The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system

Simulating a Flexible Robotic System based on Musculoskeletal Modeling

Humanoid robotics offers a unique research tool for understanding the human brain and body. The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research, with the recent advent of complex humanoid systems. This work presents the design and development of a new-generation bipedal robot. Its modeling and simulation has been realized by using an open-source software to create and analyze dynamic simulation of movement: OpenSim. Starting from a study by Fuben He, our model aims to be used as an innovative approach to the study of a such type of robot in which there are series elastic actuators represented by active and passive spring components in series with motors. It has provided of monoarticular and biarticular joint in a very similar manner to human musculoskeletal model. This thesis is only the starting point of a wide range of other possible future works: from the control structure completion and whole-body control application, to imitation learning and reinforcement learning for human locomotion, from motion test on at ground to motion test on rough ground, and obviously the transition from simulation to practice with a real elastic bipedal robot biologically-inspired that can move like a human bein

Bipedal humanoid robot walking reference tuning by the use of evolutionary algorithms

Various aspects of humanoid robotics attracted the attention of researchers in the past four decades. One of the most challenging tasks in this area is the control of bipedal locomotion. The dynamics involved are highly nonlinear and hard to stabilize. A typical fullbody humanoid robot has more than twenty joints and the coupling effects between the links are significant. Reference generation plays a vital role for the success of the walking controller. Stability criteria including the Zero Moment Point (ZMP) criterion are extensively applied for this purpose. However, the stability criteria are usually applied on simplified models like the Linear Inverted Pendulum Model (LIPM) which only partially describes the equations of the motion of the robot. There are also trial and error based techniques and other ad-hoc reference generation techniques as well. This background of complicated dynamics and difficulties in reference generation makes automatic gait (step patterns of legged robots) tuning an interesting area of research. A natural command for a legged robot is the velocity of its locomotion. A number of walk parameters including temporal and spatial variables like stepping period and step size need to be set properly in order to obtain the desired speed. These problems, when considered from kinematics point of view, do not have a unique set of walking parameters as a solution. However, some of the solutions can be more suitable for a stable walk, whereas others may lead to instability and cause robot to fall. This thesis proposes a gait tuning method based on evolutionary methods. A velocity command is given as the input to the system. A ZMP based reference generation method is employed. Walking simulations are performed to assess the fitness of artificial populations. The fitness is measured by the amount of support the simulated bipedal robot received from torsional virtual springs and dampers opposing the changes in body orientation. Cross-over and mutation mechanisms generate new populations. A number of different walking parameters and fitness functions are tested to improve this tuning process. The walking parameters obtained in simulations are applied to the experimental humanoid platform SURALP (Sabanci University ReseArch Labaratory Platform). Experiments verify the merits of the proposed reference tuning method

Bio-inspired robotic control in underactuation: principles for energy efficacy, dynamic compliance interactions and adaptability.

Biological systems achieve energy efficient and adaptive behaviours through extensive autologous and exogenous compliant interactions. Active dynamic compliances are created and enhanced from musculoskeletal system (joint-space) to external environment (task-space) amongst the underactuated motions. Underactuated systems with viscoelastic property are similar to these biological systems, in that their self-organisation and overall tasks must be achieved by coordinating the subsystems and dynamically interacting with the environment. One important question to raise is: How can we design control systems to achieve efficient locomotion, while adapt to dynamic conditions as the living systems do? In this thesis, a trajectory planning algorithm is developed for underactuated microrobotic systems with bio-inspired self-propulsion and viscoelastic property to achieve synchronized motion in an energy efficient, adaptive and analysable manner. The geometry of the state space of the systems is explicitly utilized, such that a synchronization of the generalized coordinates is achieved in terms of geometric relations along the desired motion trajectory. As a result, the internal dynamics complexity is sufficiently reduced, the dynamic couplings are explicitly characterised, and then the underactuated dynamics are projected onto a hyper-manifold. Following such a reduction and characterization, we arrive at mappings of system compliance and integrable second-order dynamics with the passive degrees of freedom. As such, the issue of trajectory planning is converted into convenient nonlinear geometric analysis and optimal trajectory parameterization. Solutions of the reduced dynamics and the geometric relations can be obtained through an optimal motion trajectory generator. Theoretical background of the proposed approach is presented with rigorous analysis and developed in detail for a particular example. Experimental studies are conducted to verify the effectiveness of the proposed method. Towards compliance interactions with the environment, accurate modelling or prediction of nonlinear friction forces is a nontrivial whilst challenging task. Frictional instabilities are typically required to be eliminated or compensated through efficiently designed controllers. In this work, a prediction and analysis framework is designed for the self-propelled vibro-driven system, whose locomotion greatly relies on the dynamic interactions with the nonlinear frictions. This thesis proposes a combined physics-based and analytical-based approach, in a manner that non-reversible characteristic for static friction, presliding as well as pure sliding regimes are revealed, and the frictional limit boundaries are identified. Nonlinear dynamic analysis and simulation results demonstrate good captions of experimentally observed frictional characteristics, quenching of friction-induced vibrations and satisfaction of energy requirements. The thesis also performs elaborative studies on trajectory tracking. Control schemes are designed and extended for a class of underactuated systems with concrete considerations on uncertainties and disturbances. They include a collocated partial feedback control scheme, and an adaptive variable structure control scheme with an elaborately designed auxiliary control variable. Generically, adaptive control schemes using neural networks are designed to ensure trajectory tracking. Theoretical background of these methods is presented with rigorous analysis and developed in detail for particular examples. The schemes promote the utilization of linear filters in the control input to improve the system robustness. Asymptotic stability and convergence of time-varying reference trajectories for the system dynamics are shown by means of Lyapunov synthesis

14th Conference on Dynamical Systems Theory and Applications DSTA 2017 ABSTRACTS

From Preface: This is the fourteen time when the conference “Dynamical Systems – Theory and Applications” gathers a numerous group of outstanding scientists and engineers, who deal with widely understood problems of theoretical and applied dynamics. Organization of the conference would not have been possible without a great effort of the staff of the Department of Automation, Biomechanics and Mechatronics. The patronage over the conference has been taken by the Committee of Mechanics of the Polish Academy of Sciences and the Ministry of Science and Higher Education. It is a great pleasure that our invitation has been accepted by so many people, including good colleagues and friends as well as a large group of researchers and scientists, who decided to participate in the conference for the first time. With proud and satisfaction we welcome nearly 250 persons from 38 countries all over the world. They decided to share the results of their research and many years experiences in the discipline of dynamical systems by submitting many very interesting papers. This booklet contains a collection of 375 abstracts, which have gained the acceptance of referees and have been qualified for publication in the conference proceedings [...]

Sim-to-Real Reinforcement Learning Framework for Autonomous Aerial Leaf Sampling

Using unmanned aerial systems (UAS) for leaf sampling is contributing to a better understanding of the influence of climate change on plant species, and the dynamics of forest ecology by studying hard-to-reach tree canopies. Currently, multiple skilled operators are required for UAS maneuvering and using the leaf sampling tool. This often limits sampling to only the canopy top or periphery. Sim-to-real reinforcement learning (RL) can be leveraged to tackle challenges in the autonomous operation of aerial leaf sampling in the changing environment of a tree canopy. However, trans- ferring an RL controller that is learned in simulation to real UAS applications is challenging due to the risk of crashes. UAS crashes pose safety risks to the operator and its surroundings which often leads to expensive UAS repairs. In this thesis, we present a Sim-to-Real Transfer framework using a computer numerical control (CNC) platform as a safer, and more robust proxy, before using the controller on a UAS. In addition, our framework provides an end-to-end complete pipeline to learn, and test, any deep RL controller for UAS or any three-axis robot for various control tasks. Our framework facilitates bi-directional iterative improvements to the simulation environment and real robot, by allowing instant deployment of the simulation learned controller to the real robot for performance verification and issue identification. Our results show that we can perform a zero-shot transfer of the RL agent, which is trained in simulation, to real CNC. The accuracy and precision do not meet the requirement for complex leaf sampling tasks yet. However, the RL agent trained for a static target following still follows or attempts to follow more dynamic and changing targets with predictable performance. This works lays the foundation by setting up the initial validation requirements for the leaf sampling tasks and identifies potential areas for improvement. Further tuning of the system and experimentation of the RL agent type would pave the way to autonomous aerial leaf sampling. Adviser: Carrick Detweile

Chaotic exploration and learning of locomotor behaviours

Recent developments in the embodied approach to understanding the generation of adaptive behaviour, suggests that the design of adaptive neural circuits for rhythmic motor patterns should not be done in isolation from an appreciation, and indeed exploitation, of neural-body-environment interactions. Utilising spontaneous mutual entrainment between neural systems and physical bodies provides a useful passage to the regions of phase space which are naturally structured by the neuralbody- environmental interactions. A growing body of work has provided evidence that chaotic dynamics can be useful in allowing embodied systems to spontaneously explore potentially useful motor patterns. However, up until now there has been no general integrated neural system that allows goal-directed, online, realtime exploration and capture of motor patterns without recourse to external monitoring, evaluation or training methods. For the first time, we introduce such a system in the form of a fully dynamic neural system, exploiting intrinsic chaotic dynamics, for the exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modelled as a network of neural oscillators which are coupled only through physical embodiment, and goal directed exploration of coordinated motor patterns is achieved by a chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organised dynamics each of which is a candidate for a locomotion behaviour. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states using its intrinsic chaotic dynamics as a driving force and stabilises the system on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced which results in an increased diversity of motor outputs, thus achieving multi-scale exploration. A rhythmic pattern discovered by this process is memorised and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronisation method. The dynamical nature of the weak coupling through physical embodiment allows this adaptive weight learning to be easily integrated, thus forming a continuous exploration-learning system. Our result shows that the novel neuro-robotic system is able to create and learn a number of emergent locomotion behaviours for a wide range of body configurations and physical environment, and can re-adapt after sustaining damage. The implications and analyses of these results for investigating the generality and limitations of the proposed system are discussed

Control oriented modelling of an integrated attitude and vibration suppression architecture for large space structures

This thesis is divided into two parts. The main focus of the research, namely active vibration control for large flexible spacecraft, is exposed in Part I and, in parallel, the topic of machine learning techniques for modern space applications is described in Part II. In particular, this thesis aims at proposing an end-to-end general architecture for an integrated attitude-vibration control system, starting from the design of structural models to the synthesis of the control laws. To this purpose, large space structures based on realistic missions are investigated as study cases, in accordance with the tendency of increasing the size of the scientific instruments to improve their sensitivity, being the drawback an increase of its overall flexibility. An active control method is therefore investigated to guarantee satisfactory pointing and maximum deformation by avoiding classical stiffening methods. Therefore, the instrument is designed to be supported by an active deployable frame hosting an optimal minimum set of collocated smart actuators and sensors. Different spatial configurations for the placement of the distributed network of active devices are investigated, both at closed-loop and open-loop levels. Concerning closed-loop techniques, a method to optimally place the poles of the system via a Direct Velocity Feedback (DVF) controller is proposed to identify simultaneously the location and number of active devices for vibration control with an in-cascade optimization technique. Then, two general and computationally efficient open-loop placement techniques, namely Gramian and Modal Strain Energy (MSE)-based methods, are adopted as opposed to heuristic algorithms, which imply high computational costs and are generally not suitable for high-dimensional systems, to propose a placement architecture for generically shaped tridimensional space structures. Then, an integrated robust control architecture for the spacecraft is presented as composed of both an attitude control scheme and a vibration control system. To conclude the study, attitude manoeuvres are performed to excite main flexible modes and prove the efficacy of both attitude and vibration control architectures. Moreover, Part II is dedicated to address the problem of improving autonomy and self-awareness of modern spacecraft, by using machine-learning based techniques to carry out Failure Identification for large space structures and improving the pointing performance of spacecraft (both flexible satellite with sloshing models and small rigid platforms) when performing repetitive Earth Observation manoeuvres

Modelování indentačních testů metodou konečných prvků a aplikace modelu neuronových sítí

The diploma work investigates the Instrumented Indentation Test (IIT) modelling with Finite Element Method (FEM) and Neural Network (NN). The final goal is to create a NN that can evaluate the experimental data from a physical IIT in such a way to directly calibrate a suitable plasticity model. The study is conducted on an austenitic stainless steel SS304L which is a challenge for IIT. To achieve the goal, IIT is performed on a SS304L specimen to obtain the indenting depth versus the reaction force the material exerts on the indentation ball. Besides, tensile tests with Digital Image Correlation (DIC) on the same material are performed to obtain the tensile stress-strain curve, from which parameters of a constitutive model can be identified. The results from IIT and tensile test are then used to establish a FEM model. Using the FEM model, several IITs can be simulated to create a dataset for NN training in the range of austenitic steels. This dataset is fed into a NN and numerical experiments are done to find an optimal architecture for the final goal. The thesis proposes a framework and proves its feasibility considering Armstrong-Frederick hardening model with three parameters. All the data and identified challenges are reported and discussed.Diplomová práce zkoumá modelování instrumentovaného indentačního testu (IIT) s využitím metody konečných prvků (MKP) a neuronových sítí (NN). Cílem práce je vytvořit NN, která bude schopna vyhodnotit experimentální data z realizovaného IIT takovým způsobem, aby přímo kalibrovala vhodný model plasticity. Studie je prováděna na austenitické nerezové oceli SS304L, která představuje výzvu pro IIT. K dosažení cíle je proveden IIT na vzorku SS304L k získání závislosti hloubky zatlačení na reakční síle, kterou materiál vyvíjí na vtlačovanou kuličku. Kromě toho jsou na stejném materiálu provedeny tahové zkoušky s digitální korelací obrazu (DIC) k získání tahové deformační křivky, z které lze identifikovat parametry konstitutivního modelu. Výsledky z IIT a tahové zkoušky jsou poté použity k vytvoření MKP modelu. S využitím FEM modelu lze simulovat několik IIT a vytvořit tak soubor dat pro trénování NN v rozsahu austenitických ocelí. Tento soubor dat je vložen do NN. K nalezení optimální architektury pro dosažení finálního cíle jsou provedeny numerické experimenty. Práce navrhuje rámec a dokazuje jeho proveditelnost při uvažování Armstrong-Frederickova modelu zpevnění se třemi parametry. Všechna data a zjištěné výzvy jsou zaznamenány a diskutovány.330 - Katedra aplikované mechanikyvýborn