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On an Improved State Parametrization for Soft Robots With Piecewise Constant Curvature and Its Use in Model Based control

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ieee ROBOTICS AND automation LETTERS 2020年第2期5卷 1001-1008页

作者： Della Santina, Cosimo Bicchi, Antonio Rus, Daniela MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Univ Pisa Res Ctr E Piaggio I-56122 Pisa Italy Univ Pisa Dept Informat Engn I-56122 Pisa Italy Ist Italiano Tecnol Soft Robot Human Cooperat & Rehabil Lab I-16163 Genoa Italy

Piecewise constant curvature models have proven to be an useful tool for describing kinematics and dynamics of soft robots. However, in their three dimensional formulation they suffer from many issues limiting their range of applicability - as discontinuities and singularities - mainly concerning the straight configuration of the robot. In this work we analyze these flaws, and we show that they are not due to the piecewise constant curvature assumption itself, but that instead they are a byproduct of the commonly employed direction/angle of bending parametrization of the state. We therefore consider an alternative state representation which solves all the discussed issues, and we derive a model based controller based on it. Examples in simulation are provided to support and describe the theoretical results. When using the novel parametrization, the system is able to perform more complex tasks, with a strongly reduced computational burden, and without incurring in spikes and discontinuous behaviors.

关键词： Modeling control and learning for soft robots motion control natural machine motion

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Demand Characterization of CPS with Conditionally-Enabled Sensors 27

Demand Characterization of CPS with Conditionally-Enabled Se...

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27th ieee International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

作者： Willcock, Aaron Fisher, Nathan Chantem, Thidapat Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Virginia Tech Dept Elect & Comp Engn Arlington VA USA

ISBN: (纸本)9781665441889

Characterizing computational demand of Cyber-Physical Systems (CPS) is critical for guaranteeing that multiple hard real-time tasks may be scheduled on shared resources without missing deadlines. In a CPS involving repetition such as industrial automation systems found in chemical process control or robotic manufacturing, sensors and actuators used as part of the industrial process may be conditionally enabled (and disabled) as a sequence of repeated steps is executed. In robotic manufacturing, for example, these steps may be the movement of a robotic arm through some trajectories followed by activation of end-effector sensors and actuators at the end of each completed motion. The conditional enabling of sensors and actuators produces a sequence of Monotonically Ascending Execution times (MAE) with lower WCET when the sensors are disabled and higher WCET when enabled. Since these systems may have several predefined steps to follow before repeating the entire sequence each unique step may result in several consecutive sequences of MAE. The repetition of these unique sequences of MAE result in a repeating WCET sequence. In the absence of an efficient demand characterization technique for repeating WCET sequences composed of subsequences with monotonically increasing execution time, this work proposes a new task model to describe the behavior of real-world systems which generate large repeating WCET sequences with subsequences of monotonically increasing execution times. In comparison to the most applicable current model, the Generalized Multiframe model (GMF), an empirically and theoretically faster method for characterizing the demand is provided. The demand characterization algorithm is evaluated through a case study of a robotic arm and simulation of 10,000 randomly generated tasks where, on average, the proposed approach is 231 and 179 times faster than the state-of-the-art in the case study and simulation respectively.

关键词： real-time systems control systems cyberphysical systems

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Enhancing SHADE and L-SHADE Algorithms Using Ordered Mutation

Enhancing SHADE and L-SHADE Algorithms Using Ordered Mutatio...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Mousavirad, Seyed Jalaleddin Rahnamayan, Shahryar Sabzevar Univ New Technol Fac Engn Sabzevar Iran Ontario Tech Univ Dept Elect Comp & Software Engn Nat Inspired Computat Intelligence NICI Lab Oshawa ON Canada

ISBN: (纸本)9781728125473

Differential Evolution (DE) algorithm is an efficient population-based metaheuristic algorithm which has shown satisfactory performance in solving complex real-world optimization problems. A Success-History Based Parameter Adaptation for Differential Evolution (SHADE) is a well-established variant of DE algorithm which employs a historical performance of the successful control parameters. L-SHADE algorithm extends SHADE with a linear population size reduction strategy. The SHADE and L-SHADE algorithms employ current-to-pbest/1 strategy for evolution, whilst the order of candidate solutions is not considered in their schemes. This paper proposes an ordering strategy for SHADE and L-SHADE algorithms which has shown a satisfactory influence on the performance of both algorithms. In the first direction, we propose current-to-3order/1 strategy for SHADE algorithm, which is based on ordering three candidate solutions. In the second direction, L-SHADE algorithm is improved based on ordering two candidate solutions. called eurrent-to-pbest-2order/1. The proposed strategies can improve the performance of SHADE and L-SHADE algorithms without adding any extra significant computational cost. The proposed strategy is evaluated on CEC-2017 benchmark functions and with dimensions 30, 50, and 100. Our experimental results clearly verify the effectiveness of the proposed strategies.

关键词： Differential Evolution SHADE L-SHADE Ordering strategy

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An Approach to Advanced Distribution automation in the Context of Smart Grid

An Approach to Advanced Distribution Automation in the Conte...

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ieee PES Transmission and Distribution Conference and Exhibition - Latin America (T and D LA)

作者： Sakai, Raphael Toshio Meschini Almeida, Carlos Frederico Leite Rosa, Luiz Henrique Kagan, Nelson Pereira, Danilo S. Medeiros, Thiago S. Kagan, Henrique da Cruz, Marcio Araujo Junior, Jose Airton da Silva, Gustavo T. A. Brito, Jose A. S. NAPREI Enerq USP Sao Paulo Brazil Grp Neoenergia Salvador BA Brazil

ISBN: (纸本)9781728141558

Recent technological advances, with advanced measurement and automation infrastructures, and sophisticated computational intelligence mechanisms, create opportunities, among others, to improve the operational efficiency of electrical systems and power quality indicators within the context of smart grids (SG). Distribution companies have corporate supervisory, control, monitoring, georeferencing and management systems and have invested in automation and modernization projects of their electric grids for this purpose. Thus, this paper presents an approach for the development of a DMS - Distribution Management System, for Advanced Distribution automation (ADA) applications, which includes state estimator, Volt/VAr control, fault location, isolation, and self-restoration tools, so that an interoperability bus (BCIM) integrates the corporate systems with the operational management. The developed applications were tested for real networks, in a computational environment, and integrated with the environment of the NAPREI / USP Smart Grids laboratory. The results demonstrate the processes of integrating ADA applications into a commercial service bus, which communicates the company's legacy systems, of testing their applications, and its challenges.

关键词： Advanced Distribution automation Estimated State Volt/VAr control FLISR (Fault Location, Isolation, and Self-Healing) Power Quality

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Application of SPCA-LSSVM model in soft measurement of Gasoline dry point 13

Application of SPCA-LSSVM model in soft measurement of Gasol...

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13th International symposium on computational intelligence and Design (ISCID)

作者： Guo, Liying Zhang, Yu Liaoning Shihua Univ Sch Informat & Control Engn Fushun Peoples R China

ISBN: (纸本)9781728184463

A soft measurement model of least squares support vector machine based on data preprocessing for sparse principal component analysis(SPCA-LSSVM) is proposed to solve the problem that the gasoline dry point on the top of atmospheric pressure tower is difficult to measure online. This method combined sparse principal component analysis (SPCA) with the least squares support vector machine (LSSVM) method, principal component analysis (PCA) is transformed to solve the regression optimization problem with quadratic penalty by introducing the sparse constraint aiming at variable selection, in this way, the dimensions of the sparse eigenvector reduces dependency and becomes more independent, it also reduces the influence of measurement noise. The data sample processed by SPCA is used as the input of LSSVM model to establish the soft measurement model of dry point at atmospheric pressure tower top, the problem of lack of sparsity in LSSVM is solved. The simulation results showes that the SPCA-LSSVM model has higher prediction accuracy than the traditional LSSVM model, and the PCA-LSSVM model, reducing the complexity of the model, showing the superiority of the soft-sensing model.

关键词： Soft sensor PCA SPCA LSSVM model Dry point of gasoline formattin

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An Intelligent and Secure control Approach for Nonlinear Systems under Attacks

An Intelligent and Secure Control Approach for Nonlinear Sys...

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ieee symposium Series on computational intelligence (SSCI)

作者： Xiangnan Zhong Zhen Ni Florida Atlantic University Boca Raton FL USA

ISBN: (纸本)9781728190495

This paper designs an intelligent and secure control approach based on adaptive dynamic programming for a class of nonlinear systems under the actuator attacks. The designed method can monitor and identify the attacks by an established state estimator based detector. When an attack is triggered, the control process automatically switches to a game-theoretical architecture for attack mitigation. The intelligent learning process is developed for both nominal and attack conditions without the requirement of system dynamics and attack information. Neural network techniques are applied to implement the proposed method with two critic networks and the control signals are calculated accordingly. Therefore, the designed intelligent control method can reduce the computation complexity. Simulation studies and results demonstrate the necessary of attack detection and mitigation during the learning process, and also verify the effectiveness of the developed method.

关键词： System dynamics Neural networks Process control Detectors Computer architecture Stability analysis Nonlinear systems

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Robust Process Identification from Step Response Data and Parallel Implementation 3

Robust Process Identification from Step Response Data and Pa...

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3rd International Conference on Industrial Artificial intelligence, IAI 2021

作者： Han, Yucheng Liu, Qingyuan Shang, Chao Huang, Dexian Tsinghua University Department of Automation Beijing100084 China

ISBN: (纸本)9781665435178

Fast, robust and accurate system identification is of importance to the process industry, and identification from step response is a prevalent approach. Recently, a new method based on rank constraint using low-quality industrial data has been proposed. However, with mean square error (MSE) used as the loss function, this identification method is sensitive to outliers, which may occasionally lead to invalid models. In this paper, we propose an improved robust process identification approach from step response data based on the Huber loss, which is less sensitive to outliers than generic MSE, and leads to a higher successful rate. A tailored solution algorithm based on alternating direction method of multipliers is developed, which, however, requires heavy computational cost especially when there are massive control loops to be identified simultaneously. To address this issue, we leverage recent advances in parallel computing. We show that this solution procedure can be parallelized, which leads to significant computation savings with graphical processing units used, and thus better conforms to requirement in practical situations. Numerical studies demonstrate that our proposed method is more robust against outliers, and the parallel implementation gives a faster speed in the presence of massive data. © 2021 ieee.

关键词： Mean square error

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Adaptive Optimal control of Continuous-Time Linear Systems via Hybrid Iteration

Adaptive Optimal Control of Continuous-Time Linear Systems v...

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ieee symposium Series on computational intelligence (SSCI)

作者： Omar Qasem Weinan Gao Tao Bian Mechanical and Civil Engineering Florida Institute of Technology Melbourne FL USA WorldQuant LLC* Old Greenwich CT USA

ISBN: (纸本)9781728190495

In this paper, we propose a novel dynamic programming (DP) algorithm, under the name of hybrid iteration (HI), for continuous-time linear systems. The proposed HI approach combines the advantages of two well-known DP algorithms, i.e., policy iteration (PI) and value iteration (VI). In particular, HI drops the need of an initial stabilizing control policy required in PI, and at the same time it maintains a faster convergence rate compared with VI. Based on the proposed HI algorithm, a data-driven adaptive optimal controller design is also proposed. Simulation results for randomly generated continuous-time linear systems with different system orders demonstrate that the proposed HI approach can save CPU time up to 73% and reduce the number of iterations to converge up to 98% comparing with the VI approach.

关键词： Linear systems Heuristic algorithms Simulation Optimal control Dynamic programming computational intelligence Convergence

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Evolutionary Algorithm Driven Explainable Adversarial Artificial intelligence

Evolutionary Algorithm Driven Explainable Adversarial Artifi...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Veal, Charlie Lindsay, Marshall Kovaleski, Scott D. Anderson, Derek T. Price, Stanton R. Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA US Army Engineer Res & Dev Ctr Vicksburg MS 39180 USA

ISBN: (纸本)9781728125473

It is well-known that machine learning algorithms can be susceptible to undesirable effects when exposed to conditions that are not expressed adequately in the training dataset. This leads to a growing interest throughout many communities;where do algorithms and trained models break? Recently, methods such as generative adversarial neural networks and variational autoencoders were proposed to create adversarial examples that challenge algorithms. This results in artificial intelligence having higher false detections or completely losing recognition. The problem is that existing solutions, are for the most part, black boxes. Current gaps include how do we better control and understand adversarial algorithms. Herein, we propose the concept of an adversarial modifier set as an understandable and controlled way to generate adversarial examples. This is achieved by exploiting the improved evolution-constructed algorithm to identify ideal features that a victim algorithm values in imagery. These features are combined to realize a tuple library that preserves spatial relations. Last, a set of algorithmically controlled modifiers that generate the imagery are found by examining the content of the false imagery. Preliminary results are encouraging and demonstrate that this approach has benefits in both generating explainable adversarial examples, as well as shedding some insight into victim algorithm decision making.

关键词： adversarial artificial intelligence generative improved evolution-constructed algorithm machine learning

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Model-Based and Data-Driven Contingency Filtering for Real-Time Corrective Security-Constrained Economic Dispatch 5

Model-Based and Data-Driven Contingency Filtering for Real-T...

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5th ieee Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Han, Donghai Yu, Yaowen He, Dingxin Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665434256

Security-constrained economic dispatch (SCED) is one of the most important problems in power system operations. Corrective SCED (CSCED) is a type of SCED that considers corrective capabilities of the power system and adjusts base case decisions according to post-contingency states. Because of the large scale of the CSCED problem, it is difficult for purely model-based approaches to meet the time limits in real-time practical operations. To leverage historical operation data, this paper develops a novel hybrid model-based and data-driven framework to accelerate the solution process of CSCED. In the offline stage, our previous model-based contingency filtering approach is utilized to label active statuses of contingencies against historical net load samples. In the online stage, a multi-label classifier based on the K-Nearest Neighbor (KNN) algorithm quickly generates an active contingency set corresponding to the real-time net load. This active contingency set can be used to calculate the economic dispatch decisions in one shot. In addition, the accuracy of our framework can be further improved by adding an optional contingency filtering procedure at the end. Numerical testing results on the ieee RTS-96 system demonstrate the accuracy and computational efficiency of the hybrid framework as compared to a purely model-based approach. © 2021 ieee

关键词： computational efficiency

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