Adaptive control of compliant robots with Reservoir Computing

In modern society, robots are increasingly used to handle dangerous, repetitive and/or heavy tasks with high precision. Because of the nature of the tasks, either being dangerous, high precision or simply repetitive, robots are usually constructed with high torque motors and sturdy materials, that makes them dangerous for humans to handle. In a car-manufacturing company, for example, a large cage is placed around the robot’s workspace that prevents humans from entering its vicinity. In the last few decades, efforts have been made to improve human-robot interaction. Often the movement of robots is characterized as not being smooth and clearly dividable into sub-movements. This makes their movement rather unpredictable for humans. So, there exists an opportunity to improve the motion generation of robots to enhance human-robot interaction. One interesting research direction is that of imitation learning. Here, human motions are recorded and demonstrated to the robot. Although the robot is able to reproduce such movements, it cannot be generalized to other situations. Therefore, a dynamical system approach is proposed where the recorded motions are embedded into the dynamics of the system. Shaping these nonlinear dynamics, according to recorded motions, allows for dynamical system to generalize beyond demonstration. As a result, the robot can generate motions of other situations not included in the recorded human demonstrations. In this dissertation, a Reservoir Computing approach is used to create a dynamical system in which such demonstrations are embedded. Reservoir Computing systems are Recurrent Neural Network-based approaches that are efficiently trained by considering only the training of the readout connections and retaining all other connections of such a network unchanged given their initial randomly chosen values. Although they have been used to embed periodic motions before, they were extended to embed discrete motions, or both. This work describes how such a motion pattern-generating system is built, investigates the nature of the underlying dynamics and evaluates their robustness in the face of perturbations. Additionally, a dynamical system approach to obstacle avoidance is proposed that is based on vector fields in the presence of repellers. This technique can be used to extend the motion abilities of the robot without need for changing the trained Motion Pattern Generator (MPG). Therefore, this approach can be applied in real-time on any system that generates a certain movement trajectory. Assume that the MPG system is implemented on an industrial robotic arm, similar to the ones used in a car factory. Even though the obstacle avoidance strategy presented is able to modify the generated motion of the robot’s gripper in such a way that it avoids obstacles, it does not guarantee that other parts of the robot cannot collide with a human. To prevent this, engineers have started to use advanced control algorithms that measure the amount of torque that is applied on the robot. This allows the robot to be aware of external perturbations. However, it turns out that, even with fast control loops, the adaptation to compensate for a sudden perturbation, is too slow to prevent high interaction forces. To reduce such forces, researchers started to use mechanical elements that are passively compliant (e.g., springs) and light-weight flexible materials to construct robots. Although such compliant robots are much safer and inherently energy efficient to use, their control becomes much harder. Most control approaches use model information about the robot (e.g., weight distribution and shape). However, when constructing a compliant robot it is hard to determine the dynamics of these materials. Therefore, a model-free adaptive control framework is proposed that assumes no prior knowledge about the robot. By interacting with the robot it learns an inverse robot model that is used as controller. The more it interacts, the better the control be- comes. Appropriately, this framework is called Inverse Modeling Adaptive (IMA) control framework. I have evaluated the IMA controller’s tracking ability on sev- eral tasks, investigating its model independence and stability. Furthermore, I have shown its fast learning ability and comparable performance to taskspecific designed controllers. Given both the MPG and IMA controllers, it is possible to improve the inter- actability of a compliant robot in a human-friendly environment. When the robot is to perform human-like motions for a large set of tasks, we need to demonstrate motion examples of all these tasks. However, biological research concerning the motion generation of animals and humans revealed that a limited set of motion patterns, called motion primitives, are modulated and combined to generate advanced motor/motion skills that humans and animals exhibit. Inspired by these interesting findings, I investigate if a single motion primitive indeed can be modulated to achieve a desired motion behavior. By some elementary experiments, where an MPG is controlled by an IMA controller, a proof of concept is presented. Furthermore, a general hierarchy is introduced that describes how a robot can be controlled in a biology-inspired manner. I also investigated how motion primitives can be combined to produce a desired motion. However, I was unable to get more advanced implementations to work. The results of some simple experiments are presented in the appendix. Another approach I investigated assumes that the primitives themselves are undefined. Instead, only a high-level description is given, which describes that every primitive on average should contribute equally, while still allowing for a single primitive to specialize in a part of the motion generation. Without defining the behavior of a primitive, only a set of untrained IMA controllers is used of which each will represent a single primitive. As a result of the high-level heuristic description, the task space is tiled into sub-regions in an unsupervised manner. Resulting in controllers that indeed represent a part of the motion generation. I have applied this Modular Architecture with Control Primitives (MACOP) on an inverse kinematic learning task and investigated the emerged primitives. Thanks to the tiling of the task space, it becomes possible to control redundant systems, because redundant solutions can be spread over several control primitives. Within each sub region of the task space, a specific control primitive is more accurate than in other regions allowing for the task complexity to be distributed over several less complex tasks. Finally, I extend the use of an IMA-controller, which is tracking controller, to the control of under-actuated systems. By using a sample-based planning algorithm it becomes possible to explore the system dynamics in which a path to a desired state can be planned. Afterwards, MACOP is used to incorporate feedback and to learn the necessary control commands corresponding to the planned state space trajectory, even if it contains errors. As a result, the under-actuated control of a cart pole system was achieved. Furthermore, I presented the concept of a simulation based control framework that allows the learning of the system dynamics, planning and feedback control iteratively and simultaneously

Problèmes de stabilisation au bord pour des systèmes d'équations aux dérivées partielles hyperboliques en dimension un d'espace

Dans cette thèse, nous étudions le problème de stabilisation au bord de systèmes généraux d'équations aux dérivées partielles hyperboliques. Plus précisément, l'étude se focalise sur des systèmes où le transport est uniquement scalaire et où le sens de propagation de l'information est fixé. En outre, le contrôle choisi sera la plupart du temps sous la forme d'une loi de retour d'état (ou feedback) linéaire que l'on perturbera éventuellement par l'effet d'une saturation. Le travail est séparé en deux parties bien distinctes ; l'une se concentre sur des méthodes de Lyapunov, tandis que l'autre va plutôt utiliser des techniques propres au linéaire. Pour la première partie, deux travaux principaux sont présentés. Dans un premier temps, nous ne considérons que des équations de transport linéaires à vitesses positives et cherchons à stabiliser exponentiellement le système dans L8 grâce à un feedback linéaire saturé. La méthode consiste à utiliser des techniques classiques de Lyapunov afin d'exhiber un bassin d'attraction et d'en donner une estimation fine. On généralise ensuite ce travail dans un cadre BV pour les systèmes de lois de conservation scalaires couplées au bord. Secondement, un système de lois de conservation scalaires à vitesses positives est discrétisé en utilisant un schéma à limiteur de pente. En s'inspirant des méthodes issues du cadre continu, une fonctionnelle de Lyapunov discrète est étudiée pour prouver la stabilisation exponentielle BV par feedback linéaire de la solution discrète. Pour la seconde partie, deux études sont également exposées mais cette fois-ci, dans un cadre totalement linéaire. D'une part, il s'agit d'établir la possibilité de construire un feedback issu d'un placement de pôles pour stabiliser exponentiellement des edps hyperboliques linéaires avec couplage au bord et dans le domaine. D'autre part, nous développons une théorie du backstepping discrétisé pour stabiliser en temps fini un schéma numérique modélisant un système 2 × 2 avec couplage au bord et au sein du domaine.In this thesis, we study the problem of boundary stabilization of general hyperbolic systems of partial differential equations. More precisely, the analysis focuses on systems where the transport term is scalar and for which the information propagates in a fixed direction. In addition, the chosen control is most of the time a state feedback law for which a saturation is possibly applied. The work is divided into two distinct parts, one focusing on Lyapunov techniques while the other one uses the linearity of the problem. In the first part of the thesis, two main works are presented. In the first one, only linear transport equations with positive velocities are considered. The main goal is to design a saturated linear feedback in order to stabilize exponentially the open-loop system in L 8 . The method consists of using classical Lyapunov techniques to exhibit a basin of attraction for which a fine estimate is given. We also extend this work to nonlinear scalar conservation laws in a BV framework. In the other work, thanks to a slope limiter scheme, a system of scalar conservation laws is discretized. Inspired by "continuous" Lyapunov methods, a discrete Lyapunov functional is studied to prove the exponential BV stabilization of the discrete solution using a linear feedback. In the second part of the thesis, two works are exposed as well, this time in a full linear framework. On the one hand, we study systems of linear transport equations of arbitrary dimension, coupled on the domain and at the boundary. Designing a controller from a pole placement algorithm, the exponential stabilization is proved in L 2 . On the other hand, we develop a numerical Backstepping theory in order to stabilize in finite time a numerical scheme modeling a 2 × 2 linear system with in domain and boundary couplings

Seat belt control : from modeling to experiment

In the last decades, vehicle safety has improved considerably. For example, major improvements have been made in the area of the structural crashworthiness of the vehicle, various driver assistance systems have been developed, and enhancements can be found in the restraint systems, the final line of defense in occupant protection. Despite this increase of vehicle safety measures, many fatalities still occur in road transportation. Regarding the unavoidable crashes, a significant amount can be attributed to the fact that the seat belt system does not perform optimally. No crash event or occupant is identical, yet conventional seat belts are – in general – not able to adjust their characteristics accordingly. The system is therefore optimal for only a limited number of crash scenarios and occupant types. With the current sensor and processor technology, it may be possible to develop a seat belt that continuously adapts to the actual crash and occupant conditions. Such a device is referred to as a Continuous Restraint Control (CRC) system, and the work presented in this thesis contributes to the development of this type of systems. The main idea of seat belt control is to add sensors and actuators to the seat belt system. The force in the seat belt is prescribed by the actuator during the crash, such that the risk of injuries are minimized given the current impact severity and occupant size and position. This concept poses several technological challenges, which are in this thesis divided into four research topics. Although many sensor technologies exist nowadays, so far no methods have been proposed to measure the occupant injury responses in real-time. These responses are essential when deciding on the optimal belt force. In this thesis, a solution has been presented for the problem of real-time estimation of (thoracic) injuries and occupant position during a crash. An estimation is performed based on modelbased filtering of a small number of readily available and cheap sensors. Simulation results with a crash victim model indicate that the injury responses can be estimated with sufficient accuracy for control purposes, but that the estimation heavily depends on the accuracy of the model used in the filter. A numerical controller uses these estimated injury responses to compute the optimal seat belt force. In this computation, it has to be taken into account that the occupant position is constrained during the crash by the available space in the vehicle, since contact with the interior may result in serious injury. The controller therefore has to predict the future occupant motion, using a prediction of the future crash behavior, a choice for the future seat belt force, and a model of the vehicle-occupant-belt system. Given the type of control problem, a Model Predictive Control (MPC) approach is used to develop the controller. Simulation results with crash victim models indicate that using this controller lead to a significant injury risk reduction for the thorax, given that an ideal belt actuator is available. The injury estimator, the prediction and control algorithm proposed in the foregoing are designed with simple mathematical models of occupant, seat belt and vehicle interior. It is therefore recognized that such accurate, manageable models are essential in the development of CRC systems. In this thesis, models of various complexities have been constructed that represent three types of widely used crash test dummies. These models are validated against both numerical as experimental data. The conclusion of this validation is that in frontal crashes, the neck and thoracic injury criteria can well be described by linear (time-invariant) models. However, when the models are to be used in the design of a belt control system, more attention has to be given to the modeling of the chest and seat belt. The severity and duration of a typical impact require a seat belt actuator with challenging specifications. For example, it has to deliver very high forces over a large stroke, it must have a high bandwidth, and must be small enough to be fitted in a vehicle post. These devices do not yet exist. In this thesis, a semi-active belt actuator concept is presented. It is based on a pressure-controlled hydraulic valve, which regulates the belt force through an hydraulic cylinder. The actuator is designed and constructed at the TU/e, and evaluated experimentally. Moreover, a moving sled setup has been developed which allows testing the actuator under impact conditions. Experimental results show that the belt actuator meets the requirements, except for the maximum force. The actuator can therefore at this point be used to prescribe belt forces in a safety belt in low-speed impacts

Computational fluid dynamics for aerospace propulsion systems: an approach based on discontinuous finite elements

The purpose of this work is the development of a numerical tool devoted to the study of the flow field in the components of aerospace propulsion systems. The goal is to obtain a code which can efficiently deal with both steady and unsteady problems, even in the presence of complex geometries. Several physical models have been implemented and tested, starting from Euler equations up to a three equations RANS model. Numerical results have been compared with experimental data for several real life applications in order to understand the range of applicability of the code. Performance optimization has been considered with particular care thanks to the participation to two international Workshops in which the results were compared with other groups from all over the world. As far as the numerical aspect is concerned, state-of-art algorithms have been implemented in order to make the tool competitive with respect to existing softwares. The features of the chosen discretization have been exploited to develop adaptive algorithms (p, h and hp adaptivity) which can automatically refine the discretization. Furthermore, two new algorithms have been developed during the research activity. In particular, a new technique (Feedback filtering [1]) for shock capturing in the framework of Discontinuous Galerkin methods has been introduced. It is based on an adaptive filter and can be efficiently used with explicit time integration schemes. Furthermore, a new method (Enhance Stability Recovery [2]) for the computation of diffusive fluxes in Discontinuous Galerkin discretizations has been developed. It derives from the original recovery approach proposed by van Leer and Nomura [3] in 2005 but it uses a different recovery basis and a different approach for the imposition of Dirichlet boundary conditions. The performed numerical comparisons showed that the ESR method has a larger stability limit in explicit time integration with respect to other existing methods (BR2 [4] and original recovery [3]). In conclusion, several well known test cases were studied in order to evaluate the behavior of the implemented physical models and the performance of the developed numerical schemes

Verification and Validation of Robot Manipulator Adaptive Control with Actuator Deficiency

This work addresses the joint tracking problem of robotic manipulators with uncertain dynamical parameters and actuator deficiencies, in the form of an uncertain control effectiveness matrix, through adaptive control design, simulation, and experimentation. Specifically, two novel adaptive controller formulations are implemented and tested via simulation and experimentation. The proposed adaptive control formulations are designed to compensate for uncertainties in the dynamical system parameters as well as uncertainties in the control effectiveness matrix that pre-multiplies the control input. The uncertainty compensation of the dynamical parameters is achieved via the use of the desired model compensation–based adaptation, while the uncertainties related to the control effectiveness matrix are dealt with via two fundamentally different novel adaptation methods, namely with bound-based and projection operator-based methods. The stability of the system states and convergence of the error terms to the origin are proven via Lyapunov–based arguments. Extensive numerical studies are performed on a two–link planar robotic device, and experimental studies are preformed on Quansers QArm to illustrate the effectiveness of both adaptive controllers. In the experimental validation of the theory, both adaptive controllers demonstrate remarkable resilience, maintaining control of the Quanser QArm even with up to an 80% control input deficiency. After tuning the gains, both joints satisfactorily tracked the desired trajectories. When evaluating the entire experiment, the norm of the square of the total error is averaged. The bound-based controller exhibited an average error of 2.816◦ across all cases, while the projection operator-based controller had a reduced average error of 1.012◦ across all cases. Furthermore, over time, there is a noticeable decrease in error for both joints. These results underscore the robustness and effectiveness of the proposed adaptive controllers, even under substantial actuator deficiencies. The results highlight the significance of achieving near-perfect system knowledge and the careful selection of controls for desirable system performanc

Trellis phase codes for power-bandwith efficient satellite communications

Support work on improved power and spectrum utilization on digital satellite channels was performed. Specific attention is given to the class of signalling schemes known as continuous phase modulation (CPM). The specific work described in this report addresses: analytical bounds on error probability for multi-h phase codes, power and bandwidth characterization of 4-ary multi-h codes, and initial results of channel simulation to assess the impact of band limiting filters and nonlinear amplifiers on CPM performance

Artificial Neural Network Application In Environmental Engineering.

The objective of this thesis research is to apply two artificial neural network (ANN) methods, back-propagation neural network (BPN) and radial basis function generalized regression neural network (RBFGRNN) in two environmental engineering case studies to explore their ability to modeling the complex environmental engineering systems. The traditional environmental engineering systems modeling are frequently using the physical-based modeling methods

Advances in Control of Power Electronic Converters

This book proposes a list of contributions in the field of control of power electronics converters for different topologies: DC-DC, DC-AC and AC-DC. It particularly focuses on the use of different advanced control techniques with the aim of improving the performances, flexibility and efficiency in the context of several operation conditions. Sliding mode control, fuzzy logic based control, dead time compensation and optimal linear control are among the techniques developed in the special issue. Simulation and experimental results are provided by the authors to validate the proposed control strategies