6 research outputs found

    Control of Constrained Dynamical Systems with Performance Guarantees: With Application to Vehicle motion Control

    Get PDF
    In control engineering, models of the system are commonly used for controller design. A standard control design problem consists of steering the given system output (or states) towards a predefined reference. Such a problem can be solved by employing feedback control strategies. By utilizing the knowledge of the model, these strategies compute the control inputs that shrink the error between the system outputs and their desired references over time. Usually, the control inputs must be computed such that the system output signals are kept in a desired region, possibly due to design or safety requirements. Also, the input signals should be within the physical limits of the actuators. Depending on the constraints, their violation might result in unacceptable system failures (e.g. deadly injury in the worst case). Thus, in safety-critical applications, a controller must be robust towards the modelling uncertainties and provide a priori guarantees for constraint satisfaction. A fundamental tool in constrained control application is the robust control invariant sets (RCI). For a controlled dynamical system, if initial states belong to RCI set, control inputs always exist that keep the future state trajectories restricted within the set. Hence, RCI sets can characterize a system that never violates constraints. These sets are the primary ingredient in the synthesis of the well-known constraint control strategies like model predictive control (MPC) and interpolation-based controller (IBC). Consequently, a large body of research has been devoted to the computation of these sets. In the thesis, we will focus on the computation of RCI sets and the method to generate control inputs that keep the system trajectories within RCI set. We specifically focus on the systems which have time-varying dynamics and polytopic constraints. Depending upon the nature of the time-varying element in the system description (i.e., if they are observable or not), we propose different sets of algorithms.The first group of algorithms apply to the system with time-varying, bounded uncertainties. To systematically handle the uncertainties and reduce conservatism, we exploit various tools from the robust control literature to derive novel conditions for invariance. The obtained conditions are then combined with a newly developed method for volume maximization and minimization in a convex optimization problem to compute desirably large and small RCI sets. In addition to ensuring invariance, it is also possible to guarantee desired closed-loop performance within the RCI set. Furthermore, developed algorithms can generate RCI sets with a predefined number of hyper-planes. This feature allows us to adjust the computational complexity of MPC and IBC controller when the sets are utilized in controller synthesis. Using numerical examples, we show that the proposed algorithms can outperform (volume-wise) many state-of-the-art methods when computing RCI sets.In the other case, we assume the time-varying parameters in system description to be observable. The developed algorithm has many similar characteristics as the earlier case, but now to utilize the parameter information, the control law and the RCI set are allowed to be parameter-dependent. We have numerically shown that the presented algorithm can generate invariant sets which are larger than the maximal RCI sets computed without exploiting parameter information.Lastly, we demonstrate how we can utilize some of these algorithms to construct a computationally efficient IBC controller for the vehicle motion control. The devised IBC controller guarantees to meet safety requirements mentioned in ISO 26262 and the ride comfort requirement by design

    Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning

    Full text link
    We develop a real-time feasible mixed-integer programming-based decision making (MIP-DM) system for automated driving. Using a linear vehicle model in a road-aligned coordinate frame, the lane change constraints, collision avoidance and traffic rules can be formulated as mixed-integer inequalities, resulting in a mixed-integer quadratic program (MIQP). The proposed MIP-DM simultaneously performs maneuver selection and trajectory generation by solving the MIQP at each sampling time instant. While solving MIQPs in real time has been considered intractable in the past, we show that our recently developed solver BB-ASIPM is capable of solving MIP-DM problems on embedded hardware in real time. The performance of this approach is illustrated in simulations in various scenarios including merging points and traffic intersections, and hardware-in-the-loop simulations on dSPACE Scalexio and MicroAutoBox-III. Finally, we present results from hardware experiments on small-scale automated vehicles.Comment: 14 pages, 11 figures, 3 tables, submitted to IEEE Transactions on Control Systems Technolog

    Real-Time Collision Imminent Steering Using One-Level Nonlinear Model Predictive Control

    Full text link
    Automotive active safety features are designed to complement or intervene a human driver's actions in safety critical situations. Existing active safety features, such as adaptive cruise control and lane keep assist, are able to exploit the ever growing sensor and computing capabilities of modern automobiles. An emerging feature, collision imminent steering, is designed to perform an evasive lane change to avoid collision if the vehicle believes collision cannot be avoided by braking alone. This is a challenging maneuver, as the expected highway setting is characterized by high speeds, narrow lane restrictions, and hard safety constraints. To perform such a maneuver, the vehicle may be required to operate at the nonlinear dynamics limits, necessitating advanced control strategies to enforce safety and drivability constraints. This dissertation presents a one-level nonlinear model predictive controller formulation to perform a collision imminent steering maneuver in a highway setting at high speeds, with direct consideration of safety criteria in the highway environment and the nonlinearities characteristic of such a potentially aggressive maneuver. The controller is cognizant of highway sizing constraints, vehicle handling capability and stability limits, and time latency when calculating the control action. In simulated testing, it is shown the controller can avoid collision by conducting a lane change in roughly half the distance required to avoid collision by braking alone. In preliminary vehicle testing, it is shown the control formulation is compatible with the existing perception pipeline, and prescribed control action can safely perform a lane change at low speed. Further, the controller must be suitable for real-time implementation and compatible with expected automotive control architecture. Collision imminent steering, and more broadly collision avoidance, control is a computationally challenging problem. At highway speeds, the required time for action is on the order of hundreds of milliseconds, requiring a control formulation capable of operating at tens of Hertz. To this extent, this dissertation investigates the computational expense of such a controller, and presents a framework for designing real-time compatible nonlinear model predictive controllers. Specifically, methods for numerically simulating the predicted vehicle response and response sensitivities are compared, their cross interaction with trajectory optimization strategy are considered, and the resulting mapping to a parallel computing hardware architecture is investigated. The framework systematically evaluates the underlying numerical optimization problem for bottlenecks, from which it provides alternative solutions strategies to achieve real-time performance. As applied to the baseline collision imminent steering controller, the procedure results in an approximate three order of magnitude reduction in compute wall time, supporting real-time performance and enabling preliminary testing on automotive grade hardware.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163063/1/jbwurts_1.pd

    Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving)

    Full text link
    [EN] Nowadays, there are many electronic products that incorporate elements and features coming from the research in the field of mobile robotics. For instance, the well-known vacuum cleaning robot Roomba by iRobot, which belongs to the field of service robotics, one of the most active within the sector. There are also numerous autonomous robotic systems in industrial warehouses and plants. It is the case of Autonomous Guided Vehicles (AGVs), which are able to drive completely autonomously in very structured environments. Apart from industry and consumer electronics, within the automotive field there are some devices that give intelligence to the vehicle, derived in most cases from advances in mobile robotics. In fact, more and more often vehicles incorporate Advanced Driver Assistance Systems (ADAS), such as navigation control with automatic speed regulation, lane change and overtaking assistant, automatic parking or collision warning, among other features. However, despite all the advances there are some problems that remain unresolved and can be improved. Collisions and rollovers stand out among the most common accidents of vehicles with manual or autonomous driving. In fact, it is almost impossible to guarantee driving without accidents in unstructured environments where vehicles share the space with other moving agents, such as other vehicles and pedestrians. That is why searching for techniques to improve safety in intelligent vehicles, either autonomous or manual-assisted driving, is still a trending topic within the robotics community. This thesis focuses on the design of tools and techniques for planning and control of intelligent vehicles in order to improve safety and comfort. The dissertation is divided into two parts, the first one on autonomous driving and the second one on manual-assisted driving. The main link between them is the use of clothoids as mathematical formulation for both trajectory generation and collision detection. Among the problems solved the following stand out: obstacle avoidance, rollover avoidance and advanced driver assistance to avoid collisions with pedestrians.[ES] En la actualidad se comercializan infinidad de productos de electr贸nica de consumo que incorporan elementos y caracter铆sticas procedentes de avances en el sector de la rob贸tica m贸vil. Por ejemplo, el conocido robot aspirador Roomba de la empresa iRobot, el cual pertenece al campo de la rob贸tica de servicio, uno de los m谩s activos en el sector. Tambi茅n hay numerosos sistemas rob贸ticos aut贸nomos en almacenes y plantas industriales. Es el caso de los veh铆culos autoguiados (AGVs), capaces de conducir de forma totalmente aut贸noma en entornos muy estructurados. Adem谩s de en la industria y en electr贸nica de consumo, dentro del campo de la automoci贸n tambi茅n existen dispositivos que dotan de cierta inteligencia al veh铆culo, derivados la mayor铆a de las veces de avances en rob贸tica m贸vil. De hecho, cada vez con mayor frecuencia los veh铆culos incorporan sistemas avanzados de asistencia al conductor (ADAS por sus siglas en ingl茅s), tales como control de navegaci贸n con regulaci贸n autom谩tica de velocidad, asistente de cambio de carril y adelantamiento, aparcamiento autom谩tico o aviso de colisi贸n, entre otras prestaciones. No obstante, pese a todos los avances siguen existiendo problemas sin resolver y que pueden mejorarse. La colisi贸n y el vuelco destacan entre los accidentes m谩s comunes en veh铆culos con conducci贸n tanto manual como aut贸noma. De hecho, la dificultad de conducir en entornos desestructurados compartiendo el espacio con otros agentes m贸viles, tales como coches o personas, hace casi imposible garantizar la conducci贸n sin accidentes. Es por ello que la b煤squeda de t茅cnicas para mejorar la seguridad en veh铆culos inteligentes, ya sean de conducci贸n aut贸noma o manual asistida, es un tema que siempre est谩 en auge en la comunidad rob贸tica. La presente tesis se centra en el dise帽o de herramientas y t茅cnicas de planificaci贸n y control de veh铆culos inteligentes, para la mejora de la seguridad y el confort. La disertaci贸n se ha dividido en dos partes, la primera sobre conducci贸n aut贸noma y la segunda sobre conducci贸n manual asistida. El principal nexo de uni贸n es el uso de clotoides como elemento de generaci贸n de trayectorias y detecci贸n de colisiones. Entre los problemas que se resuelven destacan la evitaci贸n de obst谩culos, la evitaci贸n de vuelcos y la asistencia avanzada al conductor para evitar colisiones con peatones.[CA] En l'actualitat es comercialitzen infinitat de productes d'electr貌nica de consum que incorporen elements i caracter铆stiques procedents d'avan莽os en el sector de la rob貌tica m貌bil. Per exemple, el conegut robot aspirador Roomba de l'empresa iRobot, el qual pertany al camp de la rob貌tica de servici, un dels m茅s actius en el sector. Tamb茅 hi ha nombrosos sistemes rob貌tics aut貌noms en magatzems i plantes industrials. 脡s el cas dels vehicles autoguiats (AGVs), els quals s贸n capa莽os de conduir de forma totalment aut貌noma en entorns molt estructurats. A m茅s de en la ind煤stria i en l'electr貌nica de consum, dins el camp de l'automoci贸 tamb茅 existeixen dispositius que doten al vehicle de certa intel路lig猫ncia, la majoria de les vegades derivats d'avan莽os en rob貌tica m貌bil. De fet, cada vegada amb m茅s freq眉猫ncia els vehicles incorporen sistemes avan莽ats d'assist猫ncia al conductor (ADAS per les sigles en angl茅s), com ara control de navegaci贸 amb regulaci贸 autom脿tica de velocitat, assistent de canvi de carril i avan莽ament, aparcament autom脿tic o av铆s de col路lisi贸, entre altres prestacions. No obstant aix貌, malgrat tots els avan莽os segueixen existint problemes sense resoldre i que poden millorar-se. La col路lisi贸 i la bolcada destaquen entre els accidents m茅s comuns en vehicles amb conducci贸 tant manual com aut貌noma. De fet, la dificultat de conduir en entorns desestructurats compartint l'espai amb altres agents m貌bils, tals com cotxes o persones, fa quasi impossible garantitzar la conducci贸 sense accidents. 脡s per aix貌 que la recerca de t猫cniques per millorar la seguretat en vehicles intel路ligents, ja siguen de conducci贸 aut貌noma o manual assistida, 茅s un tema que sempre est脿 en auge a la comunitat rob貌tica. La present tesi es centra en el disseny d'eines i t猫cniques de planificaci贸 i control de vehicles intel路ligents, per a la millora de la seguretat i el confort. La dissertaci贸 s'ha dividit en dues parts, la primera sobre conducci贸 aut貌noma i la segona sobre conducci贸 manual assistida. El principal nexe d'uni贸 茅s l'煤s de clotoides com a element de generaci贸 de traject貌ries i detecci贸 de col路lisions. Entre els problemes que es resolen destaquen l'evitaci贸 d'obstacles, l'evitaci贸 de bolcades i l'assist猫ncia avan莽ada al conductor per evitar col路lisions amb vianants.Girb茅s Juan, V. (2016). Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving) [Tesis doctoral no publicada]. Universitat Polit猫cnica de Val猫ncia. https://doi.org/10.4995/Thesis/10251/65072TESI

    Fast and Safe Trajectory Optimization for Autonomous Mobile Robots using Reachability Analysis

    Full text link
    Autonomous mobile robots (AMRs) can transform a wide variety of industries including transportation, shipping and goods delivery, and defense. AMRs must match or exceed human performance in metrics for task completion and safety. Motion plans for AMRs are generated by solving an optimization program where collision avoidance and the trajectory obeying a dynamic model of the robot are enforced as constraints. This dissertation focuses on three main challenges associated with trajectory planning. First, collision checks are typically performed at discrete time steps. Second, there can be a nontrivial gap between the planning model used and the actual system. Finally, there is inherent uncertainty in the motion of other agents or robots. This dissertation first proposes a receding-horizon planning methodology called Reachability-based Trajectory Design (RTD) to address the first and second challenges, where uncertainty is dealt with robustly. Sums-of-Squares (SOS) programming is used to represent the forward reachable set for a dynamic system plus uncertainty, over an interval of time, as a polynomial level set. The trajectory optimization is a polynomial optimization program over a space of trajectory parameters. Hardware demonstrations are implemented on a Segway, rover, and electric vehicle. In a simulation of 1,000 trials with static obstacles, RTD is compared to Rapidly-exploring Random Tree (RRT) and Nonlinear Model Predictive Control (NMPC) planners. RTD has success rates of 95.4% and 96.3% for the Segway and rover respectively, compared to 97.6% and 78.2% for RRT and 0% for NMPC planners. RTD is the only successful planner with no collisions. In 10 simulations with a CarSim model, RTD navigates a test track on all trials. In 1,000 simulations with random dynamic obstacles RTD has success rates of 96.8% and 100% respectively for the electric vehicle and Segway, compared to 77.3% and 92.4% for a State Lattice planner. In 100 simulations performing left turns, RTD has a success rate of 99% compared to 80% for an MPC controller tracking the lane centerline. The latter half of the dissertation treats uncertainty with the second and/or third challenges probabilistically. The Chance-constrained Parallel Bernstein Algorithm (CCPBA) allows one to solve the trajectory optimization program from RTD when obstacle states are given as probability functions. A comparison for an autonomous vehicle planning a lane change with one obstacle shows an MPC algorithm using Cantelli's inequality is unable to find a solution when the obstacle's predictions are generated with process noise three orders of magnitude less than CCPBA. In environments with 1-6 obstacles, CCPBA finds solutions in 1e-3 to 1.2 s compared to 1 to 16 s for an NMPC algorithm using the Chernoff bound. A hardware demonstration is implemented on the Segway. The final portion of the dissertation presents a chance-constrained NMPC method where uncertain components of the robot model are estimated online. The application is an autonomous vehicle with varying road surfaces. In the first study, the controller uses a linear tire force model. Over 200 trials of lane changes at 17 m/s, the chance-constrained controller has a cost 86% less than a controller using fixed coefficients for snow, and only 29% more than an oracle controller using the simulation model. The chance-constrained controller also has 0 lateral position constraint violations, while an adaptive-only controller has minor violations. The second study uses nonlinear tire models on a more aggressive maneuver and provides similar results.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169729/1/skvaskov_1.pd
    corecore