Control of autonomous multibody vehicles using artificial intelligence

The field of autonomous driving has been evolving rapidly within the last few years and a lot of research has been dedicated towards the control of autonomous vehicles, especially car-like ones. Due to the recent successes of artificial intelligence techniques, even more complex problems can be solved, such as the control of autonomous multibody vehicles. Multibody vehicles can accomplish transportation tasks in a faster and cheaper way compared to multiple individual mobile vehicles or robots. But even for a human, driving a truck-trailer is a challenging task. This is because of the complex structure of the vehicle and the maneuvers that it has to perform, such as reverse parking to a loading dock. In addition, the detailed technical solution for an autonomous truck is challenging and even though many single-domain solutions are available, e.g. for pathplanning, no holistic framework exists. Also, from the control point of view, designing such a controller is a high complexity problem, which makes it a widely used benchmark. In this thesis, a concept for a plurality of tasks is presented. In contrast to most of the existing literature, a holistic approach is developed which combines many stand-alone systems to one entire framework. The framework consists of a plurality of modules, such as modeling, pathplanning, training for neural networks, controlling, jack-knife avoidance, direction switching, simulation, visualization and testing. There are model-based and model-free control approaches and the system comprises various pathplanning methods and target types. It also accounts for noisy sensors and the simulation of whole environments. To achieve superior performance, several modules had to be developed, redesigned and interlinked with each other. A pathplanning module with multiple available methods optimizes the desired position by also providing an efficient implementation for trajectory following. Classical approaches, such as optimal control (LQR) and model predictive control (MPC) can safely control a truck with a given model. Machine learning based approaches, such as deep reinforcement learning, are designed, implemented, trained and tested successfully. Furthermore, the switching of the driving direction is enabled by continuous analysis of a cost function to avoid collisions and improve driving behavior. This thesis introduces a working system of all integrated modules. The system proposed can complete complex scenarios, including situations with buildings and partial trajectories. In thousands of simulations, the system using the LQR controller or the reinforcement learning agent had a success rate of >95 % in steering a truck with one trailer, even with added noise. For the development of autonomous vehicles, the implementation of AI at scale is important. This is why a digital twin of the truck-trailer is used to simulate the full system at a much higher speed than one can collect data in real life.Tesi

Sensor Reduction for Backing-Up Control of a Vehicle With Triple Trailers

This paper presents a cost-effective design based on sensor reduction for backing-up control of a vehicle with triple trailers. To realize a cost-effective design, we newly derive two linear-matrix-inequality (LMI) conditions for a discrete Takagi-Sugeno fuzzy system. One is an optimal dynamic output feedback design that guarantees desired control performance. The other is an avoidance of jackknife phenomenon for the use of the optimal dynamic output feedback controller. Our results demonstrate that the proposed LMI-based design effectively achieves the backing-up control of the vehicle with triple trailers while avoiding the jackknife phenomenon. More importantly, we demonstrate that the designed optimal control can achieve the backing-up control without, at least, two potentiometers that were employed to measure the relative angles (of a vehicle with triple trailers) in our previous experiments. Since the relative angles directly relate to the jackknife phenomenon, the successful control results without two potentiometers are very interesting and important from the cost-effective design point of view

Multiobjective control of a vehicle with triple trailers

We consider the backing-up control of a vehicle with triple trailers using a model-based fuzzy-control methodology. First, the vehicle model is represented by a Takagi-Sugeno fuzzy model. Then, we employ the so-called "parallel distributed compensation" design to arrive at a controller that guarantees the stability of the closed-loop system consisted of the fuzzy model and controller. The control-design problem is cast in terms of linear matrix inequalities (LMIs). In addition to stability, the control performance considerations such as decay rate, constraints on input and output, and disturbance rejection are incorporated in the LMI conditions. In application to the vehicle with triple trailers setup, we utilize these LMI conditions to explicitly avoid the saturation of the steering angle and the jackknife phenomenon in the control design. Both simulation and experimental results are presented. Our results demonstrate that the fuzzy controller effectively achieves the backing-up control of the vehicle with triple trailers while avoiding the saturation of the actuator and "jackknife" phenomenon

Autonomous Control of a Scale Model of a Trailer-Truck using an Obstacle-Avoidance Path-Planning Hierarchy

A scale model of a tractor-trailer truck was developed as a testbed for control algorithms. The truck operates in autonomous or semi-autonomous modes. An on-board Pentium computer with a PC104 bus performs the computations and data collection. Various sensors and a wireless transceiver are on-board the truck. Our research focus has been in the autonomous control of vehicles using intelligent systems. For this document we have employed a multi-resolutional hierarchy to plan a path for the tractor-trailer truck. The hierarchy starts with a simple path then warps it around obstacles. The modular construction of the hierarchy allows more intelligent agents to perform some of tasks. The current system has some limitations as to the placement of obstacles, however, it is an extremely fast algorithm and is able to handle some motion of the obstacles

ANN Controller Design for Lime Kiln Process

The lime kiln is a very complex multivariable process with severe non linearities, high degree of coupling and frequent disturbances. In this paper a 2x2 lime kiln process with two manipulated variables namely the fuel gas flowrate, and the percent opening of the induced draft damper and two controlled variables namely front end temperature and back end temperature has been considered. After its decoupling, artificial neural network (ANN) controllers have been designed to control the front end temperature. The performance of ANN controllers have been compared with that of conventional controllers

ANN Controller Design for Lime Kiln Process

The lime kiln is a very complex multivariable process with severe non linearities, high degree of coupling and frequent disturbances. In this paper a 2x2 lime kiln process with two manipulated variables namely the fuel gas flowrate, and the percent opening of the induced draft damper and two controlled variables namely front end temperature and back end temperature has been considered. After its decoupling, artificial neural network (ANN) controllers have been designed to control the front end temperature. The performance of ANN controllers have been compared with that of conventional controllers

Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks

Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations, paths are generated to loading docks with arbitrary pose using Dubins Curves. The combination vehicles can be varied in wheelbase, hitch length, weight distributions, and tire cornering stiffness. The closed form calculation of the gains for the Linear Quadratic Regulator (LQR) rely heavily on having an accurate model of the plant. However, real-world applications cannot expect to have an updated model for each new trailer. Finding alternative robust controllers when the trailer model is changed was the motivation of this research. Reinforcement learning, with neural networks as their function approximators, can allow for generalized control from its learned experience that is characterized by a scalar reward value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering the kinematic model trailer wheelbase, hitch length, and velocity from the nominal case. In order to close the gap from simulation and reality, the control methods are also assessed with sensor noise and various controller frequencies. The root mean squared and maximum errors from the path are used as metrics, including the number of times the controllers cause the vehicle to jackknife or reach the goal. Considering the runs where the LQR did not cause the trailer to jackknife, the LQR tended to have slightly better precision. DDPG, however, controlled the trailer successfully on the paths where the LQR jackknifed. Reinforcement learning was found to sacrifice a short term reward, such as precision, to maximize the future expected reward like reaching the loading dock. The reinforcement learning agent learned a policy that imposed nonlinear constraints such that it never jackknifed, even when it wasn\u27t the trailer it trained on

Diseño de un sistema de control de tráiler autónomo

The present work defines a control design, which consists of integrating two techniques: linear LQR and a neuro-diffuse network, in such a way that this hybrid system provides a wide working range for the trailer to follow any trajectory in advance and recoil directions simulating the real conditions of human driving. It is also proposed the monitoring of any trajectory designing a general method to calculate the desired values of the system states, in such a way that, by simply defining a mathematical function of the route to follow, the values for the control of the trailer type robot are known. Favourable results of the system were achieved and applied in a controlled environment.El presente trabajo define un diseño de control que consiste en integrar dos técnicas: una lineal LQR y una red neurodifusa, de tal manera que este sistema híbrido brinde un rango de trabajo amplio para que el tráiler siga cualquier trayectoria en direcciones de avance y retroceso simulando las reales condiciones de una conducción humana. Se propone también el seguimiento de cualquier trayectoria mediante el diseño de un método general para calcular los valores deseados de los estados del sistema, de tal manera que, con solo definir una función matemática de la ruta que se va a seguir, se conozcan los valores para el control del robot tipo tráiler. Se lograron resultados favorables del sistema aplicándolo en un ambiente controlado