Yashar Shabbouei Hagh

Multibody system dynamics approaches together with state estimation methods can reduce the need for a large number of sensors, especially in the digital twin of working mobile machinery. To demonstrate this, a hydraulically actuated machine was modeled using the double-step semi-recursive multibody formulation and lumped fluid theory in terms of system independent states. Next, because of the high non-linearity of the modeled system and with respect to the reported performance degradation of the Extended Kalman Filters (EKF), which are mostly related to the linearization procedure of this filter, the Unscented Kalman Filter (UKF) was implemented to achieve high accuracy and performance. The methodology of the proposed approaches was applied to a mobile log crane model PATU 655. The implementation of the proposed estimation algorithms is demonstrated with three different multibody based simulation models: the synthetic real system producing the artificial measurements, the simulation model, and the estimation model. Encoders and pressure sensors, installed on the synthetic real system, provided synthetic sensor measurement data. To mimic real-world conditions, the estimation and simulation models used in the processing of the state estimation algorithm were assumed to have errors in the initial conditions and force model. The proposed UKF was applied to the estimation model with the synthetic sensor measurement data. The minimum percent normalized root mean square errors in the associated measured and unmeasured states of case example were 0.11% and 1.86%, respectively. The UKF using the multibody system dynamics formulations is able to estimate the case example states despite 15% and 60% errors in mass and inertial properties of bodies and Payload, respectively, confirming the accuracy and performance of the algorithm.

Real-world products and physics-based simulations are becoming interconnected. In particular, real-time capable dynamic simulation has made it possible for simulation models to run in parallel and simultaneously with operating machinery. This capability combined with state observer techniques such as Kalman filtering have enabled the synchronization between simulation and the real world. State estimator techniques can be applied to estimate unmeasured quantities, also referred as virtual sensing, or to enhance the quality of measured signals. Although synchronized models could be used in a number of ways, value creation and business model development are currently defining the most practical and beneficial use cases from a business perspective. The research reported here reveals the communication and collaboration methods that lead to economically relevant technology solutions. Two case examples are given that demonstrate the proposed methodology. The work benefited from the broad perspective of researchers from different backgrounds and the joint effort to drive the technology development towards business relevant cases.

This paper describes an estimator incorporating the Unscented Kalman Filter (UKF) technique and multibody system dynamics, to determine the state of the flexible multibody applications. The dynamic equation of the flexible mechanism is formed using a set of non-linear equations as functions of reference and modal coordinates. Since both reference and modal coordinates have no physical meaning, their information is not able to be obtained directly from sensors. Thus, a novel technique is proposed in this work that can successfully translate physical measurements collected by sensors into non-physical modal coordinates. To validate the performance of the proposed modeling technique to apply a UKF to determine the state of a nonlinear flexible multibody system, simulation were carried out for a four-bar mechanism case study to compare the simulation data and UKF data.

This paper proposes a novel robust proportional integral non-singular terminal synergetic control (PI-NTSC) for a class of affine nonlinear systems. To counteract the effects of the disturbances, a hybrid nonlinear disturbance observer (HNDOB) scheme is proposed to estimate the matched and mismatched disturbances. A novel proportional integral non-singular terminal synergetic control (PI-NTSC) is proposed for a class of affine nth-order systems. The stability of the PI-NTSC approach is proven using the Lyapunov stability theory. Its effectiveness and applicability is evaluated through simulations of a four-bar linkage mechanism and a servo-hydraulic system. The proposed HNDOB-based PI-NTSC is characterized by a finite time convergence rate, simple implementation, better disturbance rejection ability to both matched and mismatched uncertainties, and chattering free dynamics compared to conventional sliding mode control.

This paper proposes an active fault-tolerant control (FTC) scheme for robotic manipulators subject to actuator faults. Its main objective is to mitigate actuator faults and maintain system performance and stability, even under faulty conditions. The proposed FTC design combines the robustness and finite time convergence of non-singular terminal synergetic control with the optimization properties of an interval type-2 fuzzy satin bowerbird algorithm. System stability is established via the Lyapunov stability criteria. An adaptive state-augmented extended Kalman filter is implemented as the fault detection and diagnosis (FDD) module, to provide the controller with necessary information about faults in real time. This FDD scheme is based on the simultaneous estimation of the faulty parameters and system states. The effectiveness of the proposed approach is assessed using a simulated two-degree-of-freedom robotic manipulator subject to various faulty scenarios.