Quadrupedal Robots with Stiff and Compliant Actuation

In the broader context of quadrupedal locomotion, this overview article introduces and compares two platforms that are similar in structure, size, and morphology, yet differ greatly in their concept of actuation. The first, ALoF, is a classically stiff actuated robot that is controlled kinematically, while the second, StarlETH, uses a soft actuation scheme based on Changedhighly compliant series elastic actuators. We show how this conceptual difference influences design and control of the robots, compare the hardware of the two systems, and show exemplary their advantages in different application

Precise Trajectory Tracking of Multi-Rotor UAVs Using Wind Disturbance Rejection Approach

This paper discusses the resilience of the UAV quadrotor to wind disturbances. An unknown input-state observer is presented that uses the Lipschitz method to estimate the internal states and disturbances of the quadrotor and compensate for them by varying the velocities of the four rotors. The observer intends to use existing sensor measurements to estimate the unknown states of the quadrotor and reconstruct the three-dimensional wind disturbances. The estimated states and external disturbances are sent to the PD controller, which compensates for the disturbances to achieve the desired position and attitude, as well as robustness and accuracy. The Lipschitz observer was designed using the LMI approach, and the results were validated using Matlab/Simulink and using the Parrot Mambo mini quadrotor

Design and Validation of Cyber-Physical Systems Through Co-Simulation: The Voronoi Tessellation Use Case

This paper reports on the use of co-simulation techniques to build prototypes of co-operative autonomous robotic cyber-physical systems. Designing such systems involves a mission-specific planner algorithm, a control algorithm to drive an agent performing its task; and the plant model to simulate the agent dynamics. An application aimed at positioning a swarm of unmanned aerial vehicles (drones) in a bounded area, exploiting a Voronoi tessellation algorithm developed in this work, is taken as a case study. The paper shows how co-simulation allows testing the complex system at the design phase using models created with different languages and tools. The paper then reports on how the adopted co-simulation platform enables control parameters calibration, by exploiting design space exploration technology. The INTO-CPS co-simulation platform, compliant with the Functional Mock-up Interface standard to exchange dynamic simulation models using various languages, was used in this work. The different software modules were written in Modelica, C, and Python. In particular, the latter was used to implement an original variant of the Voronoi algorithm to tesselate a convex polygonal region, by means of dummy points added at appropriate positions outside the bounding polygon. A key contribution of this case study is that it demonstrates how an accurate simulation of a cooperative drone swarm requires modeling the physical plant together with the high-level coordination algorithm. The coupling of co-simulation and design space exploration has been demonstrated to support control parameter calibration to optimize energy consumption and convergence time to the target positions of the drone swarm. From a practical point of view, this makes it possible to test the ability of the swarm to self-deploy in space in order to achieve optimal detection coverage and allow unmanned aerial vehicles in a swarm to coordinate with each other

Robust motion control of nonlinear quadrotor model with wind disturbance observer

This paper focuses on robust wind disturbance rejection for nonlinear quadrotor models. By leveraging on nonlinear unknown observer theory, it proposes a nonlinear dynamic filter that, using sensors already on-board the aircraft, can estimate in real-time wind gust signals in the three dimensions. The wind disturbance is then treated as input to the PD controller for a quick and robust flight pathway in presence of disturbances. With this scheme, the wind disturbance can be precisely estimated online and compensated in real-time. Hence, the quadrotor can successfully reach its desired attitude and position. To show the effective and desired performance of the method, simulation results are presented in Matlab/Simulink and ROS-enabled Gazebo platform

Improving Power Delivery of Grid-Connected Induction Machine Based Wind Generators under Dynamic Conditions Using Feedforward Linear Neural Networks

In the conventional grid-connected Wind Energy Conversion System (WECS), the generator side inverter is typically controlled via Field Oriented Control (FOC), while Voltage Oriented Control (VOC) controls the grid side inverter. However, robust operation cannot be guaranteed during sudden changes in wind speeds and weak grid connections. This paper presents a novel method to improve the overall dynamic performance of on-grid induction machine-based wind generators. An online mechanical parameter estimation technique is devised using Recursive Least Squares (RLS) to compute the machine inertia and friction coefficient iteratively. An adaptive feedforward neural (AFN) controller is also proposed in the synchronous reference frame, which is constructed using the estimated parameters and the system's inverse. The output of the neural controller is added to the output of the speed PI controller in the outer loop of the FOC to enhance the speed response of the wind generator. A similar approach is taken to improve the classical VOC structure for the grid-side inverter. In this case, the RLS estimates the equivalent Thevenin's grid impedance in real-time. As for the adaptive action, two identical neural networks are integrated with the inner loop direct and quadrature axis current PI controllers. Under nominal operating conditions, it is observed that the PI+AFN provides a faster settling time for the generator's speed and torque response. Upon being subjected to variations in the wind speed, the PI+AFN outperforms the classical PI controller and attains a lower integral-time error. In addition, the proposed PI+AFN controller has a better ability to maintain the grid-side inverter stability during stochastic variations in grid impedance. One significant advantage of the proposed control approach is that no data for training or validation is required since the neural network weights are directly the output of the RLS estimator. Hardware verification for the improved FOC for wind generators using the adaptive controller is also made using the DSPACE 1007 AUTOBOX platform

Convergence Analysis of Distributed Set-Valued Information Systems

This paper focuses on the convergence of information in distributed systems of agents communicating over a network. The information on which the convergence is sought is not rep- resented by real numbers, as often in the literature, rather by sets. The dynamics of the evolution of information across the net- work is accordingly described by set-valued iterative maps. While the study of convergence of set-valued iterative maps is highly complex in general, this paper focuses on Boolean maps, which are comprised of arbitrary combinations of unions, intersections, and complements of sets. For these important class of systems, we provide tools to study both global and local convergence. A distributed geographic information system, leading to successful information reconstruction from partial and corrupted data, is used to illustrate the applications of the proposed methods

Wind gust estimation for precise quasi - hovering control of quadrotor aircraft

This paper focuses on the control of quadrotor vehicles without wind sensors that are required to accurately track low-speed trajectories in the presence of moderate yet unknown wind gusts. By modeling the wind disturbance as exogenous inputs, and assuming that compensation of its effects can be achieved through quasistatic vehicle motions, this paper proposes an innovative estimation and control scheme comprising a linear dynamic filter for the estimation of such unknown inputs and requiring only position and attitude information. The filter is built upon results from Unknown Input Observer theory and allows estimation of wind and vehicle state without measurement of the wind itself. A simple feedback control law can be used to compensate for the offset position error induced by the disturbance. The proposed filter is independent of the recovery control scheme used to nullify the tracking error, as long as the corresponding applied rotor speeds are available. The solution is first checked in simulation environment by using the Robot Operating System middleware and the Gazebo simulator and then experimentally validated with a quadcopter system flying with real wind sources

Identification for Control of Variable Impedance Actuators

Development of Variable Impedance Actuators (VIA) is a recent evolution in robotics to face hallenges as adaptability to the environment, energy saving, safety and robustness. VIA allow to change the impedance of the limbs of a robot using physical elastic and dissipative elements rather than through traditional Impedance Control. This leads to the problem of controlling a VIA, one important aspect of this problem lies in the absence of sensors able to measure on-line the mechanical impedance of a system. This thesis deals with the problem of impedance parameters observation in a VIA robot. This in order to develop an instrument to be used in implementing real closed-loop control of impedance of a VIA. After an introduction to VIA and traditional impedance measurement techniques, we follow an innovative approach to derive an observer able to estimate in real-time the impedance of a VIA. In particular three observers are presented: a nonparametric stiffness observer, a parametric stiffness observer, and an impedance observer able to estimate either non-linear time-varying stiffness, as long as linear damping and inertia coefficients. Derivation of the algorithms is shown and both simulation and experimental results are presented to support the thesis

Development, Control, and Empirical Evaluation of the Six-Legged Robot SpaceClimber Designed for Extraterrestrial Crater Exploration

In the recent past, mobile robots played an important role in the field of extraterrestrial surface exploration. Unfortunately, the currently available space exploration rovers do not provide the necessary mobility to reach scientifically interesting places in rough and steep terrain like boulder fields and craters. Multi-legged robots have proven to be a good solution to provide high mobility in unstructured environments. However, space missions place high demands on the system design, control, and performance which are hard to fulfill with such kinematically complex systems. This thesis focuses on the development, control, and evaluation of a six-legged robot for the purpose of lunar crater exploration considering the requirements arising from the envisaged mission scenario. The performance of the developed system is evaluated and optimized based on empirical data acquired in significant and reproducible experiments performed in a laboratory environment in order to show thecapability of the system to perform such a task and to provide a basis for the comparability with other mobile robotic solutions