Les Robots Parallèles à Câbles (RPC) sont particulièrement adaptés pour des applications telles que le transport de charges lourdes au travers de grands espaces de travail. Afin de contrôler l'ensemble des degrés de liberté de la plate-forme tout en optimisant la taille de l'espace de travail du robot par rapport au volume de sa structure, la redondance d'actionnement est nécessaire. Dans cette thèse, un algorithme de distribution des tensions des câbles compatible temps-réel est introduit. Il permet de calculer efficacement différentes solutions optimales au problème de la distribution des tensions des RPC à deux degrés de redondance. Des schémas de commande adaptés aux RPC, intégrant l'algorithme de distribution des tensions, sont ensuite proposés. Un schéma de commande en espace double est introduit pour compenser la dynamique de la plate-forme et des enrouleurs. Afin de pallier les incertitudes et les variations des paramètres des modèles, une commande adaptative en espace double est finalement proposée. Des résultats expérimentaux prouvent la compatibilité temps-réel des algorithmes et des lois de commande développés dans cette thèse, ainsi que leur stabilité le long de la trajectoire suivie. / Cable-driven parallel robots (CDPR) are particularly well adapted for some applications such as handling of heavy payloads over large workspaces. However, in order to fully control all the degrees of freedomof the mobile platformand to obtain large workspace to footprint ratios, redundant actuation may be required, which implies the determination of feasible cable tension distributions. In this thesis, in the case of CDPR with two degrees of actuation redundancy, real-time compatible algorithms capable of efficiently calculating various continuous tension distribution are introduced. Furthermore, efficient control schemes are proposed in order to increase the CDPR tracking performances. First, an dual-space feedforward control scheme is introduced to compensate for the plate-formeand whinches dynamics. In order to deal with parametric variations and incertainties in the models, an adaptive dual-space motion control scheme for CDPR is finally presented. Experimental results validate the reel-time efficiency of the proposed tension distribution algorithmand control schemes as well as their stability along the tracked trajectory.

This study assesses the viability of using a towfish mounted ADV for quantifying water velocity fluctuations in the Florida Current relevant to ocean current turbine performance. For this study a motion compensated ADV is operated in a test flume. Water velocity fluctuations are generated by a 1.3 cm pipe suspended in front of the ADV at relative current speeds of 0.9 m/s and 0.15 m/s, giving Reynolds numbers on the order of 1000. ADV pitching motion of +/- 2.5 [degree] at 0.3 Hz and a heave motion of 0.3 m amplitude at 0.2 Hz are utilized to evaluate the motion compensation approach. The results show correction for motion provides up to an order of magnitude reduction in turbulent kinetic energy at frequencies of motion while the IMU is found to generate 2% error at 1/30 Hz and 9% error at 1/60 Hz in turbulence intensity. / by James William Lovenbury. / Thesis (M.S.C.S.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

In this paper I describe a motion planning technique for intelligent ground vehicles. The technique is an implementation of a path selection algorithm based on fuzzy inference. The approach extends on the motion planning algorithm known as driving with tentacles. The selection of the tentacle (a drivable path) to follow relies on the calculation of a weighted cost function for each tentacle in the current speed set, and depends on variables such as the distance to the desired position, speed, and the closeness of a tentacle to any obstacles. A Matlab simulation and the practical implementation of the fuzzy inference rule on a Clearpath Husky robot within the Robot Operating System (ROS) framework are provided.

<p>U tezi je analizirana opravdanost primene FPGA tehnologije za razvoj upravljačkog sistema za linearne aktuatore. Realizovan je upravljački sistem za servo upravljanje linearnim pneumatskim aktuatorom, čiji rad je eksperimentalno proveren. Razvijeni su i algoritmi za detekciju opterećenosti aktuatora, kao i za detekciju prepreke na nepoznatoj poziciji korišćenjem metode analize promene pritiska u komorama pneumatskog cilindra.</p> / <p>This thesis discusses the possibilities of FPGA technology application in<br />the development of a control system for linear actuators. A control system<br />for servo control of linear pneumatic actuators was realized, and<br />experimentally tested. Furthermore, algorithms were developed for<br />detection of actuator load, as well as for detection of an obstacle in<br />unknown position, by analysing pressure change in the pneumatic<br />cylinder chambers.</p>

Exploiting high altitude wind energy using power kites is an emerging topic in the field of renewable energy. The claimed advantages of power kites over traditional wind power technologies are the lower construction costs, less land occupation and more importantly, the possibility of efficiently harvesting wind energy at high altitudes, where more dense and steady wind power exists. One of the most challenging issues to bring the power kite concept to real industrialization is the controller design. While traditional wind turbines can be inherently stabilized, the airborne nature of kites causes a strong instability of the systems. This thesis aims to develop a novel economic model predictive path-following control (EMPFC) framework to tackle the path-following control of power kites, as well as provide insightful stability analysis of the proposed control scheme. Chapter 3 is focused on the stability analysis of EMPFC. We proceed with a sampled-data EMPC scheme for set-point stabilization problems. An extended definition of dissipativity is introduced for continuous-time systems, followed by giving sufficient stability conditions. Then, the EMPFC scheme for output path-following problems is proposed. Sufficient conditions that guarantee the convergence of the system to the optimal operation on the reference path are derived. Finally, an example of a 2-DoF robot is given. The simulation results show that under the proposed EMPFC scheme, the robot can follow along the reference path in forward direction with enhanced economic performance, and finally converges to its optimal steady state. In Chapter 4, the proposed EMPFC scheme is applied to a challenging nonlinear kite model. By introducing additional degrees of freedom in the zero-error manifold (i.e., the space where the output error is zero), a relaxation of the optimal operation is achieved. The effectiveness of the proposed control scheme is shown in two aspects. For a static reference path, the generated power is increased while the kite is stabilized in the neighborhood of the reference path. For a dynamic reference path, the economic performance can be further enhanced since parameters for the reference path are treated as additional optimization variables. The proposed EMPFC achieves the integration of path optimization and path-following, resulting in a better economic performance for the closed-loop system. Simulation results are given to show the effectiveness of the proposed control scheme. Finally, Chapter 5 concludes the thesis and future research topics are discussed. / Graduate / 2020-05-14

<p>his thesis proposes two motion synchronization approaches to coordinate the motion of a follower to a leader within the Euler-Lagrange system framework. The information requirements from the leader are that of position and orientation only, i.e. the mathematical model with its parameters and the velocity and acceleration of the leader are considered unknown and unmeasured.</p><p>The follower is responsible for the control action necessary to coordinate the systems, and the leader system is free to manoeuvre independently of the follower. There is no off-line synchronization of the systems through predefined paths or trajectories. %The parameters of the dynamic model of the leader are unknown, and its unmeasured system states (velocity and acceleration) must be estimated in order to be utilized in the coordination controller of the follower.</p><p>The concept of motion control of multiple objects is discussed in terms of the different forms of synchronization; cooperation (where all objects contribute equally) and coordination (where one object governs the motion of the others). Motivating examples and literature provide the motivation for the definition of two motion coordination problems. The output reference state feedback synchronization problem is defined by utilizing only output feedback from the desired motion reference, while assuming state feedback for the follower in the coordination control law. Furthermore, to increase the usefulness of the proposed control schemes and to provide robustness towards loss or poor quality of velocity measurements, the requirements of state information for the follower are alleviated in the definition of the output reference output feedback synchronization problem utilizing only output information of both the leader and the follower in the synchronization design. Furthermore, the necessary tools of stability are presented to prove that the proposed coordination schemes are uniformly ultimately bounded or practically asymptotically stable closed-loop systems.</p><p>In order to solve the output reference state feedback and the output reference output feedback synchronization problems, an observer-controller scheme is proposed that estimates the unknown states of the leader indirectly through a nonlinear model-based error observer. The observer-controller approach makes the follower system a physical observer of the leader system through the coupled observer and controller error-dynamics. A second nonlinear model-based observer is introduced for the follower to remove the state feedback assumption. The observer-controller scheme is proven to be uniformly globally ultimately bounded when utilizing state feedback of the follower in the coordination control law, and to be uniformly semiglobally ultimately bounded when utilizing only output feedback of the follower in the coordination control law. The observer-controller approach to motion coordination is studied through simulations and experiments, and a back-to-back comparison between ideal simulations and practical experiments is presented to allow for a discussion on the performance of the scheme under modelling errors, measurement noise and external disturbances. The observer-controller scheme is demonstrated to be suitable for practical applications.</p><p>Furthermore, a virtual vehicle scheme is proposed to solve the output reference state/ output feedback synchronization problems through a cascaded approach. The virtual vehicle approach is based on a two-level control structure to decouple the estimation and coordination error dynamics in the stability analysis and the tuning process. The virtual vehicle scheme estimates the unknown states of the leader through a virtual kinematic vehicle stabilized to the output of the leader system. A stable first-order velocity filter is introduced for the follower to remove the state feedback assumption. The virtual vehicle scheme is proven to be uniformly globally practically asymptotically stable when utilizing state feedback of the follower in the coordination control law, and to be uniformly semiglobally practically asymptotically stable when utilizing only output feedback of the follower in the coordination control law. Application of the virtual vehicle scheme to both vehicle coordination and robot manipulator coordination is presented, and the virtual vehicle approach to motion coordination is studied through simulations and experiments. The virtual vehicle scheme is demonstrated to be suitable for practical applications. In addition, an extension to a dynamic synchronization scheme is proposed to impose a smooth behaviour on the follower during a change of relative position.</p><p>The proposed coordination schemes are compared in terms of estimation principle, performance and robustness. Simulation studies compare the performance of the proposed schemes in terms of gain tuning and bounds on the closed-loop errors, and in terms of impact from external disturbances, modelling errors and measurement noise. The two coordination schemes are distinguished by concept rather than by performance, and both of the proposed schemes are believed to be suitable for practical implementation in coordination applications.</p>

No description available.

his thesis proposes two motion synchronization approaches to coordinate the motion of a follower to a leader within the Euler-Lagrange system framework. The information requirements from the leader are that of position and orientation only, i.e. the mathematical model with its parameters and the velocity and acceleration of the leader are considered unknown and unmeasured. The follower is responsible for the control action necessary to coordinate the systems, and the leader system is free to manoeuvre independently of the follower. There is no off-line synchronization of the systems through predefined paths or trajectories. %The parameters of the dynamic model of the leader are unknown, and its unmeasured system states (velocity and acceleration) must be estimated in order to be utilized in the coordination controller of the follower. The concept of motion control of multiple objects is discussed in terms of the different forms of synchronization; cooperation (where all objects contribute equally) and coordination (where one object governs the motion of the others). Motivating examples and literature provide the motivation for the definition of two motion coordination problems. The output reference state feedback synchronization problem is defined by utilizing only output feedback from the desired motion reference, while assuming state feedback for the follower in the coordination control law. Furthermore, to increase the usefulness of the proposed control schemes and to provide robustness towards loss or poor quality of velocity measurements, the requirements of state information for the follower are alleviated in the definition of the output reference output feedback synchronization problem utilizing only output information of both the leader and the follower in the synchronization design. Furthermore, the necessary tools of stability are presented to prove that the proposed coordination schemes are uniformly ultimately bounded or practically asymptotically stable closed-loop systems. In order to solve the output reference state feedback and the output reference output feedback synchronization problems, an observer-controller scheme is proposed that estimates the unknown states of the leader indirectly through a nonlinear model-based error observer. The observer-controller approach makes the follower system a physical observer of the leader system through the coupled observer and controller error-dynamics. A second nonlinear model-based observer is introduced for the follower to remove the state feedback assumption. The observer-controller scheme is proven to be uniformly globally ultimately bounded when utilizing state feedback of the follower in the coordination control law, and to be uniformly semiglobally ultimately bounded when utilizing only output feedback of the follower in the coordination control law. The observer-controller approach to motion coordination is studied through simulations and experiments, and a back-to-back comparison between ideal simulations and practical experiments is presented to allow for a discussion on the performance of the scheme under modelling errors, measurement noise and external disturbances. The observer-controller scheme is demonstrated to be suitable for practical applications. Furthermore, a virtual vehicle scheme is proposed to solve the output reference state/ output feedback synchronization problems through a cascaded approach. The virtual vehicle approach is based on a two-level control structure to decouple the estimation and coordination error dynamics in the stability analysis and the tuning process. The virtual vehicle scheme estimates the unknown states of the leader through a virtual kinematic vehicle stabilized to the output of the leader system. A stable first-order velocity filter is introduced for the follower to remove the state feedback assumption. The virtual vehicle scheme is proven to be uniformly globally practically asymptotically stable when utilizing state feedback of the follower in the coordination control law, and to be uniformly semiglobally practically asymptotically stable when utilizing only output feedback of the follower in the coordination control law. Application of the virtual vehicle scheme to both vehicle coordination and robot manipulator coordination is presented, and the virtual vehicle approach to motion coordination is studied through simulations and experiments. The virtual vehicle scheme is demonstrated to be suitable for practical applications. In addition, an extension to a dynamic synchronization scheme is proposed to impose a smooth behaviour on the follower during a change of relative position. The proposed coordination schemes are compared in terms of estimation principle, performance and robustness. Simulation studies compare the performance of the proposed schemes in terms of gain tuning and bounds on the closed-loop errors, and in terms of impact from external disturbances, modelling errors and measurement noise. The two coordination schemes are distinguished by concept rather than by performance, and both of the proposed schemes are believed to be suitable for practical implementation in coordination applications.

Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. Furthermore, uncertainties in the physical parameters of a system may be introduced from significant discrepancies between the manufacturer data and the actual system. Traditionally, adaptive and robust control strategies have been developed to deal with parametric uncertainty in the dynamic model, but often require knowledge of the structure of the dynamics. Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship, eliminating the need for any a-priori knowledge of the system model. Locally Weighted Projection Regression (LWPR) has been proposed for learning the inverse dynamics function of a manipulator. Due to its use of simple local, linear models, LWPR is suitable for online and incremental learning. Although global regression techniques such as Gaussian Process Regression (GPR) have been shown to outperform LWPR in terms of accuracy, due to its heavy computational requirements, GPR has been applied mainly to offline learning of inverse dynamics. More recent efforts in making GPR computationally tractable for real-time control have resulted in several approximations which operate on a select subset, or sparse representation of the entire training data set. Despite the significant advancements that have been made in the area of learning control, there has not been much work in recent years to evaluate these newer regression techniques against traditional model-based control strategies such as adaptive control. Hence, the first portion of this thesis provides a comparison between a fixed model-based control strategy, an adaptive controller and the LWPR-based learning controller. Simulations are carried out in order to evaluate the position and orientation tracking performance of each controller under varied end effector loading, velocities and inaccuracies in the known dynamic parameters. Both the adaptive controller and LWPR controller are shown to have comparable performance in the presence of parametric uncertainty. However, it is shown that the learning controller is unable to generalize well outside of the regions in which it has been trained. Hence, achieving good performance requires significant amounts of training in the anticipated region of operation. In addition to poor generalization performance, most learning controllers commence learning entirely from `scratch,' making no use of any a-priori knowledge which may be available from the well-known rigid body dynamics (RBD) formulation. The second portion of this thesis develops two techniques for online, incremental learning algorithms which incorporate prior knowledge to improve generalization performance. First, prior knowledge is incorporated into the LWPR framework by initializing the local linear models with a first order approximation of the prior information. Second, prior knowledge is incorporated into the mean function of Sparse Online Gaussian Processes (SOGP) and Sparse Pseudo-input Gaussian Processes (SPGP), and a modified version of the algorithm is proposed to allow for online, incremental updates. It is shown that the proposed approaches allow the system to operate well even without any initial training data, and further performance improvement can be achieved with additional online training. Furthermore, it is also shown that even partial knowledge of the system dynamics, for example, only the gravity loading vector, can be used effectively to initialize the learning.

Vehicle motion and tire forces have been estimated using extended Kalman filters for many years. The use of extended Kalman filters is primarily motivated by the simultaneous presence of nonlinear dynamics and sensor noise. Two versions of extended Kalman filters are employed in this thesis: one using a deterministic tire-force model and the other using a stochastic tire-force model. Previous literature has focused on linear stochastic tire-force models and on linear deterministic tire-force models. However, it is well known that there exists a nonlinear relationship between slip variables and tire-force variables. For this reason, it is suitable to use a nonlinear deterministic tire-force model for the extended Kalman filter, and this is the novel aspect at this work. The objective of this research is to show the improvement of the extended Kalman filter using a nonlinear deterministic tire-force model in comparison to linear stochastic tire-force model. The simulation model is a seven degree-of-freedom bicycle model that includes vertical suspension dynamics but neglects the roll motion. A comparison between the linear stochastic tire-force model and the nonlinear deterministic tire-force model confirms the expected results. Simulation studies are performed on some illustrative examples obtaining good tracking performance.