we first optimally control the nanopositioner system using MPC techniques and then we use an additional controller on the MPC+nanopositioner system to minimize the tracking error. To demonstrate the efficacy of the proposed methods, they were developed and used for the trajectory tracking of a nanopositioner. And the result were then compared to the traditional control schemes like linear MPC etc. The second method proposed is adaptive Model Predictive Control to achieve the optimal tracking control of the nanopositioner. Adaptive MPC is a novel method in which we use adaptive time varying linear model at each operating point to achieve the control goal. Nanopositioners are generally fast responding systems hence the sampling frequency used for their practical operations is high. Consequently, adaptive MPC will lead to optimal control performance. Since models are built for each operating point, adaptive MPC can be used for wide variety of input profiles. To demonstrate its performance, an adaptive MPC controller was developed and implemented for the trajectory tracking of a nanopositioner and its results were then compared with traditional control techniques like PID controller. The results of both the proposed methods show that they can be used for a wide range of frequency profiles. Unlike many other data-driven techniques where the developed systems will be biased to the profile of the data used to develop the system

Platooning-based control techniques in transportation and logistic

This thesis explores the integration of autonomous vehicle technology with smart manufacturing systems. At first, essential control methods for autonomous vehicles, including Linear Matrix Inequalities (LMIs), Linear Quadratic Regulation (LQR)/Linear Quadratic Tracking (LQT), PID controllers, and dynamic control logic via flowcharts, are examined. These techniques are adapted for platooning to enhance coordination, safety, and efficiency within vehicle fleets, and various scenarios are analyzed to confirm their effectiveness in achieving predetermined performance goals such as inter-vehicle distance and fuel consumption. A first approach on simplified hardware, yet realistic to model the vehicle's behavior, is treated to further prove the theoretical results. Subsequently, performance improvement in smart manufacturing systems (SMS) is treated. The focus is placed on offline and online scheduling techniques exploiting Mixed Integer Linear Programming (MILP) to model the shop floor and Model Predictive Control (MPC) to adapt scheduling to unforeseen events, in order to understand how optimization algorithms and decision-making frameworks can transform resource allocation and production processes, ultimately improving manufacturing efficiency. In the final part of the work, platooning techniques are employed within SMS. Autonomous Guided Vehicles (AGVs) are reimagined as autonomous vehicles, grouping them within platoon formations according to different criteria, and controlled to avoid collisions while carrying out production orders. This strategic integration applies platooning principles to transform AGV logistics within the SMS. The impact of AGV platooning on key performance metrics, such as makespan, is devised, providing insights into optimizing manufacturing processes. Throughout this work, various research fields are examined, with intersecting future technologies from precise control in autonomous vehicles to the coordination of manufacturing resources. This thesis provides a comprehensive view of how optimization and automation can reshape efficiency and productivity not only in the domain of autonomous vehicles but also in manufacturing