5 research outputs found
Design, testing and validation of model predictive control for an unmanned ground vehicle
The rapid increase in designing, manufacturing, and using autonomous robots has attracted numerous researchers and industries in recent decades. The logical motivation behind this interest is the wide range of applications. For instance, perimeter surveillance, search and rescue missions, agriculture, and construction.
In this thesis, motion planning and control based on model predictive control (MPC) for unmanned ground vehicles (UGVs) is tackled. In addition, different variants of MPC are designed, analysed, and implemented for such non-holonomic systems. It is imperative to focus on the ability of MPC to handle constraints as one of the motivations. Furthermore, the proliferation of computer processing enables these systems to work in a real-time scenario. The controller's responsibility is to guarantee an accurate trajectory tracking process to deal with other specifications usually not considered or solved by the planner. However, the separation between planner and controller is not necessarily defined uniquely, even though it can be a hybrid process, as seen in part of this thesis.
Firstly, a robust MPC is designed and implemented for a small-scale autonomous bulldozer in the presence of uncertainties, which uses an optimal control action and a feed-forward controller to suppress these uncertainties. More precisely, a linearised variant of MPC is deployed to solve the trajectory tracking problem of the vehicle. Afterwards, a nonlinear MPC is designed and implemented to solve the path-following problem of the UGV for masonry in a construction context, where longitudinal velocity and yaw rate are employed as control inputs to the platform. For both the control techniques, several experiments are performed to validate the robustness and accuracy of the proposed scheme. Those experiments are performed under realistic localisation accuracy, provided by a typical localiser.
Most conspicuously, a novel proximal planning and control strategy is implemented in the presence of skid-slip and dynamic and static collision avoidance for the posture control and tracking control problems. The ability to operate in moving objects is critical for UGVs to function well. The approach offers specific planning capabilities, able to deal at high frequency with context characteristics, which the higher-level planner may not well solve. Those context characteristics are related to dynamic objects and other terrain details detected by the platform's onboard perception capabilities. In the control context, proximal and interior-point optimisation methods are used for MPC. Relevant attention is given to the processing time required by the MPC process to obtain the control actions at each actual control time. This concern is due to the need to optimise each control action, which must be calculated and applied in real-time. Because the length of a prediction horizon is critical in practical applications, it is worth looking into in further detail. In another study, the accuracies of robust and nonlinear model predictive controllers are compared.
Finally, a hybrid controller is proposed and implemented. This approach exploits the availability of a simplified cost-to-go function (which is provided by a higher-level planner); thus, the hybrid approach fuses, in real-time, the nominal CTG function (nominal terrain map) with the rest of the critical constraints, which the planner usually ignores. The conducted research fills necessary gaps in the application areas of MPC and UGVs. Both theoretical and practical contributions have been made in this thesis. Moreover, extensive simulations and experiments are performed to test and verify the working of MPC with a reasonable processing capability of the onboard process
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
Aerial Vehicles
This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space
Fuzzy Techniques for Decision Making 2018
Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches