585 research outputs found
Distributed model predictive control of leader-follower systems using an interior point method with efficient computations
Standard model predictive control strategies imply the online computation of
control inputs at each sampling instance, which traditionally limits this type
of control scheme to systems with slow dynamics. This paper focuses on
distributed model predictive control for large-scale systems comprised of
interacting linear subsystems, where the online computations required for the
control input can be distributed amongst them. A model predictive controller
based on a distributed interior point method is derived, for which every
subsystem in the network can compute stabilizing control inputs using
distributed computations. We introduce local terminal sets and cost functions,
which together satisfy distributed invariance conditions for the whole system,
that guarantees stability of the closed-loop interconnected system. We show
that the synthesis of both terminal sets and terminal cost functions can be
done in a distributed framework.Comment: 8 pages, Partially Accepted in the Proceedings of the 2013 American
Control Conferenc
Distributed eco-driving control of a platoon of electric vehicles through Riccati recursion
This paper presents a distributed optimization procedure for the cooperative eco-driving control problem of a platoon of electric vehiclessubject to safety and travel time constraints. Individual optimal trajectories are generated for each platoon member to account for heterogeneous vehicles and for the road slope. By rearranging the problem variables, the Riccati recursion can be applied along the chain-like structure of the platoon and be used to solve the problem by repeatedly transmitting information up and down the platoon. Since each vehicle is only responsible for its own part of the computations, the proposed control strategy is privacy-preserving and could therefore be deployed by any group of vehicles to form a platoon spontaneously while driving. The energy efficiency of this control strategy is evaluated in numerical experiments for platoons of electric trucks with different masses and rated motor powers
Distributed eco-driving control of a platoon of electric vehicles through Riccati recursion
This paper presents a distributed optimization procedure for the cooperative eco-driving control problem of a platoon of electric vehiclessubject to safety and travel time constraints. Individual optimal trajectories are generated for each platoon member to account for heterogeneous vehicles and for the road slope. By rearranging the problem variables, the Riccati recursion can be applied along the chain-like structure of the platoon and be used to solve the problem by repeatedly transmitting information up and down the platoon. Since each vehicle is only responsible for its own part of the computations, the proposed control strategy is privacy-preserving and could therefore be deployed by any group of vehicles to form a platoon spontaneously while driving. The energy efficiency of this control strategy is evaluated in numerical experiments for platoons of electric trucks with different masses and rated motor powers
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Iterative Algorithms for Distributed Optimization with Applications to Multi-Agent Estimation and Control
Optimization is a prevalent tool in control and estimation. This work explores the theoretical and practical challenges in the design and analysis of distributed algorithms to solve optimization problems related to multi-agent systems.We begin by considering a problem related to parameter estimation in sensor networks. We show that the maximum likelihood estimation formulation of several localization problems based on inter-sensor measurements reduces to the form of a common constrained optimization. We then design a distributed algorithm that utilizes only the most recent measurements and the estimates from the neighboring sensors, to iteratively compute the optimal solution. Our analysis shows that the solutions obtained from this algorithm converge locally to the maximum likelihood estimates, nevertheless simulations show that this convergence may occur globally. Furthermore, in experimental results using custom ultra-wideband radio frequency devices, this algorithm outperformed other distributed methods tested for a given localization problem.Next, we consider a multi-agent coordination problem formulated as a finite horizon optimization of the type used in model predictive control. We present two distributed and iterative algorithms, in which each agent is assigned a cost function, which it optimizes to compute its own control action. These cost functions depend on the states and the estimates of the control variables of the agents' neighbors, which are obtained through inter-agent communication. For the first algorithm, the agents are able to receive estimates from 2-hop neighbors, whereas the second algorithm utilizes only 1-hop neighbor information. For the first algorithm, our results show that the local solutions converge to the solution of the original model predictive control problem, regardless of how the algorithm is initialized. Because this convergence is asymptotic, we derive practical conditions for terminating the algorithm in a finite number of iterations, such that the closed-loop system achieves the desired coordination. For the second algorithm, due to more restrictive constraints, the convergence occurs to suboptimal solutions of the model predictive control problem. Nevertheless, simulations demonstrate that the optimality gap is small, and in some cases zero.A key takeaway from these results is that in many problems of multi-agent systems, the communication between the agents can be leveraged to design distributed algorithms that match the quality of solutions that one would obtain from centralized approaches
State-Dependent Dynamic Tube MPC: A Novel Tube MPC Method with a Fuzzy Model of Disturbances
Most real-world systems are affected by external disturbances, which may be
impossible or costly to measure. For instance, when autonomous robots move in
dusty environments, the perception of their sensors is disturbed. Moreover,
uneven terrains can cause ground robots to deviate from their planned
trajectories. Thus, learning the external disturbances and incorporating this
knowledge into the future predictions in decision-making can significantly
contribute to improved performance. Our core idea is to learn the external
disturbances that vary with the states of the system, and to incorporate this
knowledge into a novel formulation for robust tube model predictive control
(TMPC). Robust TMPC provides robustness to bounded disturbances considering the
known (fixed) upper bound of the disturbances, but it does not consider the
dynamics of the disturbances. This can lead to highly conservative solutions.
We propose a new dynamic version of robust TMPC (with proven robust stability),
called state-dependent dynamic TMPC (SDD-TMPC), which incorporates the dynamics
of the disturbances into the decision-making of TMPC. In order to learn the
dynamics of the disturbances as a function of the system states, a fuzzy model
is proposed. We compare the performance of SDD-TMPC, MPC, and TMPC via
simulations, in designed search-and-rescue scenarios. The results show that,
while remaining robust to bounded external disturbances, SDD-TMPC generates
less conservative solutions and remains feasible in more cases, compared to
TMPC.Comment: 39 pages, 16 figures, 4 tables, 2 appendices, to be submitted to
"international journal of robust and nonlinear control", [40] from paper
cites our code to be submitted
Model Predictive Control Applications to Spacecraft Rendezvous and Small Bodies Exploration
The overarching goal of this thesis is the design of model predictive control algorithms for
spacecraft proximity operations. These include, but it is not limited to, spacecraft rendezvous,
hovering phases or orbiting in the vicinity of small bodies. The main motivation
behind this research is the increasing demand of autonomy, understood as the spacecraft
capability to compute its own control plan, in current and future space operations. This
push for autonomy is fostered by the recent introduction of disruptive technologies changing
the traditional concept of space exploration and exploitation. The development of miniaturized
satellite platforms and the drastic cost reduction in orbital access have boosted space
activity to record levels. In the near future, it is envisioned that numerous artificial objects
will simultaneously operate across the Solar System. In that context, human operators will
be overwhelmed in the task of tracking and commanding each spacecraft in real time. As a
consequence, developing intelligent and robust autonomous systems has been identified by
several space agencies as a cornerstone technology.
Inspired by the previous facts, this work presents novel controllers to tackle several scenarios
related to spacecraft proximity operations. Mastering proximity operations enables
a wide variety of space missions such as active debris removal, astronauts transportation,
flight-formation applications, space stations resupply and the in-situ exploration of small
bodies. Future applications may also include satellite inspection and servicing. This thesis
has focused on four scenarios: six-degrees of freedom spacecraft rendezvous; near-rectilinear
halo orbits rendezvous; the hovering phase; orbit-attitude station-keeping in the vicinity of a
small body. The first problem aims to demonstrate rendezvous capabilities for a lightweight
satellite with few thrusters and a reaction wheels array. For near-rectilinear halo orbits
rendezvous, the goal is to achieve higher levels of constraints satisfaction than with a stateof-
the-art predictive controller. In the hovering phase, the objective is to augment the
control accuracy and computational efficiency of a recent global stable controller. The small
body exploration aims to demonstrate the positive impact of model-learning in the control
accuracy. Although based on model predictive control, the specific approach for each scenario differs.
In six-degrees of freedom rendezvous, the attitude flatness property and the transition
matrix for Keplerian-based relative are used to obtain a non-linear program. Then, the control
loop is closed by linearizing the system around the previous solution. For near-rectilinear
halo orbits rendezvous, the constraints are assured to be satisfied in the probabilistic sense by
a chance-constrained approach. The disturbances statistical properties are estimated on-line.
For the hovering phase problem, an aperiodic event-based predictive controller is designed.
It uses a set of trigger rules, defined using reachability concepts, deciding when to execute a
single-impulse control. In the small body exploration scenario, a novel learning-based model
predictive controller is developed. This works by integrating unscented Kalman filtering and
model predictive control. By doing so, the initially unknown small body inhomogeneous
gravity field is estimated over time which augments the model predictive control accuracy.El objeto de esta tesis es el dise˜no de algoritmos de control predictivo basado en modelo
para operaciones de veh´ıculos espaciales en proximidad. Esto incluye, pero no se limita, a
la maniobra de rendezvous, las fases de hovering u orbitar alrededor de cuerpos menores.
Esta tesis est´a motivada por la creciente demanda en la autonom´ıa, entendida como la capacidad
de un veh´ıculo para calcular su propio plan de control, de las actuales y futuras
misiones espaciales. Este inter´es en incrementar la autonom´ıa est´a relacionado con las actuales
tecnolog´ıas disruptivas que est´an cambiando el concepto tradicional de exploraci´on y
explotaci´on espacial. Estas son el desarrollo de plataformas satelitales miniaturizadas y la
dr´astica reducci´on de los costes de puesta en ´orbita. Dichas tecnolog´ıas han impulsado la
actividad espacial a niveles de record. En un futuro cercano, se prev´e que un gran n´umero de
objetos artificiales operen de manera simult´anea a lo largo del Sistema Solar. Bajo dicho escenario,
los operadores terrestres se ver´an desbordados en la tarea de monitorizar y controlar
cada sat´elite en tiempo real. Es por ello que el desarrollo de sistemas aut´onomos inteligentes
y robustos es considerado una tecnolog´ıa fundamental por diversas agencias espaciales.
Debido a lo anterior, este trabajo presenta nuevos resultados en el control de operaciones
de veh´ıculos espaciales en proximidad. Dominar dichas operaciones permite llevar a cabo
una gran variedad de misiones espaciales como la retirada de basura espacial, transferir
astronautas, aplicaciones de vuelo en formaci´on, reabastecer estaciones espaciales y la exploraci
´on de cuerpos menores. Futuras aplicaciones podr´ıan incluir operaciones de inspecci´on y
mantenimiento de sat´elites. Esta tesis se centra en cuatro escenarios: rendezvous de sat´elites
con seis grados de libertad; rendezvous en ´orbitas halo cuasi-rectil´ıneas; la fase de hovering;
el mantenimiento de ´orbita y actitud en las inmendiaciones de un cuerpo menor. El primer
caso trata de proveer capacidades de rendezvous para un sat´elite ligero con pocos propulsores
y un conjunto de ruedas de reacci´on. Para el rendezvous en ´orbitas halo cuasi-rectil´ıneas, se
intenta aumentar el grado de cumplimiento de restricciones con respecto a un controlador
predictivo actual. Para la fase de hovering, se mejora la precisi´on y eficiencia computacional
de un controlador globalmente estable. En la exploraci´on de un cuerpo menor, se pretende
demostrar el mayor grado de precisi´on que se obtiene al aprender el modelo.
Siendo la base el control predictivo basado en modelo, el enfoque espec´ıfico difiere para
cada escenario. En el rendezvous con seis grados de libertad, se obtiene un programa no-lineal
con el uso de la propiedad flatness de la actitud y la matriz de transici´on del movimiento
relativo Kepleriano. El bucle de control se cierra linealizando en torno a la soluci´on anterior.
Para el rendezvous en ´orbitas halo cuasi-rectil´ıneas, el cumplimiento de restricciones
se garantiza probabil´ısticamente mediante la t´ecnica chance-constrained. Las propiedades
estad´ısticas de las perturbaciones son estimadas on-line. En la fase de hovering, se usa el
control predictivo basado en eventos. Ello consiste en unas reglas de activaci´on, definidas
con conceptos de accesibilidad, que deciden la ejecuci´on de un ´unico impulso de control.
En la exploraci´on de cuerpos menores, se desarrolla un controlador predictivo basado en el
aprendizaje del modelo. Funciona integrando un filtro de Kalman con control predictivo
basado en modelo. Con ello, se consigue estimar las inomogeneidades del campo gravitario
lo que repercute en una mayor precisi´on del controlador predictivo basado en modelo
Optimization based solutions for control and state estimation in non-holonomic mobile robots: stability, distributed control, and relative localization
Interest in designing, manufacturing, and using autonomous robots has been rapidly growing
during the most recent decade. The main motivation for this interest is the wide range
of potential applications these autonomous systems can serve in. The applications include,
but are not limited to, area coverage, patrolling missions, perimeter surveillance, search
and rescue missions, and situational awareness. In this thesis, the area of control and
state estimation in non-holonomic mobile robots is tackled. Herein, optimization based
solutions for control and state estimation are designed, analyzed, and implemented to such
systems. One of the main motivations for considering such solutions is their ability of
handling constrained and nonlinear systems such as non-holonomic mobile robots. Moreover,
the recent developments in dynamic optimization algorithms as well as in computer
processing facilitated the real-time implementation of such optimization based methods
in embedded computer systems.
Two control problems of a single non-holonomic mobile robot are considered first; these
control problems are point stabilization (regulation) and path-following. Here, a model
predictive control (MPC) scheme is used to fulfill these control tasks. More precisely, a
special class of MPC is considered in which terminal constraints and costs are avoided.
Such constraints and costs are traditionally used in the literature to guarantee the asymptotic
stability of the closed loop system. In contrast, we use a recently developed stability
criterion in which the closed loop asymptotic stability can be guaranteed by appropriately
choosing the prediction horizon length of the MPC controller. This method is based on finite time controllability as well as bounds on the MPC value function.
Afterwards, a regulation control of a multi-robot system (MRS) is considered. In this
control problem, the objective is to stabilize a group of mobile robots to form a pattern.
We achieve this task using a distributed model predictive control (DMPC) scheme based
on a novel communication approach between the subsystems. This newly introduced
method is based on the quantization of the robots’ operating region. Therefore, the
proposed communication technique allows for exchanging data in the form of integers
instead of floating-point numbers. Additionally, we introduce a differential communication
scheme to achieve a further reduction in the communication load.
Finally, a moving horizon estimation (MHE) design for the relative state estimation
(relative localization) in an MRS is developed in this thesis. In this framework, robots
with less payload/computational capacity, in a given MRS, are localized and tracked
using robots fitted with high-accuracy sensory/computational means. More precisely,
relative measurements between these two classes of robots are used to localize the less
(computationally) powerful robotic members. As a complementary part of this study, the
MHE localization scheme is combined with a centralized MPC controller to provide an
algorithm capable of localizing and controlling an MRS based only on relative sensory
measurements. The validity and the practicality of this algorithm are assessed by realtime
laboratory experiments.
The conducted study fills important gaps in the application area of autonomous navigation
especially those associated with optimization based solutions. Both theoretical as
well as practical contributions have been introduced in this research work. Moreover, this
thesis constructs a foundation for using MPC without stabilizing constraints or costs in
the area of non-holonomic mobile robots
Formation control of autonomous vehicles with emotion assessment
Autonomous driving is a major state-of-the-art step that has the potential to transform the mobility of individuals and goods fundamentally. Most developed autonomous ground vehicles (AGVs) aim to sense the surroundings and control the vehicle autonomously with limited or no driver intervention. However, humans are a vital part of such vehicle operations. Therefore, an approach to understanding human emotions and creating trust between humans and machines is necessary. This thesis proposes a novel approach for multiple AGVs, consisting of a formation controller and human emotion assessment for autonomous driving and collaboration. As the interaction between multiple AGVs is essential, the performance of two multi-robot control algorithms is analysed, and a platoon formation controller is proposed. On the other hand, as the interaction between AGVs and humans is equally essential to create trust between humans and AGVs, the human emotion assessment method is proposed and used as feedback to make autonomous decisions for AGVs. A novel simulation platform is developed for navigating multiple AGVs and testing controllers to realise this concept. Further to this simulation tool, a method is proposed to assess human emotion using the affective dimension model and physiological signals such as an electrocardiogram (ECG) and photoplethysmography (PPG). The experiments are carried out to verify that humans' felt arousal and valence levels could be measured and translated to different emotions for autonomous driving operations. A per-subject-based classification accuracy is statistically significant and validates the proposed emotion assessment method. Also, a simulation is conducted to verify AGVs' velocity control effect of different emotions on driving tasks
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