Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees

In this paper, we show how Behavior Trees that have performance guarantees, in terms of safety and goal convergence, can be extended with components that were designed using machine learning, without destroying those performance guarantees. Machine learning approaches such as reinforcement learning or learning from demonstration can be very appealing to AI designers that want efficient and realistic behaviors in their agents. However, those algorithms seldom provide guarantees for solving the given task in all different situations while keeping the agent safe. Instead, such guarantees are often easier to find for manually designed model based approaches. In this paper we exploit the modularity of Behavior trees to extend a given design with an efficient, but possibly unreliable, machine learning component in a way that preserves the guarantees. The approach is illustrated with an inverted pendulum example.Comment: Submitted to IEEE Transactions on Game

Intelligent control of nonlinear systems with actuator saturation using neural networks

Common actuator nonlinearities such as saturation, deadzone, backlash, and hysteresis are unavoidable in practical industrial control systems, such as computer numerical control (CNC) machines, xy-positioning tables, robot manipulators, overhead crane mechanisms, and more. When the actuator nonlinearities exist in control systems, they may exhibit relatively large steady-state tracking error or even oscillations, cause the closed-loop system instability, and degrade the overall system performance. Proportional-derivative (PD) controller has observed limit cycles if the actuator nonlinearity is not compensated well. The problems are particularly exacerbated when the required accuracy is high, as in micropositioning devices. Due to the non-analytic nature of the actuator nonlinear dynamics and the fact that the exact actuator nonlinear functions, namely operation uncertainty, are unknown, the saturation compensation research is a challenging and important topic with both theoretical and practical significance. Adaptive control can accommodate the system modeling, parametric, and environmental structural uncertainties. With the universal approximating property and learning capability of neural network (NN), it is appealing to develop adaptive NN-based saturation compensation scheme without explicit knowledge of actuator saturation nonlinearity. In this dissertation, intelligent anti-windup saturation compensation schemes in several scenarios of nonlinear systems are investigated. The nonlinear systems studied within this dissertation include the general nonlinear system in Brunovsky canonical form, a second order multi-input multi-output (MIMO) nonlinear system such as a robot manipulator, and an underactuated system-flexible robot system. The abovementioned methods assume the full states information is measurable and completely known. During the NN-based control law development, the imposed actuator saturation is assumed to be unknown and treated as the system input disturbance. The schemes that lead to stability, command following and disturbance rejection is rigorously proved, and verified using the nonlinear system models. On-line NN weights tuning law, the overall closed-loop performance, and the boundedness of the NN weights are rigorously derived and guaranteed based on Lyapunov approach. The NN saturation compensator is inserted into a feedforward path. The simulation conducted indicates that the proposed schemes can effectively compensate for the saturation nonlinearity in the presence of system uncertainty

Study and Development of Mechatronic Devices and Machine Learning Schemes for Industrial Applications

Obiettivo del presente progetto di dottorato è lo studio e sviluppo di sistemi meccatronici e di modelli machine learning per macchine operatrici e celle robotizzate al fine di incrementarne le prestazioni operative e gestionali. Le pressanti esigenze del mercato hanno imposto lavorazioni con livelli di accuratezza sempre più elevati, tempi di risposta e di produzione ridotti e a costi contenuti. In questo contesto nasce il progetto di dottorato, focalizzato su applicazioni di lavorazioni meccaniche (e.g. fresatura), che includono sistemi complessi quali, ad esempio, macchine a 5 assi e, tipicamente, robot industriali, il cui utilizzo varia a seconda dell’impiego. Oltre alle specifiche problematiche delle lavorazioni, si deve anche considerare l’interazione macchina-robot per permettere un’efficiente capacità e gestione dell’intero impianto. La complessità di questo scenario può evidenziare sia specifiche problematiche inerenti alle lavorazioni (e.g. vibrazioni) sia inefficienze più generali che riguardano l’impianto produttivo (e.g. asservimento delle macchine con robot, consumo energetico). Vista la vastità della tematica, il progetto si è suddiviso in due parti, lo studio e sviluppo di due specifici dispositivi meccatronici, basati sull’impiego di attuatori piezoelettrici, che puntano principalmente alla compensazione di vibrazioni indotte dal processo di lavorazione, e l’integrazione di robot per l’asservimento di macchine utensili in celle robotizzate, impiegando modelli di machine learning per definire le traiettorie ed i punti di raggiungibilità del robot, al fine di migliorarne l’accuratezza del posizionamento del pezzo in diverse condizioni. In conclusione, la presente tesi vuole proporre soluzioni meccatroniche e di machine learning per incrementare le prestazioni di macchine e sistemi robotizzati convenzionali. I sistemi studiati possono essere integrati in celle robotizzate, focalizzandosi sia su problematiche specifiche delle lavorazioni in macchine operatrici sia su problematiche a livello di impianto robot-macchina. Le ricerche hanno riguardato un’approfondita valutazione dello stato dell’arte, la definizione dei modelli teorici, la progettazione funzionale e l’identificazione delle criticità del design dei prototipi, la realizzazione delle simulazioni e delle prove sperimentali e l’analisi dei risultati.The aim of this Ph.D. project is the study and development of mechatronic systems and machine learning models for machine tools and robotic applications to improve their performances. The industrial demands have imposed an ever-increasing accuracy and efficiency requirement whilst constraining the cost. In this context, this project focuses on machining processes (e.g. milling) that include complex systems such as 5-axes machine tool and industrial robots, employed for various applications. Beside the issues related to the machining process itself, the interaction between the machining centre and the robot must be considered for the complete industrial plant’s improvement. This scenario´s complexity depicts both specific machining problematics (e.g. vibrations) and more general issues related to the complete plant, such as machine tending with an industrial robot and energy consumption. Regarding the immensity of this area, this project is divided in two parts, the study and development of two mechatronic devices, based on piezoelectric stack actuators, for the active vibration control during the machining process, and the robot machine tending within the robotic cell, employing machine learning schemes for the trajectory definition and robot reachability to improve the corresponding positioning accuracy. In conclusion, this thesis aims to provide a set of solutions, based on mechatronic devices and machine learning schemes, to improve the conventional machining centre and the robotic systems performances. The studied systems can be integrated within a robotic cell, focusing on issues related to the specific machining process and to the interaction between robot-machining centre. This research required a thorough study of the state-of-the-art, the formulation of theoretical models, the functional design development, the identification of the critical aspects in the prototype designs, the simulation and experimental campaigns, and the analysis of the obtained results

Torque Control

This book is the result of inspirations and contributions from many researchers, a collection of 9 works, which are, in majority, focalised around the Direct Torque Control and may be comprised of three sections: different techniques for the control of asynchronous motors and double feed or double star induction machines, oriented approach of recent developments relating to the control of the Permanent Magnet Synchronous Motors, and special controller design and torque control of switched reluctance machine

Exponential stabilization of switched-reluctance motors via speed-sensorless feedback

International audienceWe solve the control problem of switched-reluctance motors without velocity measurements. Our controller is composed of a loop in the mechanical dynamics which consists of a PI2 D controller and a "tracking" controller closing an inner loop with the stator currents dynamics. The PI2 D controller consists in a linear proportional derivative controller in which the measurement of velocities is replaced by approximate derivatives of angular position. Then a double integrator is added, composed of an integral of the angular position errors and a second integral correction term in function of the approximate derivative. We show global exponential stability and illustrate the performance of our controller in numerical simulations

Simulink modeling and design of an efficient hardware-constrained FPGA-based PMSM speed controller

The aim of this paper is to present a holistic approach to modeling and FPGA implementation of a permanent magnet synchronous motor (PMSM) speed controller. The whole system is modeled in the Matlab Simulink environment. The controller is then translated to discrete time and remodeled using System Generator blocks, directly synthesizable into FPGA hardware. The algorithm is further refined and factorized to take into account hardware constraints, so as to fit into a low cost FPGA, without significantly increasing the execution time. The resulting controller is then integrated together with sensor interfaces and analysis tools and implemented into an FPGA device. Experimental results validate the controller and verify the design

A passivity based control methodology for flexible joint robots with application to a simplified shuttle RMS arm

The main goal is to develop a general theory for the control of flexible robots, including flexible joint robots, flexible link robots, rigid bodies with flexible appendages, etc. As part of the validation, the theory is applied to the control law development for a test example which consists of a three-link arm modeled after the shoulder yaw joint of the space shuttle remote manipulator system (RMS). The performance of the closed loop control system is then compared with the performance of the existing RMS controller to demonstrate the effectiveness of the proposed approach. The theoretical foundation of this new approach to the control of flexible robots is presented and its efficacy is demonstrated through simulation results on the three-link test arm

Design of a Haptic Interface for Medical Applications using Magneto-Rheological Fluid based Actuators

This thesis reports on the design, construction, and evaluation of a prototype two degrees-of-freedom (DOF) haptic interface, which takes advantage of Magneto-Rheological Fluid (MRF) based clutches for actuation. Haptic information provides important cues in teleoperated systems and enables the user to feel the interaction with a remote or virtual environment during teleoperation. The two main objectives in designing a haptic interface are stability and transparency. Indeed, deficiencies in these factors in haptics-enabled telerobotic systems has the introduction of haptics in medical environments where safety and reliability are prime considerations. An actuator with poor dynamics, high inertia, large size, and heavy weight can significantly undermine the stability and transparency of a teleoperated system. In this work, the potential benefits of MRF-based actuators to the field of haptics in medical applications are studied. Devices developed with such fluids are known to possess superior mechanical characteristics over conventional servo systems. These characteristics significantly contribute to improved stability and transparency of haptic devices. This idea is evaluated and verified through both theoretical and experimental points of view. The design of a small-scale MRF-based clutch, suitable for a multi-DOF haptic interface, is discussed and its performance is compared with conventional servo systems. This design is developed into four prototype clutches. In addition, a closed-loop torque control strategy is presented. The feedback signal used in this control scheme comes from the magnetic field acquired from embedded Hall sensors in the clutch. The controller uses this feedback signal to compensate for the nonlinear behavior using an estimated model, based on Artificial Neural Networks. Such a control strategy eliminates the need for torque sensors for providing feedback signals. The performance of the developed design and the effectiveness of the proposed modeling and control techniques are experimentally validated. Next, a 2-DOF haptic interface based on a distributed antagonistic configuration of MRF-based clutches is constructed for a class of medical applications. This device is incorporated in a master-slave teleoperation setup that is used for applications involving needle insertion and soft-tissue palpation. Phantom and in vitro animal tissue were used to assess the performance of the haptic interface. The results show a great potential of MRF-based actuators for integration in haptic devices for medical interventions that require reliable, safe, accurate, highly transparent, and stable force reflection

Semi-active control techniques for shock isolation

In this thesis the problem of control of semi-active devices (MR damper, MR elastomer) for shock isolation systems are considered. Semi-active control systems combine the best features of both the passive and active control systems, offering the reliability of passive devices, yet maintaining the versatility and adaptability of fully active devices. First the question of stability and control of a two degree-of-freedom magnetorheological (MR) fluid damper shock isolation system is considered. It is shown that for any arbitrarily time varying input current, the system is absolutely stable. This explains the shock isolation capability of the MR damper system even with control laws clamped in an ad hock way to limit the control magnitude. Then a nonlinear inverse (feedback linearizing) control law and a nonlinear suboptimal control law based on the state-dependent Riccati equation (SDRE) method are designed for the shock isolation of the payload mass. For the inverse control law derivation, the inertial position of the payload is chosen as the controlled output variable. For the design via the SDRE method, constraint on the input current is introduced and a quadratic performance index is chosen for minimization. It is shown that in the closed-loop system the inverse and suboptimal control laws are effective in shock isolation of the payload mass; Secondly, the mathematical modeling and predictive control of a magnetorheological fluid damper system is considered. (Abstract shortened by UMI.)