Actuator Constrained Motion Cueing Algorithm for a Redundantly Actuated Stewart Platform

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Justin Pradipta ◽  
Oliver Sawodny
Keyword(s):  
Optimization Problem ◽  
Flight Simulation ◽  
Constrained Motion ◽  
Motion Platform ◽  
Case Scenario ◽  
Worst Case ◽  
Motion Cues ◽  
Motion Cueing ◽  
Redundantly Actuated ◽  
Relationship Of

An improved method to provide a motion trajectory for full flight simulator to simulate the acceleration during a flight simulation is presented. The motion cueing trajectory is based on a constrained optimization problem, with the generated optimal acceleration cues subjected to the actuators travel constraints of the motion platform. The motion platform researched in this contribution is a redundantly actuated parallel manipulator, therefore the available workspace is more limited and the actuator constraints become more complex. The differential kinematic analysis is utilized in the optimization problem to define the relationship of the acceleration in the platform coordinate and in the actuator coordinates. An acceleration profile is defined in function of the actuator travel to create a strict acceleration constraint in the actuator coordinate, thus a strict travel constraint. The algorithm is tested in a simulation and implemented in a full size redundantly actuated motion platform. Measurement results show that the proposed new motion cueing algorithm (MCA) is able to keep the actuators within their travel limit and at the same time provide the correct motion cues for the simulator pilots. The need to tune the MCA for the worst case scenario which is necessary to avoid damage to the platform, while at the same time can be disadvantageous for the normal case use, is relieved by the utilization of the online optimization process.

Reliability-Based Design Optimization of Microstructures With Analytical Formulation

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Pinar Acar
Keyword(s):  
Design Optimization ◽  
Material Properties ◽  
Optimization Problem ◽  
Optimization Under Uncertainty ◽  
Reliability Based Design Optimization ◽  
Case Scenario ◽  
Worst Case ◽  
Analytical Formulation ◽  
Reliability Based Design ◽  
Safe Approach

Microstructures are stochastic by their nature. These aleatoric uncertainties can alter the expected material performance substantially and thus they must be considered when designing materials. One safe approach would be assuming the worst case scenario of uncertainties in design. However, design under the worst case conditions can lead to over-conservative solutions that provide less effective material properties. Here, a more powerful design approach can be developed by implementing reliability constraints into the optimization problem to achieve superior material properties while satisfying the prescribed design criteria. This is known as reliability-based design optimization (RBDO), and it has not been studied for microstructure design before. In this work, an analytical formulation that models the propagation of microstructural uncertainties to the material properties is utilized to compute the probability of failure. Next, the analytical uncertainty solution is integrated into the optimization problem to define the reliability constraints. The presented optimization under uncertainty scheme is exercised to maximize the yield stress of α-Titanium and magnetostriction of Galfenol, respectively.

scholarly journals MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints

Vehicles ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 625-647
Author(s):  
Yash Raj Khusro ◽  
Yanggu Zheng ◽  
Marco Grottoli ◽  
Barys Shyrokau
Keyword(s):  
Driving Simulator ◽  
Motion Platform ◽  
Driver Training ◽  
Human Machine Interaction ◽  
Reference Tracking ◽  
Motion Cues ◽  
Adaptive Weights ◽  
Washout Filter ◽  
Motion Cueing ◽  
And Performance

Driving simulators are widely used for understanding human–machine interaction, driver behavior and in driver training. The effectiveness of simulators in this process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion-cueing strategies have provided reasonable results, these methods suffer from poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is nonlinear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. This paper presents a nonlinear MPC-based algorithm which incorporates the nonlinear kinematics of the Stewart platform within the MPC algorithm in order to increase the cueing fidelity and use maximum workspace. Furthermore, adaptive weights-based tuning is used to smooth the movement of the platform towards its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights-based tuning was also observed in the form of smoother actuator movements and better workspace use.

scholarly journals Secrecy Wireless-Powered Sensor Networks for Internet of Things

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Junxia Li ◽  
Hui Zhao ◽  
Xueyan Chen ◽  
Zheng Chu ◽  
Li Zhen ◽  
...  
Keyword(s):  
Time Allocation ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Access Point ◽  
Case Scenario ◽  
Secrecy Rate ◽  
Global Optimal Solution ◽  
Worst Case ◽  
Sdp Relaxation ◽  
Global Optimal

This paper investigates a secure wireless-powered sensor network (WPSN) with the aid of a cooperative jammer (CJ). A power station (PS) wirelessly charges for a user equipment (UE) and the CJ to securely transmit information to an access point (AP) in the presence of multiple eavesdroppers. Also, the CJ are deployed, which can introduce more interference to degrade the performance of the malicious eavesdroppers. In order to improve the secure performance, we formulate an optimization problem for maximizing the secrecy rate at the AP to jointly design the secure beamformer and the energy time allocation. Since the formulated problem is not convex, we first propose a global optimal solution which employs the semidefinite programming (SDP) relaxation. Also, the tightness of the SDP relaxed solution is evaluated. In addition, we investigate a worst-case scenario, where the energy time allocation is achieved in a closed form. Finally, numerical results are presented to confirm effectiveness of the proposed scheme in comparison to the benchmark scheme.

scholarly journals AKIBAT HUKUM WANPRESTASI DAN TANGGUNG JAWAB PARA PIHAK DALAM TRANSAKSI ANJAK PIUTANG

2019 ◽  
Vol 5 (2) ◽  
pp. 374-398
Author(s):  
Sudjana Sudjana
Keyword(s):  
Financial Risk ◽  
Legal Issues ◽  
Case Scenario ◽  
Breach Of Contract ◽  
Worst Case ◽  
Normative Approach ◽  
Worst Case Scenario ◽  
Legal Relationship ◽  
The Right ◽  
Relationship Of

In this article, using a juridical-normative approach, the author discusses legal issues stemming from the breach of two interlocking contracts: supplier agreement and factoring agreement.  To be analysed is the legal relationship of all parties in the case of breach of contract or worse bankruptcy of the supplier.  Issues to be raised in particular concerns who in the case of breach of contract will in the end possess the right to demand payment of outstanding debts and who bear the (legal and financial) risk in the worst case scenario: bankruptcy of buyer.

scholarly journals MPC-Based Motion Cueing Algorithm for a 6 DOF Driving Simulator with Actuator Constraints

2020 ◽  
Author(s):  
Yash Raj Khusro ◽  
Yanggu Zheng ◽  
Marco Grottoli ◽  
Barys Shyrokau
Keyword(s):  
Driving Simulator ◽  
Motion Platform ◽  
Driver Training ◽  
Human Machine Interaction ◽  
Reference Tracking ◽  
Motion Cues ◽  
Adaptive Weights ◽  
Washout Filter ◽  
Motion Cueing ◽  
And Performance

Driving simulators are widely used for understanding human-machine interaction, driver behavior and in driver training. The effectiveness of simulators in these process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion cueing strategies have provided reasonable results, these methods suffer from poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is non-linear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. This paper presents a nonlinear MPC-based algorithm which incorporates the nonlinear kinematics of the Stewart platform within the MPC algorithm in order to increase the cueing fidelity and utilize maximum workspace. Further, adaptive weights-based tuning is used to smoothen the movement of the platform towards its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights-based tuning was also observed in the form of smoother actuator movements and better workspace utilization.

Motion Cueing Algorithms: A Review

2017 ◽  
Vol 1 (1) ◽  
pp. 90-106 ◽  
Author(s):  
Sergio Casas ◽  
Ricardo Olanda ◽  
Nilanjan Dey
Keyword(s):  
Predictive Control ◽  
Control Strategies ◽  
General Scheme ◽  
Level Control ◽  
Motion Platform ◽  
Motion Cues ◽  
Vehicle Simulation ◽  
Robotic Motion ◽  
Motion Cueing ◽  
High Level

Robotic motion platforms are commonly used in motion-based vehicle simulation. However, the reproduction of realistic accelerations within a reduced workspace is a major challenge. Thus, high-level control strategies commonly referred to as motion cueing algorithms (MCA) are required to convert the simulated vehicle physical state into actual motion for the motion platform. This paper reviews the most important strategies for the generation of motion cues in simulators, listing the advantages and drawbacks of the different solutions. The motion cueing problem, a general scheme and the four most common approaches – classical washout, adaptive washout, optimal control and model predictive control – are presented. The existing surveys of the state-of-the-art on motion cueing are usually limited to list the MCA or to a particular vehicle application. In this work, a comprehensive vehicle-agnostic review is presented. Moreover, evaluation and tuning of MCA are also considered, classifying the different methods, and providing examples of each class.

Reducing Probabilistic Weather Forecasts to the Worst Case Scenario: Anchoring Effects

2008 ◽  
Author(s):  
Sonia Savelli ◽  
Susan Joslyn ◽  
Limor Nadav-Greenberg ◽  
Queena Chen
Keyword(s):  
Case Scenario ◽  
Worst Case ◽  
Weather Forecasts ◽  
Worst Case Scenario ◽  
Anchoring Effects

EXPEDITION TO MALI, 2020

2020 ◽  
pp. 12-28
Author(s):  
D. V. Vaniukova ◽  
◽  
P. A. Kutsenkov ◽  
Keyword(s):  
Mass Murder ◽  
The State ◽  
Case Scenario ◽  
Worst Case ◽  
Traditional Art ◽  
Peter The Great ◽  
State Museum ◽  
Bobo Dioulasso ◽  
Oriental Studies ◽  
The Village

The research expedition of the Institute of Oriental studies of the Russian Academy of Sciences has been working in Mali since 2015. Since 2017, it has been attended by employees of the State Museum of the East. The task of the expedition is to study the transformation of traditional Dogon culture in the context of globalization, as well as to collect ethnographic information (life, customs, features of the traditional social and political structure); to collect oral historical legends; to study the history, existence, and transformation of artistic tradition in the villages of the Dogon Country in modern conditions; collecting items of Ethnography and art to add to the collection of the African collection of the. Peter the Great Museum (Kunstkamera, Saint Petersburg) and the State Museum of Oriental Arts (Moscow). The plan of the expedition in January 2020 included additional items, namely, the study of the functioning of the antique market in Mali (the “path” of things from villages to cities, which is important for attributing works of traditional art). The geography of our research was significantly expanded to the regions of Sikasso and Koulikoro in Mali, as well as to the city of Bobo-Dioulasso and its surroundings in Burkina Faso, which is related to the study of migrations to the Bandiagara Highlands. In addition, the plan of the expedition included organization of a photo exhibition in the Museum of the village of Endé and some educational projects. Unfortunately, after the mass murder in March 2019 in the village of Ogossogou-Pel, where more than one hundred and seventy people were killed, events in the Dogon Country began to develop in the worst-case scenario: The incessant provocations after that revived the old feud between the Pel (Fulbe) pastoralists and the Dogon farmers. So far, this hostility and mutual distrust has not yet developed into a full-scale ethnic conflict, but, unfortunately, such a development now seems quite likely.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

scholarly journals A Thermally Conductive Pt/AAO Catalyst for Hydrogen Passive Autocatalytic Recombination

Catalysts ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 491
Author(s):  
Alina E. Kozhukhova ◽  
Stephanus P. du Preez ◽  
Aleksander A. Malakhov ◽  
Dmitri G. Bessarabov
Keyword(s):  
Electron Microscopy ◽  
Particle Size ◽  
Combustion Temperature ◽  
Volume Fraction ◽  
Morphological Characteristics ◽  
Al Alloy ◽  
Impregnation Method ◽  
Case Scenario ◽  
Worst Case ◽  
Thermal Distribution

In this study, a Pt/anodized aluminum oxide (AAO) catalyst was prepared by the anodization of an Al alloy (Al6082, 97.5% Al), followed by the incorporation of Pt via an incipient wet impregnation method. Then, the Pt/AAO catalyst was evaluated for autocatalytic hydrogen recombination. The Pt/AAO catalyst’s morphological characteristics were determined by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The average Pt particle size was determined to be 3.0 ± 0.6 nm. This Pt/AAO catalyst was tested for the combustion of lean hydrogen (0.5–4 vol% H2 in the air) in a recombiner section testing station. The thermal distribution throughout the catalytic surface was investigated at 3 vol% hydrogen (H2) using an infrared camera. The Al/AAO system had a high thermal conductivity, which prevents the formation of hotspots (areas where localized surface temperature is higher than an average temperature across the entire catalyst surface). In turn, the Pt stability was enhanced during catalytic hydrogen combustion (CHC). A temperature gradient over 70 mm of the Pt/AAO catalyst was 23 °C and 42 °C for catalysts with uniform and nonuniform (worst-case scenario) Pt distributions. The commercial computational fluid dynamics (CFD) code STAR-CCM+ was used to compare the experimentally observed and numerically simulated thermal distribution of the Pt/AAO catalyst. The effect of the initial H2 volume fraction on the combustion temperature and conversion of H2 was investigated. The activation energy for CHC on the Pt/AAO catalyst was 19.2 kJ/mol. Prolonged CHC was performed to assess the durability (reactive metal stability and catalytic activity) of the Pt/AAO catalyst. A stable combustion temperature of 162.8 ± 8.0 °C was maintained over 530 h of CHC. To confirm that Pt aggregation was avoided, the Pt particle size and distribution were determined by TEM before and after prolonged CHC.

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