Reinforcement Learning for Robot Motion Planning Facilitated by Implicit Behavior Cloning and Dynamic Movement Primitive (IBC-DMP RL)

This dataset contains the programs to train and test the implicit behavior cloning (IBC) dynamic movement primitive (DMP) reinforcement learning (RL) agent for robot motion planning. It is associated with an under-reviewed journal paper with the same title. See ReadMe.md in the zip file for details

Dataset of Human Hand Motion Planning

This dataset contains 544 human hand motion trajectories in a point-to-point reaching experiment. The purpose of this dataset is to provide human demonstrations for imitation-learning- and reinforcement-learning -based robot motion planning. Refer to 'ReadMe.md' for the details about the format and usage of the dataset

Decentralized Optimal Coverage Control for Constant-Speed Unicycle Multi-Agent Systems

This dataset contains the program code for the following publication: Liu, Qingchen, Zengjie Zhang, Nhan Khanh Le, Jiahu Qin, Fangzhou Liu, and Sandra Hirche. "Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems." IEEE Transactions on Automation Science and Engineering (2024). It can be used for coverage control of fixed-wing drones

Parallel Wiener-Hammerstein Time Series

A Parallel Wiener-Hammerstein system is a nonlinear dynamical system obtained by connecting multiple Wiener-Hammerstein systems in parallel. Each parallel branch contains a static nonlinearity that is sandwiched in between two linear time-invariant (LTI) blocks. The presence of the two LTI blocks, and the parallel branches results in a problem that is harder to identify. The LTI blocks are realized as active filters while the static nonlinearity is implemented as a diode-resistor electronic circuit. The provided data was part of a previously published Automatica paper available online at Sciencedirect or as an ArXiv preprint. The Parallel Wiener-Hammerstein system, the measurement setup, and the input signals used are detailed in Section 10 of the aforementioned paper. This zip-file contains multiple measured input-output time series: a multisine estimation/validation data set, and a multisine and increasing-amplitude test data set. The data is available in the .csv and .mat file format.

F-16 Aircraft Benchmark Based on Ground Vibration Test Data

The F-16 Ground Vibration Test benchmark features a high order system with clearance and friction nonlinearities at the mounting interface of the payloads. The experimental data made available here were acquired on a full-scale F-16 aircraft on the occasion of the Siemens LMS Ground Vibration Testing Master Class. During the test campaign, two dummy payloads were mounted at the wing tips to simulate the mass and inertia properties of real devices typically equipping an F-16 in flight. The aircraft structure was instrumented with accelerometers. One shaker was attached underneath the right wing to apply input signals. The dominant source of nonlinearity in the structural dynamics was expected to originate from the mounting interfaces of the two payloads. These interfaces consist of T-shaped connecting elements on the payload side, slid through a rail attached to the wing side. A preliminary investigation showed that the back connection of the right-wing-to-payload interface was the predominant source of nonlinear distortions in the aircraft dynamics, and is therefore the focus of this benchmark study. All the provided files and information together with a detailed description of the F-16 aircraft benchmark system are available for download here. This zip-file contains a detailed system description, the estimation and test data sets, and some pictures of the setup. The data is available in the .csv and .mat file format

SySCoRe (ARCH 2022)

Synthesis via Stochastic Coupling Relations (SysCoRe) for stochastic continuous-state systems

Hysteretic Benchmark with a Dynamic Nonlinearity

Hysteresis is a dynamic nonlinearity commonly encountered in very diverse engineering and science disciplines, ranging from solid mechanics, electromagnetism and aerodynamics to biology, ecology and psychology. In particular, the Bouc-Wen model has been intensively exploited during the last decades to represent hysteretic effects in mechanical engineering, especially in the case of random vibrations. It is proposed as a benchmark problem to identify a Bouc-Wen system based on synthetic input-output data time-series. A detailed formulation of the identification problem can be downloaded here. All the provided files and information on the Bouc-Wen system are available for download. The zip-file contains a detailed system description with a signal generation guide and the test data sets. This benchmark requires MATLAB to run

Wiener-Hammerstein benchmark with process noise

The Wiener-Hammerstein system structure is a well-known block-oriented structure. It contains a static nonlinearity that is sandwiched in between two linear time-invariant (LTI) blocks. The presence of the two LTI blocks results in a problem that is harder to identify. The LTI blocks are realized as active filters while the static nonlinearity is implemented as a diode-resistor electronic circuit. The Wiener-Hammerstein system proposed here as a benchmark contains dominant process noise. The process noise enters the system before the static nonlinearity. Two much less significant noise sources are present in the measurement channels of the input and output. All the provided files, data and information on the Wiener-Hammerstein system are available for download here together with an in-depth description of the measured input-output time series. This zip-file contains a detailed system description, an example estimation data set, the test data sets, the datasets measured during the past measurement campaign(s), pictures of the measurement setup, and an indicative electrical circuit schematic of the system. It is possible that the actual implemented Wiener-Hammerstein system deviates at some points (resistor values, opamp type) from the electrical circuit provided here. The data is available in the .csv and .mat file format

Cascaded Tanks Benchmark Combining Soft and Hard Nonlinearities

The considered cascaded tanks system is a fluid level control system consisting of two tanks with free outlets fed by a pump. The input signal controls a water pump that delivers the water from a reservoir into the upper water tank. The water of the upper tank flows through a small opening into the lower tank, and finally through a small opening from the lower tank back into the reservoir. This benchmark combines soft and hard nonlinearities to be identified based on relatively short data records. All the provided files and information on the cascaded tanks system are available for download here. The zip-file contains a detailed system description, the estimation and test input-output time series data sets, and some pictures and a video of the setup. The data is available in the .csv and .mat file format

SySCoRe: Synthesis via Stochastic Coupling Relations

SySCoRe is a MATLAB toolbox that synthesizes controllers for stochastic continuous-state systems to satisfy temporal logic specifications. Starting from a system description and a co-safe temporal logic specification, SySCoRe provides all necessary functions for synthesizing a robust controller and quantifying the associated formal robustness guarantees. It distinguishes itself from other available tools by supporting nonlinear dynamics, complex co-safe temporal logic specifications over infinite horizons and model-order reduction. To achieve this, SySCoRe first generates a finite-state abstraction of the provided model and performs probabilistic model checking. Then, it establishes a probabilistic coupling to the original stochastic system encoded in an approximate simulation relation, based on which a lower bound on the satisfaction probability is computed. SySCoRe provides non-trivial lower bounds for infinite-horizon properties and unbounded disturbances since its computed error does not grow linear in the horizon of the specification. It exploits a tensor representation to facilitate the efficient computation of transition probabilities. We showcase these features on several benchmarks