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Robotic Trajectory Tracking: Position- and Force-Control Klecker, Sophie Doctoral thesis (2019) This thesis employs a bottom-up approach to develop robust and adaptive learning algorithms for trajectory tracking: position and torque control. In a first phase, the focus is put on the following of a ... [more ▼] This thesis employs a bottom-up approach to develop robust and adaptive learning algorithms for trajectory tracking: position and torque control. In a first phase, the focus is put on the following of a freeform surface in a discontinuous manner. Next to resulting switching constraints, disturbances and uncertainties, the case of unknown robot models is addressed. In a second phase, once contact has been established between surface and end effector and the freeform path is followed, a desired force is applied. In order to react to changing circumstances, the manipulator needs to show the features of an intelligent agent, i.e. it needs to learn and adapt its behaviour based on a combination of a constant interaction with its environment and preprogramed goals or preferences. The robotic manipulator mimics the human behaviour based on bio-inspired algorithms. In this way it is taken advantage of the know-how and experience of human operators as their knowledge is translated in robot skills. A selection of promising concepts is explored, developed and combined to extend the application areas of robotic manipulators from monotonous, basic tasks in stiff environments to complex constrained processes. Conventional concepts (Sliding Mode Control, PID) are combined with bio-inspired learning (BELBIC, reinforcement based learning) for robust and adaptive control. Independence of robot parameters is guaranteed through approximated robot functions using a Neural Network with online update laws and model-free algorithms. The performance of the concepts is evaluated through simulations and experiments. In complex freeform trajectory tracking applications, excellent absolute mean position errors (<0.3 rad) are achieved. Position and torque control are combined in a parallel concept with minimized absolute mean torque errors (<0.1 Nm). [less ▲] Detailed reference viewed: 113 (6 UL)Robotix-Academy Conference for Industrial Robotics (RACIR) 2017 ; Plapper, Peter ; et al Book published by Shaker Verlag - 1st ed (2017) Detailed reference viewed: 31 (2 UL)Robotix-Academy Conference for Industrial Robotics (RACIR) 2018 ; Plapper, Peter ; et al Book published by Shaker Verlag - 1st ed (2018) Detailed reference viewed: 93 (4 UL)Robust control strategy for minimising energy consumption of electric buses using cooperative ITS technology Giorgione, Giulio ; Viti, Francesco ; Scientific Conference (2016) Detailed reference viewed: 73 (15 UL)Robust dynamical network reconstruction ; ; et al in The proceedings of the 49th IEEE Conference on Decision and Control (CDC) (2010) Motivated by biological applications, this paper addresses the problem of network reconstruction from data. Previous work has shown necessary and sufficient conditions for network reconstruction of noise ... [more ▼] Motivated by biological applications, this paper addresses the problem of network reconstruction from data. Previous work has shown necessary and sufficient conditions for network reconstruction of noise-free LTI systems. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). Algorithms are therefore proposed to reconstruct dynamical (Boolean) network structure from time-series (steady-state) data respectively in presence of noise and nonlinearities. In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular Boolean structures. Information criteria and optimisation technique balancing such distance and model complexity are introduced to search for the true structure. We conclude with biologically-inspired network reconstruction examples which include noise and nonlinearities. [less ▲] Detailed reference viewed: 73 (0 UL)Robust dynamical network structure reconstruction ; ; et al Scientific Conference (2010) Motivated by biological applications, this paper addresses the problem of network reconstruction from data. Previous work has shown necessary and sufficient conditions for network reconstruction of noise ... [more ▼] Motivated by biological applications, this paper addresses the problem of network reconstruction from data. Previous work has shown necessary and sufficient conditions for network reconstruction of noise-free LTI systems. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). Algorithms are therefore proposed to reconstruct dynamical (Boolean) network structure from time-series (steady-state) data respectively in presence of noise and nonlinearities. In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular Boolean structures. Information criteria and optimisation technique balancing such distance and model complexity are introduced to search for the true structure. We conclude with biologically-inspired network reconstruction examples which include noise and nonlinearities. [less ▲] Detailed reference viewed: 71 (0 UL)Robust dynamical network structure reconstruction ; ; et al in Automatica (2011), 47(6), This paper addresses the problem of network reconstruction from data. Previous work identified necessary and sufficient conditions for network reconstruction of LTI systems, assuming perfect measurements ... [more ▼] This paper addresses the problem of network reconstruction from data. Previous work identified necessary and sufficient conditions for network reconstruction of LTI systems, assuming perfect measurements (no noise) and perfect system identification. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular network structures. We conclude with biologically inspired network reconstruction examples which include noise and nonlinearities. [less ▲] Detailed reference viewed: 101 (0 UL)Robust H∞ feedback control for uncertain stochastic delayed genetic regulatory networks with additive and multiplicative noise Pan, Wei ; ; et al in International Journal of Robust and Nonlinear Control (2010), 20(18), 2093-2107 Noises are ubiquitous in genetic regulatory networks (GRNs). Gene regulation is inherently a stochastic process because of intrinsic and extrinsic noises that cause kinetic parameter variations and basal ... [more ▼] Noises are ubiquitous in genetic regulatory networks (GRNs). Gene regulation is inherently a stochastic process because of intrinsic and extrinsic noises that cause kinetic parameter variations and basal rate disturbance. Time delays are usually inevitable due to different biochemical reactions in such GRNs. In this paper, a delayed stochastic model with additive and multiplicative noises is utilized to describe stochastic GRNs. A feedback gene controller design scheme is proposed to guarantee that the GRN is mean-square asymptotically stable with noise attenuation, where the structure of the controllers can be specified according to engineering requirements. By applying control theory and mathematical tools, the analytical solution to the control design problem is given, which helps to provide some insight into synthetic biology and systems biology. The control scheme is employed in a three-gene network to illustrate the applicability and usefulness of the design. [less ▲] Detailed reference viewed: 68 (0 UL)Robust network reconstruction in polynomial time ; ; Goncalves, Jorge in The proceedings of the 51st IEEE Conference on Decision and Control (2012) This paper presents an efficient algorithm for robust network reconstruction of Linear Time-Invariant (LTI) systems in the presence of noise, estimation errors and unmodelled nonlinearities. The method ... [more ▼] This paper presents an efficient algorithm for robust network reconstruction of Linear Time-Invariant (LTI) systems in the presence of noise, estimation errors and unmodelled nonlinearities. The method here builds on previous work [1] on robust reconstruction to provide a practical implementation with polynomial computational complexity. Following the same experimental protocol, the algorithm obtains a set of structurally-related candidate solutions spanning every level of sparsity. We prove the existence of a magnitude bound on the noise, which if satisfied, guarantees that one of these structures is the correct solution. A problem-specific model-selection procedure then selects a single solution from this set and provides a measure of confidence in that solution. Extensive simulations quantify the expected performance for different levels of noise and show that significantly more noise can be tolerated in comparison to the original method. [less ▲] Detailed reference viewed: 89 (0 UL)Robust Online Obstacle Detection and Tracking for Collision-free Navigation of Multirotor UAVs in Complex Environments Wang, Min ; Voos, Holger ; in 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore 18-21 November 2018 (2018) Object detection and tracking is a challenging task, especially for unmanned aerial robots in complex environments where both static and dynamic objects are present. It is, however, essential for ensuring ... [more ▼] Object detection and tracking is a challenging task, especially for unmanned aerial robots in complex environments where both static and dynamic objects are present. It is, however, essential for ensuring safety of the robot during navigation in such environments. In this work we present a practical online approach which is based on a 2D LIDAR. Unlike common approaches in the literature of modeling the environment as 2D or 3D occupancy grids, our approach offers a fast and robust method to represent the objects in the environment in a compact form, which is significantly more efficient in terms of both memory and computation in comparison with the former. Our approach is also capable of classifying objects into categories such as static and dynamic, and tracking dynamic objects as well as estimating their velocities with reasonable accuracy. [less ▲] Detailed reference viewed: 100 (30 UL)Robust Signal-Structure Reconstruction ; ; Goncalves, Jorge et al in The proceedings of the IEEE 52nd Annual Conference on Decision and Control (2013) This paper focuses on the reconstruction of the signal structure of a system in the presence of noise and nonlinearities. Previous results on polynomial time reconstruction in this area were restricted to ... [more ▼] This paper focuses on the reconstruction of the signal structure of a system in the presence of noise and nonlinearities. Previous results on polynomial time reconstruction in this area were restricted to systems where target specificity was part of the inherent structure, [5]. This work extends these results to all reconstructible systems and proposes a faster reconstruction algorithm along with an improved model selection procedure. Finally, a simulation study then details the performance of this new algorithm on reconstructible systems. [less ▲] Detailed reference viewed: 79 (0 UL)Robust stability of delayed genetic regulatory networks with different sources of uncertainties Pan, Wei ; ; in Asian Journal of Control (2011), 13(5), 645-654 Gene regulation is inherently a stochastic process due to intrinsic and extrinsic noises which cause the fluctuations and uncertainties of kinetic parameters. On the other hand, time delays are usually ... [more ▼] Gene regulation is inherently a stochastic process due to intrinsic and extrinsic noises which cause the fluctuations and uncertainties of kinetic parameters. On the other hand, time delays are usually inevitable due to different biochemical reactions in the genetic regulatory networks (GRNs) which are also affected by noises. Therefore, in this paper, we propose a GRN model that is subject to additive and multiplicative noises as well as time-varying delays. The time-varying delay is assumed to belong to an interval and no restriction on the derivative of the time-varying delay is needed, which allows the delay to be a fast time-varying function. Robust stochastic stability of such GRNs with disturbance attenuation is analyzed by applying the control theory and mathematical tools. Based on the Lyapunov method, new stability conditions are derived in the form of linear matrix inequalities that are dependent on the upper and lower bounds of time delays. An example is employed to illustrate the applicability and usefulness of the developed theoretical results. [less ▲] Detailed reference viewed: 76 (0 UL)Robust synchronization in networks of cyclic feedback systems ; Goncalves, Jorge ; in The proceedings of the 47th IEEE Conference on Decision and Control (2008) This paper presents a result on the robust synchronization of outputs of statically interconnected non-identical cyclic feedback systems that are used to model, among other processes, gene expression. The ... [more ▼] This paper presents a result on the robust synchronization of outputs of statically interconnected non-identical cyclic feedback systems that are used to model, among other processes, gene expression. The result uses incremental versions of the small gain theorem and dissipativity theory to arrive at an upper bound on the norm of the synchronization error between corresponding states, giving a measure of the degree of convergence of the solutions. This error bound is shown to be a function of the difference between the parameters of the interconnected systems, and disappears in the case where the systems are identical, thus retrieving an earlier synchronization result. [less ▲] Detailed reference viewed: 71 (2 UL)Robust Traffic Anomaly Detection with Principal Component Pursuit ; ; Wang, Wei et al in Proceedings of the ACM CoNEXT Student Workshop (2010) Principal component analysis (PCA) is a statistical technique that has been used for data analysis and dimensionality reduction. It was introduced as a network traffic anomaly detection technique firstly ... [more ▼] Principal component analysis (PCA) is a statistical technique that has been used for data analysis and dimensionality reduction. It was introduced as a network traffic anomaly detection technique firstly in [1]. Since then, a lot of research attention has been received, which results in an extensive analysis and several extensions. In [2], the sensitivity of PCA to its tuning parameters, such as the dimension of the low-rank subspace and the detection threshold, on traffic anomaly detection was indicated. However, no explanation on the underlying reasons of the problem was given in [2]. In [3], further investigation on the PCA sensitivity was conducted and it was found that the PCA sensitivity comes from the inability of PCA to detect temporal correlations. Based on this finding, an extension of PCA to Kalman-Loeve expansion (KLE) was proposed in [3]. While KLE shows slight improvement, it still exhibits similar sensitivity issue since a new tuning parameter called temporal correlation range was introduced. Recently, in [4], additional effort was paid to illustrate the PCA-poisoning problem. To underline this problem, an evading strategy called Boiled-Frog was proposed which adds a high fraction of outliers to the traffic. To defend against this, the authors employed a more robust version of PCA called PCA-GRID. While PCA-GRID shows performance improvement regarding the robustness to the outliers, it experiences a high sensitivity to the threshold estimate and the k-dimensional subspace that maximizes the dispersion of the data. The purpose of this work is to consider another technique to address the PCA poisoning problems to provide robust traffic anomaly detection: The Principal Component Pursuit. [less ▲] Detailed reference viewed: 97 (1 UL)Robustness analysis of cellular systems for in silico drug discovery Perumal, Thanneer Malai ; ; Scientific Conference (2008) Detailed reference viewed: 61 (1 UL)Route diversity on the internet: a natural way to improve the network traffic Melakessou, Foued Doctoral thesis (2008) Detailed reference viewed: 60 (3 UL)S-GHOST: Un modèle d'auto-organisation de l'étalement urbain et du réseau de transport Caruso, Geoffrey ; ; et al in Antoni, Jean-Philippe (Ed.) Modéliser la ville: formes urbaines et politiques de transport (2011) Detailed reference viewed: 70 (2 UL)Sa-TikZ: a library to draw switching architectures Fiandrino, Claudio in Ars TeXnica (2013), 15 The article illustrates how it is possible to draw some types of switching architectures in a simple manner. The Sa-TikZ library provides not only the keys devoted to the drawing part, but also the keys ... [more ▼] The article illustrates how it is possible to draw some types of switching architectures in a simple manner. The Sa-TikZ library provides not only the keys devoted to the drawing part, but also the keys devoted to customize the aspect of the architectures in the spirit of the TikZ syntax. [less ▲] Detailed reference viewed: 88 (9 UL)Safer UAV Piloting: A Robust Sense-and-Avoid Solution for Remotely Piloted Quadrotor UAVs in Complex Environments Wang, Min ; Voos, Holger in Safer UAV Piloting: A Robust Sense-and-Avoid Solution for Remotely Piloted Quadrotor UAVs in Complex Environments (2019, December) Current commercial UAVs are to a large extent remotely piloted by amateur human pilots. Due to lack of teleoperation experience or skills, they often drive the UAVs into collision. Therefore, in order to ... [more ▼] Current commercial UAVs are to a large extent remotely piloted by amateur human pilots. Due to lack of teleoperation experience or skills, they often drive the UAVs into collision. Therefore, in order to ensure safety of the UAV as well as its surroundings, it is necessary for the UAV to boast the capability of detecting emergency situation and acting on its own when facing imminent threat. However, the majority of UAVs currently available in the market are not equipped with such capability. To fill in the gap, in this paper we present a complete sense-and-avoid solution for assisting unskilled pilots in ensuring a safe flight. Particularly, we propose a novel nonlinear vehicle control system which takes into account of sensor characteristics, an emergency evaluation policy and a novel optimization-based avoidance control strategy. The effectiveness of the proposed approach is demonstrated and validated in simulation with multiple moving objects. [less ▲] Detailed reference viewed: 45 (13 UL)Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees ; ; Kasprzak, Mikolaj et al in Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019 (2019) Detailed reference viewed: 25 (5 UL) |
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