From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

Control of an over-actuated nanopositioning system by means of control allocation

This Master’s Thesis is devoted to the analysis and design of a control structure for the nanopositioning system LAU based on the dynamic control allocation technique. The objective is to control the vertical displacement with nanometer precision under a control effort distribution criterion among the actuator set. In this case, the pneumatic actuator is used as a passive gravity compensator while the voice coil motor generates the transient forces. The analysis of the system characteristics allows defining the design criterion for the control allocation. In this direction, the proposed dynamic control allocation stage considers a frequency distribution of the control effort. The lower frequency components are assigned to the pneumatic actuator while the higher frequencies are handled by the voice coil drive. The significant actuator dynamics are compensated through a Kalman filter approach. The position controller is based on a feedback linearization framework with a disturbance observer for enhanced robustness. The experimental validation demonstrates the feasibility of the proposed technique.Diese Masterarbeit widmet sich der Analyse und dem Entwurf einer Regelungsstruktur für das Nanopositioniersystem LAU. Dabei werden Methoden untersucht, welche das notwendige Stellsignal auf zwei Aktoren aufteilen. Ziel ist es, die vertikale Verschiebung des LAU mit Nanometerpräzision zu regeln. In diesem Fall wird der pneumatische Aktor als passiver Schwerkraftkompensator verwendet, während die elktromagnetische Tauchspule die transienten Kräfte erzeugt. Die Analyse der Eigenschaften des LAUSystems ermöglicht die Definition der Entwurfskriterien zur Aufteilung der Stellgröße. In dieser Richtung berücksichtigt die vorgeschlagene dynamische Methode eine Aufteilung der Stellgröße bezüglich der Frequenzanteile. Die niederfrequenten Komponenten werden dem pneumatischen Aktor zugeordnet. Dem elektromagnetische Aktor werden die verbliebenen hochfrequenten Anteile zugeordnet. Die signifikanten Effekte der Aktordynamik in Bezug auf die Bewegungsdynamik werden durch einen Kalman- Filteransatz kompensiert. Nichtlineare Streckenanteile werden basierend auf dem Modell und einem Störbeobachter kompensiert, sodass der verbleibende Anteil des Positionsreglers mit linearen Methoden entworfen werden kann. Die experimentelle Validierung zeigt die Effektivität des untersuchten Konzeptes.Tesi

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems

Direct torque control for cable conduit mechanisms for the robotic foot for footwear testing

© 2018 Elsevier Ltd As the shoe durability is affected directly by the dynamic force/pressure between the shoe and its working environments (i.e., the contact ground and the human foot), a footwear testing system should replicate correctly this interaction force profile during gait cycles. Thus, in developing a robotic foot for footwear testing, it is important to power multiple foot joints and to control their output torque to produce correct dynamic effects on footwear. The cable conduit mechanism (CCM) offers great advantages for designing this robotic foot. It not only eliminates the cumbersome actuators and significant inertial effects from the fast-moving robotic foot but also allows a large amount of energy/force to be transmitted/propagated to the compact robotic foot. However, CCMs cause nonlinearities and hysteresis effects to the system performance. Recent studies on CCMs and hysteresis systems mostly addressed the position control. This paper introduces a new approach for modelling the torque transmission and controlling the output torque of a pair of CCMs, which are used to actuate the robotic foot for footwear testing. The proximal torque is used as the input signal for the Bouc–Wen hysteresis model to portray the torque transmission profile while a new robust adaptive control scheme is developed to online estimate and compensate for the nonlinearities and hysteresis effects. Both theoretical proof of stability and experimental validation of the new torque controller have been carried out and reported in this paper. Control experiments of other closed-loop control algorithms have been also conducted to compare their performance with the new controller effectiveness. Qualitative and quantitative results show that the new control approach significantly enhances the torque tracking performance for the system preceded by CCMs

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

Adaptive Compensation of Traction System Actuator Failures for High-Speed Trains

In this paper, an adaptive failure compensation problem is addressed for high-speed trains with longitudinal dynamics and traction system actuator failures. Considered the time-varying parameters of the train motion dynamics caused by time-varying friction characteristics, a new piecewise constant model is introduced to describe the longitudinal dynamics with variable parameters. For both the healthy piecewise constant system and the system with actuator failures, the adaptive controller structure and conditions are derived to achieve the plant-model matching. The adaptive laws are designed to update the adaptive controller parameters, in the presence of the system piecewise constant parameters and actuator failure parameters which are unknown. Based on Lyapunov functions, the closed-loop stability and asymptotic state tracking are proved. Sim-ulation results on a high-speed train model are presented to illustrate the performance of the developed adaptive actuator failure compensation control scheme

Decentralised vibration control

Master'sMASTER OF ENGINEERIN

Towards a safe and responsive control framework for human-centered robots

Human-centered robots are a specific kind of service robot, which interact with humans physically or cognitively and help humans with tasks in uncertain environments. They can be humanoid robots, exoskeletons, or manipulators and mobile platforms that provide us good services. However, human-centered robots are still not perfect enough for us to use nowadays. On the one hand, human-centered robots are still slow and inefficient for their tasks because the human inputs and dynamics that they react to are uncertain, immeasurable, or even completely unknown. On the other hand, human-centered robots face much more complicated safety requirements than other kinds of robots because humans are dynamic and vulnerable during physical human-robot interaction. To resolve these issues of human-centered robots, the work in this dissertation explores new models for reducing human uncertainty and new control algorithms for improving safety warranty. The first half of this dissertation introduces a complex stiffness model for describing the uncertain human impedance. The discovery of this new model is motivated to explain the observation of a consistent damping ratio of a human versus different environmental dynamics. It replaces the linear damping term in a conventional mass-spring-damping model with a hysteretic damping term, an imaginary value in the frequency domain. Because of the correlation between the stiffness term and the newly discovered hysteretic damping term in the complex stiffness model, we can significantly reduce the human impedance uncertainty. Based on the complex stiffness model, we can adopt nonlinear control strategies for improving the responsiveness and the human-friendliness of human-centered robots. The second half of this dissertation introduces the concept of a barrier pair, which consists of a barrier function and a controller for the safety verification and warranty of a human-centered robot. We obtain a barrier pair by solving an optimization problem subject to a series of linear matrix inequalities representing the state-space, input, and stability constraints of a human-centered robot. By incorporating sampling-based methods into the synthesis of barrier pairs, human-centered robots can guarantee safe operation with non-convex state-space constraints. The sampling-based barrier pair method helps us construct a control framework of human-robot shared autonomy. A human-centered robot in this control framework uses an inference of a human's objective to figure out how to assist the human and prevent the human from potential accidents.Mechanical Engineerin