Active stability observer using artificial neural network for intuitive physical human–robot interaction

Physical human-robot interaction may present an obstacle to transparency and operations’ intuitiveness. This barrier could occur due to the vibrations caused by a stiff environment interacting with the robotic mechanisms. In this regard, this paper aims to deal with the aforementioned issues while using an observer and an adaptive gain controller. The adaptation of the gain loop should be performed in all circumstances in order to maintain operators’ safety and operations’ intuitiveness. Hence, two approaches for detecting and then reducing vibrations will be introduced in this study as follows: 1) a statistical analysis of a sensor signal (force and velocity) and 2) a multilayer perceptron artificial neural network capable of compensating the first approach for ensuring vibrations identification in real time. Simulations and experimental results are then conducted and compared in order to evaluate the validity of the suggested approaches in minimizing vibrations

Steering control for haptic feedback and active safety functions

Steering feedback is an important element that defines driver–vehicle interaction. It strongly affects driving performance and is primarily dependent on the steering actuator\u27s control strategy. Typically, the control method is open loop, that is without any reference tracking; and its drawbacks are hardware dependent steering feedback response and attenuated driver–environment transparency. This thesis investigates a closed-loop control method for electric power assisted steering and steer-by-wire systems. The advantages of this method, compared to open loop, are better hardware impedance compensation, system independent response, explicit transparency control and direct interface to active safety functions.The closed-loop architecture, outlined in this thesis, includes a reference model, a feedback controller and a disturbance observer. The feedback controller forms the inner loop and it ensures: reference tracking, hardware impedance compensation and robustness against the coupling uncertainties. Two different causalities are studied: torque and position control. The two are objectively compared from the perspective of (uncoupled and coupled) stability, tracking performance, robustness, and transparency.The reference model forms the outer loop and defines a torque or position reference variable, depending on the causality. Different haptic feedback functions are implemented to control the following parameters: inertia, damping, Coulomb friction and transparency. Transparency control in this application is particularly novel, which is sequentially achieved. For non-transparent steering feedback, an environment model is developed such that the reference variable is a function of virtual dynamics. Consequently, the driver–steering interaction is independent from the actual environment. Whereas, for the driver–environment transparency, the environment interaction is estimated using an observer; and then the estimated signal is fed back to the reference model. Furthermore, an optimization-based transparency algorithm is proposed. This renders the closed-loop system transparent in case of environmental uncertainty, even if the initial condition is non-transparent.The steering related active safety functions can be directly realized using the closed-loop steering feedback controller. This implies, but is not limited to, an angle overlay from the vehicle motion control functions and a torque overlay from the haptic support functions.Throughout the thesis, both experimental and the theoretical findings are corroborated. This includes a real-time implementation of the torque and position control strategies. In general, it can be concluded that position control lacks performance and robustness due to high and/or varying system inertia. Though the problem is somewhat mitigated by a robust H-infinity controller, the high frequency haptic performance remains compromised. Whereas, the required objectives are simultaneously achieved using a torque controller

A variable-fractional order admittance controller for pHRI

In today’s automation driven manufacturing environments, emerging technologies like cobots (collaborative robots) and augmented reality interfaces can help integrating humans into the production workflow to benefit from their adaptability and cognitive skills. In such settings, humans are expected to work with robots side by side and physically interact with them. However, the trade-off between stability and transparency is a core challenge in the presence of physical human robot interaction (pHRI). While stability is of utmost importance for safety, transparency is required for fully exploiting the precision and ability of robots in handling labor intensive tasks. In this work, we propose a new variable admittance controller based on fractional order control to handle this trade-off more effectively. We compared the performance of fractional order variable admittance controller with a classical admittance controller with fixed parameters as a baseline and an integer order variable admittance controller during a realistic drilling task. Our comparisons indicate that the proposed controller led to a more transparent interaction compared to the other controllers without sacrificing the stability. We also demonstrate a use case for an augmented reality (AR) headset which can augment human sensory capabilities for reaching a certain drilling depth otherwise not possible without changing the role of the robot as the decision maker

Experimental Evaluation of the Projection-based Force Reflection Algorithms for Haptic Interaction with Virtual Environment

Haptic interaction with virtual environments is currently a major and growing area of research with a number of emerging applications, particularly in the field of robotics. Digital implementation of the virtual environments, however, introduces errors which may result in instability of the haptic displays. This thesis deals with experimental investigation of the Projection-Based Force Reflection Algorithms (PFRAs) for haptic interaction with virtual environments, focusing on their performance in terms of stability and transparency. Experiments were performed to compare the PFRA in terms of performance for both non-delayed and delayed haptic interactions with more conventional haptic rendering methods, such as the Virtual Coupling (VC) and Wave Variables (WV). The results demonstrated that the PFRA is more stable, guarantees higher levels of transparency, and is less sensitive to decrease in update rates

On the passivity of interaction control with series elastic actuation

Regulating the mechanical interaction between robot and environment is a fundamentally important problem in robotics. Many applications such as manipulation and assembly tasks necessitate interaction control. Applications in which the robots are expected to collaborate and share the workspace with humans also require interaction control. Therefore, interaction controllers are quintessential to physical human-robot interaction (pHRI) applications. Passivity paradigm provides powerful design tools to ensure the safety of interaction. It relies on the idea that passive systems do not generate energy that can potentially destabilize the system. Thus, coupled stability is guaranteed if the controller and the environment are passive. Fortunately, passive environments constitute an extensive and useful set, including all combinations of linear or nonlinear masses, springs, and dampers. Moreover, a human operator may also be treated as a passive network element. Passivity paradigm is appealing for pHRI applications as it ensures stability robustness and provides ease-of-control design. However, passivity is a conservative framework which imposes stringent limits on control gains that deteriorate the performance. Therefore, it is of paramount importance to obtain the most relaxed passivity bounds for the control design problem. Series Elastic Actuation (SEA) has become prevalent in pHRI applications as it provides considerable advantages over traditional sti actuators in terms of stability robustness and delity of force control, thanks to deliberately introduced compliance between the actuator and the load. Several impedance control architectures have been proposed for SEA. Among the alternatives, the cascaded controller with an inner-most velocity loop, an intermediate torque loop and an outer-most impedance loop is particularly favoured for its simplicity, robustness, and performance. In this thesis, we derive the necessary and su cient conditions to ensure the passivity of the cascade-controller architecture for rendering two classical linear impedance models of null impedance and pure spring. Based on the newly established passivity conditions, we provide non-conservative design guidelines to haptically display free-space and virtual spring while ensuring coupled stability, thus the safety of interaction. We demonstrate the validity of these conditions through simulation studies as well as physical experiments. We demonstrate the importance of including physical damping in the actuator model during derivation of passivity conditions, when integral controllers are utilized. We note the unintuitive adversary e ect of actuator damping on system passivity. More precisely, we establish that the damping term imposes an extra bound on controller gains to preserve passivity. We further study an extension to the cascaded SEA control architecture and discover that series elastic damping actuation (SEDA) can passively render impedances that are out of the range of SEA. In particular, we demonstrate that SEDA can passively render Voigt model and impedances higher than the physical spring-damper pair in SEDA. The mathematical analyses of SEDA are veri ed through simulations