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11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Jalali, Hasan Damavandi, Hossein Kalhor, Ahmad Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran Sadjad University Faculty of Computer Engineering and Information Technology Mashhad Iran University of Tehran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for informing controller design. Two identification methods, namely NN-ARX and ARMAX, have been employed to extract the dynamic characteristics of the robot actuators, resulting in dynamic models of these integral components being derived. These dynamic models have been utilized in simulations for controller design, and due to the disparities in the identification models, distinct controllers have been realized. Subsequently, these controllers have been practically implemented on delta robots, and their performances have been subjected to a comprehensive comparative analysis. The results demonstrate that controllers integrating the identified actuator models outperformed those designed without the incorporation of the identification models. In practice, the implementation of controllers based on NN-ARX yielded the most favorable results among all the tested controllers. This research not only underscores the importance of accurate actuator models in control system design but also highlights the superior performance of neural network-based controllers in real-world robotic applications. In particular, the practical results of NN-ARX-based controllers were able to achieve significantly lower RMSE of 2.3562, 1.9531, and 2.1185 for the three motors, respectively, as opposed to the 4.1369, 3.0125, and 3.0363 achieved by the ARMAX-based controllers. © 2023 IEEE.

关键词： controllers

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Self-adaptive memetic algorithm: an adaptive conjugate gradient approach

Self-adaptive memetic algorithm: an adaptive conjugate gradi...

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IEEE Conference on Cybernetics and intelligent systems

作者： N. Shahidi H. Esmaeilzadeh M. Abdollahi E. Ebrahimi C. Lucas ECE Department University of Tehran Tehran Iran Center of Excellence for Control and Intelligent Processing ECE DepartmentSchool of Intelligent Systems University of Tehran Tehran Iran

Combining hill climbing methods that search for the optimum points in a bounded region of search space with genetic algorithm is effective in the cases that the search space or optimization problem is complicated. However, parameter setting of selected HC can influence the performance of the algorithm significantly. In this paper a run-time self-adaptation strategy is utilized to discover the most appropriate HC parameters for the problem at hand. The HC method is used in this work is conjugate gradient that is an efficient gradient based hill climber for a wide range of problems. Traditionally, key parameters of the conjugate gradient are tuned by some deterministic or predetermined adaptive rules. But in our self-adaptation approach these parameters are encoded in genotypes and coevolved alongside the solutions and adjusted based on regional or generational conditions of individuals in the evolution process. Another advantage of this individualistic approach is that it puts forth different hill climbing capabilities to each individual and this prevents undesirable convergence of solutions to a local optimum that is a side effect of ordinary memetic algorithm. This proposed method not only adds no extra computation load to the genetic algorithm but also eliminates computation burden of parameter adjustment of hill climbing operator. Results of applying this approach on several test functions are demonstrated to illustrate improvements achieved using our self adaptive memetic algorithm in comparison with ordinary memetic algorithm.

关键词： Genetic algorithms Search methods Optimization methods Runtime Automatic testing Evolutionary computation Process control control systems intelligent control Hybrid intelligent systems

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Adaptive Generalized Projective Synchronization of a New Fractional Chaotic System with uncertain parameters

Adaptive Generalized Projective Synchronization of a New Fra...

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第30届中国控制与决策会议

作者： Jiaoru Huang Kun Zhong Fucai Qian The International Joint Research Center of Autonomous Systems and Intelligent Control Xi'an Technological University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

This paper investigates the generalized projective synchronization scheme of a new fractional chaotic system, which can display a four-wing chaotic attractor when the order of system is in the range of 0.82 and ***, considering the situation with unknown parameters, an adaptive nonlinear controller is obtained for synchronization and parameters identification based on fractional-order Lyapunov stability theorem. The fractional-order adaptation law, which can update parameters estimation errors online, is derived to ensure the stability of synchronization error system. Numerical simulations are used to verify the results.

关键词： Four-wing chaotic attractors uncertain parameter generalized projective synchronization parameter identification

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An extension of weighted strategy sharing in cooperative Q-learning for specialized agents

An extension of weighted strategy sharing in cooperative Q-l...

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International Conference on Neural Information processing

作者： S.M. Eshgh M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rides for unseen situations. These benefits will be gained if the learning agents know the area of expertise and the expertness values of each other. In this paper, some Q-learning agents with different skills and expertness levels cooperate in learning. The agents use some criteria to judge others information and knowledge. Four expertness criterion, certainty and entropy measures are used to assign degrees of importance to others' Q-Tables. Effects of measuring these values based on their whole Q-Table, a portion of Q-Tables that reflects their proficiencies, and the states in Q-Tables on the learning quality are studied. Simple strategy sharing and two different weighted strategy-sharing methods are used to combine the acquired knowledge from different agents.

关键词： intelligent robots intelligent agent Process control control systems intelligent control Laboratories intelligent systems Physics Mathematics Entropy

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Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers

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National Science Review 2021年第4期8卷 78-90页

作者： Xiao Zhang Dongrui Wu Lieyun Ding Hanbin Luo Chin-Teng Lin Tzyy-Ping Jung Ricardo Chavarriaga Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Civil Engineering and Mechanics Huazhong University of Science and Technology Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Swartz Center for Computational Neuroscience Institute for Neural ComputationUniversity of California San Diego Center for Advanced Neurological Engineering Institute of Engineering in Medicine University of California San Diego ZHAW Data Lab Zürich University of Applied Sciences

An electroencephalogram(EEG)-based brain–computer interface(BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a *** studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations,which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

关键词： electroencephalogram brain-computer interfaces BCI spellers adversarial examples

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Passivity properties for regulation of DC networks with stochastic load demand ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13113-13118页

作者： Amirreza Silani Michele Cucuzzella Jacquelien M.A. Scherpen Mohammad Javad Yazdanpanah Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 9747 AG Groningen the Netherlands. Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper we present new (stochastic) passivity properties for Direct Current (DC) power networks, where the unknown and unpredictable load demand is modelled by a stochastic process. More precisely, the considered power network consists of distributed generation units supplying ZIP loads, i.e., nonlinear loads comprised of impedance (Z), current (I) and power (P) components. Differently from the majority of the results in the literature, where each of these components is assumed to be constant, we consider time-varying loads whose dynamics are described by a class of stochastic differential equations. Finally, we prove that an existing distributed control scheme achieving current sharing and (average) voltage regulation ensures the asymptotic stochastic stability of the controlled network.

关键词： DC power networks Nonlinear systems Stochastic modelling Passivity theory

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Automatic segmentation of brain tumors in magnetic resonance images

Automatic segmentation of brain tumors in magnetic resonance...

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IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health Engineering

作者： Behzadfar, Neda Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Pooyandegan Rah Saadat Company Tehran 16756-965 Iran Image Analysis Laboratory Radiology Department HenryFord Healh System Detroit MI 48202 United States

ISBN: (纸本)9781457721779

Segmentation of tumors in magnetic resonance images (MRI) is an important task but is quite time consuming when performed manually by experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue in different patients, and in many cases, similarity between tumor and normal tissues. This paper presents an automatic method for segmentation of brain tumors in MRI. We use images of patients with glioblastoma multiform tumors. After pre-processing and removal of the regions that do not have useful information (e.g., eyes and scalp), we create a projection image for determining the primary location of the tumor. This image provides an overall view of the tumor. Then, we grow the primary region to segment the entire tumor. This method is automatic and independent of the operator. It segments low contrast tumors without requiring their exacta tissue boundaries. The segmentation results obtained by the proposed approach are compared with those of an expert radiologist showing excellent correlations among them (R2=0.97). © 2012 IEEE.

关键词： Tumors

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Certainty and expertness-based credit assignment for cooperative Q-Learning agents with an AND-type task

Certainty and expertness-based credit assignment for coopera...

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International Conference on Neural Information processing

作者： A. Harati M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence & AI and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

In multiagent reinforcement learning, inter-agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive. This problem is more critical in groups of independent learners with a joint task. In this research, it is assumed that a critic agent receives the environment feedback and assigns a proper credit to each agent using some measures. Three of such measures for a team of cooperative agents with a parallel and AND-type task are introduced. These measures somehow compare the agents' knowledge. One of these criteria, called normal expertness, is a non-relative measure while two other ones (certainty and relative normal expertness) are relative measure. It is experimentally shown that relative measures work better as they contain more information for the critic agent.

关键词： Feedback intelligent agent intelligent robots Process control control systems intelligent control Artificial intelligence Robot kinematics Physics computing Reliability engineering

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Design of a nonlinear adaptive natural oscillator: Towards natural dynamics exploitation in cyclic tasks

Design of a nonlinear adaptive natural oscillator: Towards n...

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IEEE/RSJ International Conference on intelligent Robots and systems

作者： Rezvan Nasiri Mahdi Khoramshahi Majid Nili Ahmadabadi Cognitive Robotics Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Iran

ISBN: (纸本)9781509037636

In this paper, we present the dynamical equations of a nonlinear adaptive natural oscillator (NANO) in order to exploit the natural dynamics in robotic systems. The presented oscillator tries to minimize an energy-based cost function by adapting the shape and frequency of the reference trajectory. Stability, convergence, and optimality of this oscillator are guaranteed analytically. Moreover, the performance of this oscillator is investigated by applying it to three different types of robotic models;i.e., the pendulum, the adaptive-toy, and the hopper-leg.

关键词： Trajectory Learning Energy Efficiency Cyclic Tasks Natural Dynamics

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Heterogeneous and Hierarchical Cooperative Learning via Combining Decision Trees

Heterogeneous and Hierarchical Cooperative Learning via Comb...

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2006 IEEE/RSJ International Conference on intelligent Robots and systems

作者： Masoud Asadpour Majid Nili Ahmadabadi Roland Siegwart Autonomous Systems Laboratory Ecole Polytechnique Federale de Lausanne Switzerland Control and Intelligent Processing Center of Excellence ECE Department University of Tehran Iran Autonomous Systems Laboratory Swiss Federal Institute of Technology Zurich Switzerland

Decision trees, being human readable and hierarchically structured, provide a suitable mean to derive state-space abstraction and simplify the inclusion of the available knowledge for a reinforcement learning (RL) agent. In this paper, we address two approaches to combine and purify the available knowledge in the abstraction trees, stored among different RL agents in a multi-agent system, or among the decision trees learned by the same agent using different methods. Simulation results in nondeterministic football learning task provide strong evidences for enhancement in convergence rate and policy performance

关键词： Decision trees Multiagent systems Learning systems intelligent robots Humans Process control control systems intelligent agent intelligent control intelligent structures

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