Robust navigation control and headland turning optimization of agricultural vehicles

Autonomous agricultural robots have experienced rapid development during the last decade. They are capable of automating numerous field operations such as data collection, spraying, weeding, and harvesting. Because of the increasing demand of field work load and the diminishing labor force on the contrary, it is expected that more and more autonomous agricultural robots will be utilized in future farming systems. The development of a four-wheel-steering (4WS) and four-wheel-driving (4WD) robotic vehicle, AgRover, was carried out at Agricultural Automation and Robotics Lab at Iowa State University. As a 4WS/4WD robotic vehicle, AgRover was able to work under four steering modes, including crabbing, front steering, rear steering, and coordinated steering. These steering modes provided extraordinary flexibilities to cope with off-road path tracking and turning situations. AgRover could be manually controlled by a remote joystick to perform activities under individual PID controller of each motor. Socket based software, written in Visual C#, was developed at both AgRover side and remote PC side to manage bi-directional data communication. Safety redundancy was also considered and implemented during the software development. One of the prominent challenges in automated navigation control for off-road vehicles is to overcome the inaccuracy of vehicle modeling and the complexity of soil-tire interactions. Further, the robotic vehicle is a multiple-input and multiple-output (MIMO) high-dimensional nonlinear system, which is hard to be controlled or incorporated by conventional linearization methods. To this end, a robust nonlinear navigation controller was developed based on the Sliding Mode Control (SMC) theory and AgRover was used as the test platform to validate the controller performance. Based on the theoretical framework of such robust controller development, a series of field experiments on robust trajectory tracking control were carried out and promising results were achieved. Another vitally important component in automated agricultural field equipment navigation is automatic headland turning. Until now automated headland turning still remains as a challenging task for most auto-steer agricultural vehicles. This is particularly true after planting where precise alignment between crop row and tractor or tractor-implement is critical when equipment entering the next path. Given the motion constraints originated from nonholonomic agricultural vehicles and allowable headland turning space, to realize automated headland turning, an optimized headland turning trajectory planner is highly desirable. In this dissertation research, an optimization scheme was developed to incorporate vehicle system models, a minimum turning-time objective, and a set of associated motion constraints through a direct collocation nonlinear programming (DCNLP) optimization approach. The optimization algorithms were implemented using Matlab scripts and TOMLAB/SNOPT tool boxes. Various case studies including tractor and tractor-trailer combinations under different headland constraints were conducted. To validate the soundness of the developed optimization algorithm, the planner generated turning trajectory was compared with the hand-calculated trajectory when analytical approach was possible. The overall trajectory planning results clearly demonstrated the great potential of utilizing DCNLP methods for headland turning trajectory optimization for a tractor with or without towed implements

PID, BFO-optimized PID, and PD-FLC control of a two-wheeled machine with two-direction handling mechanism: a comparative study

In this paper; three control approaches are utilized in order to control the stability of a novel five-degrees-of-freedom two-wheeled robotic machine designed for industrial applications that demand a limited-space working environment. Proportional–integral–derivative (PID) control scheme, bacterial foraging optimization of PID control method, and fuzzy logic control method are applied to the wheeled machine to obtain the optimum control strategy that provides the best system stabilization performance. According to simulation results, considering multiple motion scenarios, the PID controller optimized by bacterial foraging optimization method outperformed the other two control methods in terms of minimum overshoot, rise time, and applied input forces

Segway driver parameter estimation and its use for optimizing the control algorithm

Táto práca sa zaoberá vývojom, testovaním a implementáciou adaptívneho riadiaceho systému pre dvojkolesové samobalancujúce vozidlo. Adaptácia parametrov vozidla sa uskutoční na základe parametrov vodiča. Parametre sústavy sa nemerajú priamo, ale sú odhadované na základe priebehu stavových premenných a odozvy sústavy. Medzi odhadované parametre patrí hmotnosť a poloha ťažiska vodiča. Cieľom práce je zabezpečiť adaptáciu jazdných vlastností vozidla k rôznym vodičom s rôznou hmotnosťou, kvôli zlepšeniu stability vozidla. Táto práca je pokračovaním predchádzajúcich projektov z roku 2011 a 2015.This thesis deals with development, verification and implementation of an adaptive control system on a two-wheeled self-balancing vehicle that based on the driver's parameters alters its behaviour. The parameters are not obtained by direct measurement but estimated based on the evolution of state variables and the system's response. The estimated parameters include driver's mass and height of his or her centre of gravity. The goal of this thesis is to verify the idea of an adaptation of the system's dynamical properties to different users while ensuring better stability. This thesis is the continuation of earlier projects finished in 2011 and 2015.

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Arm Angle Tracking Control with Pole Balancing Using Equivalent Input Disturbance Rejection for a Rotational Inverted Pendulum

This paper proposes a robust tracking control method for swing-up and stabilization of a rotational inverted pendulum system by applying equivalent input disturbance (EID) rejection. The mathematical model of the system was developed by using a Lagrangian equation. Then, the EID, including external disturbances and parameter uncertainties, was defined; and the EID observer was designed to estimate EID using the state observer dynamics and a low-pass filter. For robustness, the linear-quadratic regulator method is used with EID rejection. The closed-loop stability is proven herein using the Lyapunov theory and input-to-state stability. The performance of the proposed method is validated and verified via experimental results

Sistema de captura de variables cinéticas de bicicletas horizontales para simulación

In this study, a system has been developed that measures the speed, the turning angle, and the braking force in bicycles or tricycles in static mode. The system also electromechanically controls the resistance to pedaling, opening the possibility of using the system in conjunction with a virtual reality simulator for a computer to offer a new rehabilitation tool for lower limb amputees that use prostheses. The study was divided into two stages. The first was a proof of concept, implemented on a bicycle, evaluating the system requirements, the possible solutions, the necessary couplings for the selected sensors and actuators to test its operation's effectiveness. In the second stage, the system was coupled to a horizontal three-wheeled bicycle with new adjustments and improvements to evaluate its performance. The sensors and actuators implemented, together with the appropriate coupling systems, worked as expected. Hence, a new rehabilitation alternative for amputees is generated based on an appropriate communication protocol with a simulator.En este estudio se desarrolló un sistema que mide la velocidad, el ángulo de giro y la fuerza de frenado en bicicletas o triciclos en modo estático, controlando la resistencia al pedaleo electromecánicamente, abriendo la posibilidad de usar el sistema en conjunto con un simulador de realidad virtual para computador, con el objetivo de ofrecer una nueva herramienta de rehabilitación para amputados de miembro inferior que usan prótesis. Se dividió el estudio en dos etapas, la primera fue una prueba de concepto implementada sobre una bicicleta, evaluando los requerimientos del sistema, las posibles soluciones, los acoples necesarios para los sensores y actuadores seleccionados con el fin de evaluar la efectividad del funcionamiento. En la segunda etapa se acopló el sistema a una bicicleta horizontal de tres ruedas mediante ajustes y mejoras, para finalmente evaluar el desempeño del sistema. Los sensores y actuadores implementados, en conjunto con los sistemas de acople apropiados, los cuales funcionaron de acuerdo con lo esperado, de manera que, con un protocolo de comunicación apropiado con un simulador, se genera una nueva alternativa de rehabilitación para amputados

Adaptive Control For Autonomous Navigation Of Mobile Robots Considering Time Delay And Uncertainty

Autonomous control of mobile robots has attracted considerable attention of researchers in the areas of robotics and autonomous systems during the past decades. One of the goals in the field of mobile robotics is development of platforms that robustly operate in given, partially unknown, or unpredictable environments and offer desired services to humans. Autonomous mobile robots need to be equipped with effective, robust and/or adaptive, navigation control systems. In spite of enormous reported work on autonomous navigation control systems for mobile robots, achieving the goal above is still an open problem. Robustness and reliability of the controlled system can always be improved. The fundamental issues affecting the stability of the control systems include the undesired nonlinear effects introduced by actuator saturation, time delay in the controlled system, and uncertainty in the model. This research work develops robustly stabilizing control systems by investigating and addressing such nonlinear effects through analytical, simulations, and experiments. The control systems are designed to meet specified transient and steady-state specifications. The systems used for this research are ground (Dr Robot X80SV) and aerial (Parrot AR.Drone 2.0) mobile robots. Firstly, an effective autonomous navigation control system is developed for X80SV using logic control by combining ‘go-to-goal’, ‘avoid-obstacle’, and ‘follow-wall’ controllers. A MATLAB robot simulator is developed to implement this control algorithm and experiments are conducted in a typical office environment. The next stage of the research develops an autonomous position (x, y, and z) and attitude (roll, pitch, and yaw) controllers for a quadrotor, and PD-feedback control is used to achieve stabilization. The quadrotor’s nonlinear dynamics and kinematics are implemented using MATLAB S-function to generate the state output. Secondly, the white-box and black-box approaches are used to obtain a linearized second-order altitude models for the quadrotor, AR.Drone 2.0. Proportional (P), pole placement or proportional plus velocity (PV), linear quadratic regulator (LQR), and model reference adaptive control (MRAC) controllers are designed and validated through simulations using MATLAB/Simulink. Control input saturation and time delay in the controlled systems are also studied. MATLAB graphical user interface (GUI) and Simulink programs are developed to implement the controllers on the drone. Thirdly, the time delay in the drone’s control system is estimated using analytical and experimental methods. In the experimental approach, the transient properties of the experimental altitude responses are compared to those of simulated responses. The analytical approach makes use of the Lambert W function to obtain analytical solutions of scalar first-order delay differential equations (DDEs). A time-delayed P-feedback control system (retarded type) is used in estimating the time delay. Then an improved system performance is obtained by incorporating the estimated time delay in the design of the PV control system (neutral type) and PV-MRAC control system. Furthermore, the stability of a parametric perturbed linear time-invariant (LTI) retarded type system is studied. This is done by analytically calculating the stability radius of the system. Simulation of the control system is conducted to confirm the stability. This robust control design and uncertainty analysis are conducted for first-order and second-order quadrotor models. Lastly, the robustly designed PV and PV-MRAC control systems are used to autonomously track multiple waypoints. Also, the robustness of the PV-MRAC controller is tested against a baseline PV controller using the payload capability of the drone. It is shown that the PV-MRAC offers several benefits over the fixed-gain approach of the PV controller. The adaptive control is found to offer enhanced robustness to the payload fluctuations

Optimal control of a motor-integrated hybrid powertrain for a two-wheeled vehicle suitable for personal transportation

The present research aims to propose an optimized configuration of the motor integrated power-train with an optimal controller suitable for small power-train based two wheeler automobile which can increase the system level efficiency without affecting drivability. This work will be the foundation for realizing the system in a production ready vehicle for the two wheeler OEM TVS Motor Company in India. A detailed power-train model is developed (from first principles) for the scooter vehicle, which is powered by a 110 cc spark ignition (SI) engine and coupled with two types of transmission, a continuous variable transmission (CVT) and a 4-speed manual transmission (MT). Both models are capable of simulating torque and NOx emission output of the SI engine and dynamic response of the full power-train. The torque production and emission outputs of the model are compared with experimental results available from TVS Motor Company. The CVT gear ratio model is developed using an indirect method and an analytical model. Both types of powertrain models are applied to perform a simulated study of fuel consumption, NOx emission and drivability study for a particular vehicle platform. In the next stage of work, the mathematical model for a brush-less direct current machine (BLDC) with the drive system and Li-Ion battery are developed. The models are verified and calibrated with the experimental results from TVS Motor Company. The BLDC machine is integrated with both the CVT and MT powertrain models in parallel hybrid configurations and a drive cycle simulation is conducted for different static assist levels by the electrical machines. The initial test confirms the need of optimal sizing of the powertrain components as well as an optimal control system. The detailed model of the powertrain is converted to a control-oriented model which is suitable for optimal control. This is followed by multi-objective optimization of different components of the motor-integrated powertrain using a single function as well as Pareto-Optimal methods. The objective function for the multi-objective optimization is proposed to reduce the fuel consumption with battery charge sustainability with least impact on the increase of financial cost and weight of the vehicle. The optimization is conducted by a nested methodology that involves Particle Swarm Optimization and a Non-dominated sorting genetic algorithm where, concurrently, a global optimal control is developed corresponding to the multi-objective design. The global optimal controller is designed using dynamic programming. The research is concluded with an optimal controller developed using the hp-collocation method. The objective function of the dynamic programming method and hp-collocation method is proposed to reduce fuel consumption with battery charge sustainability.Open Acces

Multi-Objective Iterative Learning Control: An Advanced ILC Approach for Application Diversity.

While ILC has been applied to repetitive applications in manufacturing, chemical processing, and robotics, several key assumptions limit the extension of ILC to various applications. Conventional ILC focuses on improving the performance of a single metric, such as tracking performance through iterative updates of the time domain control input. The application range is limited to systems that satisfy the assumption of iteration invariance of the plant, reference signal, initial conditions, and disturbances. We aim to relax this assumption to gain significant advantages. More specifically we focus on relaxing the strict reference tracking requirement to address multiple performance metrics and define the stability bounds across temporal and spatial domains. The aim of this research is expanding the application space of ILC towards non-traditional applications. Chapter III presents an initial framework to provide the foundation for the multi-objective ILC. This framework is validated by simulation and experimental tests with a wheeled mobile robot. Chapter IV extends the initial framework from the temporal domain to the spatial domain. The initial framework is generalized to address four classifications of performance objectives. Stability and performance analysis for each classification is provided. Simulation results on a high-resolution additive manufacturing system validate the extended framework. For the generalized framework, we present a distributed approach in which additional objectives are considered separately. Chapter V evaluates the difference between this distributed approach, and a centralized approach in which the objectives are combined into a single matrix depending on the classification. Chapter VI extends the multi-objective ILC to incorporate a region-based tracking problem in which reference uncertainty is addressed through the development of a bounded region. A multi-objective region-to-region ILC is developed and validated by a simulation of a surveillance problem with an UAV and multiple unattended ground sensors. Comparisons with point-to-point ILC, region-to-region ILC, and multi-objective region-based ILC demonstrate the performance flexibility that can be achieved when leveraging the regions. This dissertation provides new approaches for relaxing the classical assumption of iteration invariant reference tracking. New stability and convergence analysis is provided, resulting in a design methodology for multi-objective ILC. These approaches are validated by simulation and experimental results.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120875/1/ingyulim_1.pd