237 research outputs found
Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence
No abstract availabl
RendszermodellezĂ©s mĂ©rĂ©si adatokbĂłl, hibrid-neurális megközelĂtĂ©s = System modelling from measurement data: hybrid-neural approach
A kutatás cĂ©lja mĂ©rĂ©si adatok alapján törtĂ©nĹ‘ rendszermodellezĂ©si eljárások kidolgozása Ă©s vizsgálata volt, kĂĽlönös tekintettel a nemlineáris rendszerek modellezĂ©sĂ©re. A kutatás során többfĂ©le megközelĂtĂ©st alkalmaztunk: egyrĂ©szt a rendszermodellezĂ©si feladatok megoldásánál a lineáris rendszerekre kidolgozott eljárásokbĂłl indultunk ki nemlineáris hatásokat is figyelembe vĂ©ve, másrĂ©szt fekete doboz megközelĂtĂ©seket alkalmaztunk, ahol elsĹ‘dlegesen input-output adatokbĂłl törtĂ©nik a modell konstrukciĂł. Az elĹ‘bbi megközelĂtĂ©s kĂĽlönösen gyengĂ©n nemlineáris rendszerek modellezĂ©sĂ©nĂ©l tűnik járhatĂł Ăştnak, ahol a gyengĂ©n nemlineáris rendszereket, mint nemlineárisan torzĂtott lineáris rendszereket tekintjĂĽk. A nemlineáris torzĂtások hatásának megĂ©rtĂ©sĂ©re egy teljes elmĂ©letet dolgoztunk ki. A fekete doboz modellezĂ©snĂ©l általános modell-struktĂşrákbĂłl indulunk ki, melyek paramĂ©tereit a rendelkezĂ©sre állĂł mĂ©rĂ©si adatok felhasználásával, tanulással határozhatjuk meg. Ekkor az alapvetĹ‘ kĂ©rdĂ©sek a megfelelĹ‘ kiindulĂł adatbázis kialakĂtására Ă©s az adatokkal kapcsolatos problĂ©mákra (zajos adatok, kiugrĂł adatok, inkonzisztens adatok, redundáns adatok, stb.) irányultak, továbbá arra hogy hogyan lehet a fekete doboz modellstruktĂşra komplexitását kĂ©zben tartani Ă©s az adatokon tĂşl meglĂ©vĹ‘ egyĂ©b informáciĂł hatĂ©kony figyelembevĂ©telĂ©t biztosĂtani. A fekete doboz modellezĂ©snĂ©l neuronhálĂłkat Ă©s szupport vektor gĂ©peket vettĂĽnk figyelembe Ă©s a minĂ©l kisebb modell-komplexitás elĂ©rĂ©sĂ©re törekedtĂĽnk. | The goal of the research was to develop and analyse system modelling procedures, especially for modelling non-linear systems. To reach the goal different approaches were applied. One approach is to use procedures developed for linear system modelling, where nonlinear effects are taken into consideration. The other approach applied is black box modelling, where model-construction is mainly based on input-output data. The first approach proved to be successful especially for the modelling of weakly non-linear systems, where these systems are considered as linear ones with the presence of nonlinear distortion. To understand nonlinear distortions a whole theory has been developed. For black box modelling the starting point was the use of certain general model-structures, where the parameters of these structures are determined by training using measurement data. The most relevant questions in this case are related to the construction of data base, and the problems of quality of the available data (noisy data, missing data, outliers, inconsistent data, redundant data, etc.), A further important goal was to find proper ways to utilise additional knowledge and at the same time to reduce model complexity. For black box modelling some special neural network architectures and support vector machines were considered
Error minimising gradients for improving cerebellar model articulation controller performance
In motion control applications where the desired trajectory velocity exceeds an actuator’s maximum velocity limitations, large position errors will occur between the desired and actual trajectory responses. In these situations standard control approaches cannot predict the output saturation of the actuator and thus the associated error summation cannot be minimised.An adaptive feedforward control solution such as the Cerebellar Model Articulation Controller (CMAC) is able to provide an inherent level of prediction for these situations, moving the system output in the direction of the excessive desired velocity before actuator saturation occurs. However the pre-empting level of a CMAC is not adaptive, and thus the optimal point in time to start moving the system output in the direction of the excessive desired velocity remains unsolved. While the CMAC can adaptively minimise an actuator’s position error, the minimisation of the summation of error over time created by the divergence of the desired and actual trajectory responses requires an additional adaptive level of control.This thesis presents an improved method of training CMACs to minimise the summation of error over time created when the desired trajectory velocity exceeds the actuator’s maximum velocity limitations. This improved method called the Error Minimising Gradient Controller (EMGC) is able to adaptively modify a CMAC’s training signal so that the CMAC will start to move the output of the system in the direction of the excessive desired velocity with an optimised pre-empting level.The EMGC was originally created to minimise the loss of linguistic information conveyed through an actuated series of concatenated hand sign gestures reproducing deafblind sign language. The EMGC concept however is able to be implemented on any system where the error summation associated with excessive desired velocities needs to be minimised, with the EMGC producing an improved output approximation over using a CMAC alone.In this thesis, the EMGC was tested and benchmarked against a feedforward / feedback combined controller using a CMAC and PID controller. The EMGC was tested on an air-muscle actuator for a variety of situations comprising of a position discontinuity in a continuous desired trajectory. Tested situations included various discontinuity magnitudes together with varying approach and departure gradient profiles.Testing demonstrated that the addition of an EMGC can reduce a situation’s error summation magnitude if the base CMAC controller has not already provided a prior enough pre-empting output in the direction of the situation. The addition of an EMGC to a CMAC produces an improved approximation of reproduced motion trajectories, not only minimising position error for a single sampling instance, but also over time for periodic signals
Multilevel SVM and AI based Transformer Fault Diagnosis using the DGA Data
The Dissolved Gas Analysis (DGA) is utilized as a test for the detection of incipient prob-lems in transformers, and condition monitoring of transformers using software-based diagnosis tools has become crucial. This research uses dissolved gas analysis as an intel-ligent fault classification of a transformer. The Multilayer SVM technique is used to de-termine the classification of faults and the name of the gas. The learned classifier in the multilayer SVM is trained with the training samples and can classify the state as normal or fault state, which contains six fault categories. In this paper, polynomial and Gaussi-an functions are utilized to assess the effectiveness of SVM diagnosis. The results demonstrate that the combination ratios and graphical representation technique is more suitable as a gas signature, and that the SVM with the Gaussian function outperforms the other kernel functions in diagnosis accuracy
Stability and weight smoothing in CMAC neural networks
Although the CMAC (Cerebellar Model Articulation Controller) neural network has been successfully used in control systems for many years, its property of local generalization, the availability of trained information for network responses at adjacent untrained locations, although responsible for the networks rapid learning and efficient implementation, results in network responses that is, when trained with sparse or widely spaced training data, spiky in nature even when the underlying function being learned is quite smooth. Since the derivative of such a network response can vary widely, the CMAC\u27s usefulness for solving optimization problems as well as for certain other control system applications can be severely limited. This dissertation presents the CMAC algorithm in sufficient detail to explore its strengths and weaknesses. Its properties of information generalization and storage are discussed and comparisons are made with other neural network algorithms and with other adaptive control algorithms. A synopsis of the development of the fields of neural networks and adaptive control is included to lend historical perspective. A stability analysis of the CMAC algorithm for open-loop function learning is developed. This stability analysis casts the function learning problem as a unique implementation of the model reference structure and develops a Lyapunov function to prove convergence of the CMAC to the target model. A new CMAC learning rule is developed by treating the CMAC as a set of simultaneous equations in a constrained optimization problem and making appropriate choices for the weight penalty matrix in the cost equation. This dissertation then presents a new CMAC learning algorithm which has the property of weight smoothing to improve generalization, function approximation in partially trained networks and the partial derivatives of learned functions. This new learning algorithm is significant in that it derives from an optimum solution and demonstrates a dramatic performance improvement for function learning in the presence of widely spaced training data. Developed from a completely unique analytical direction, this algorithm represents a coupling and extension of single- and multi-resolution CMAC algorithms developed by other researchers. The insights derived from the analysis of the optimum solution and the resulting new learning rules are discussed and suggestions for future work are presented
Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton
A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user’s intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems
Lessons Learned from Field Tests in Croatia and Cambodia
This article describes the development and the experiments performed with Gryphon, a new platform for tele-operated landmine detection. With Gryphon, the authors aim at reducing the gap between research and application by introducing partial autonomy in mine-detection operations with a robust platform. Tests have been performed in Croatia and Cambodia
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