{"title":"Convex optimization of wafer temperature trajectories for rapid thermal processing","authors":"P. Gyugyi, Y. Cho, G. Franklin, T. Kailath","doi":"10.1109/CCA.1993.348257","DOIUrl":"https://doi.org/10.1109/CCA.1993.348257","url":null,"abstract":"In this paper, we expand on the framework for achieving the tight control of the wafer temperature essential in rapid thermal processing (RTP) of semiconductor wafers. In our previous paper (1992), we established a method for identifying a state-space model of an RTP system at a processing condition of interest and designing a linear quadratic Gaussian (LQG) controller for disturbance regulation. In this paper we describe how convex optimization is used to obtain an approximation to the desired trajectory, close enough to allow high gain feedback controllers to reduce temperature nonuniformity. Temperature errors less than 30/spl deg/C peak-to-peak, limited almost entirely by our system geometry, were achieved throughout a typical wafer recipe, which included ramps from room temperature to 900/spl deg/C and from 900/spl deg/C to 600/spl deg/C, at the rate of 40/spl deg/C per second. The benefits of convex optimization together with the LQG feedback control are demonstrated by experimental results obtained from an RTP system.<<ETX>>","PeriodicalId":276779,"journal":{"name":"Proceedings of IEEE International Conference on Control and Applications","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114811612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Chandrasekhar recursion for structured time-varying systems and its application to recursive least squares problems","authors":"P. Park, Y. Cho, T. Kailath","doi":"10.1109/CCA.1993.348233","DOIUrl":"https://doi.org/10.1109/CCA.1993.348233","url":null,"abstract":"Chandrasekhar recursion of Kalman filtering for a time-varying system has not been fully studied while its counterpart for a time-invariant system has been around for decades. Sayed and Kailath (1992) have shown that Chandrasekhar recursion for a certain class of structured time-varying systems can be achieved. In this paper, the authors extend the traditional discrete-time Chandrasekhar recursion of Kalman filtering to derive an algorithm applicable to an even wider class of structured time-varying systems including those with so-called quasi-internally-invariant property. This extension makes it possible to update the Kalman filter of time-varying systems with a quasi-internally-invariant property, only with O(n(p+q)) flops instead of O(n/sup 3/), where n, p and q are the number of states, the number of outputs and the displacement rank of Riccati solutions, respectively. It is also shown that the resulting algorithm can be applied to adaptive filtering (specifically, recursive least squares problems).<<ETX>>","PeriodicalId":276779,"journal":{"name":"Proceedings of IEEE International Conference on Control and Applications","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123920365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Adaptive tracking in nonlinear systems using neural networks","authors":"D. Rao, M. Gupta, H. Wood","doi":"10.1109/CCA.1993.348214","DOIUrl":"https://doi.org/10.1109/CCA.1993.348214","url":null,"abstract":"Neural networks potentially offer a general framework for modeling and control of nonlinear systems. The conventional neural network models are a parody of biological neural structures, and have the disadvantage of very slow learning. In this paper, we develop a dynamic neural network structure which is based upon the collective computation of subpopulation of neurons, thus different from the conventionally assumed structure of neural networks. The architecture and the learning algorithm to modify weights of the proposed neural model are elucidated. Three applications of this dynamic neural network, namely (i) functional approximation, (ii) control of unknown nonlinear dynamic systems, and (iii) coordination and control of multiple systems, are described through computer simulations.<<ETX>>","PeriodicalId":276779,"journal":{"name":"Proceedings of IEEE International Conference on Control and Applications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121872299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multivariable control of a linear system using feed-forward neural networks","authors":"A. Bulsari","doi":"10.1109/CCA.1993.348271","DOIUrl":"https://doi.org/10.1109/CCA.1993.348271","url":null,"abstract":"Artificial neural networks have been applied to several control problems. However, most of those are single input, single output systems. A multivariable control of a linear process is considered in this paper. The advantage of using neural networks lie in their ability to learn the process dynamics from the observations of the gross behaviour of the process, without a mathematical model. The linear process was controlled well using neural networks. The performance does not improve by using past values of the state variables.<<ETX>>","PeriodicalId":276779,"journal":{"name":"Proceedings of IEEE International Conference on Control and Applications","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114944155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Carozzi, A. Fioretti, M. Poloni, F. Nicolò, G. Ulivi
{"title":"Implementation of a tracking learning controller for an industrial manipulator","authors":"A. Carozzi, A. Fioretti, M. Poloni, F. Nicolò, G. Ulivi","doi":"10.1109/CCA.1993.348350","DOIUrl":"https://doi.org/10.1109/CCA.1993.348350","url":null,"abstract":"The paper describes the design and the implementation, of a learning controller for a series industrial manipulator SMART 6.12 from COMAU S.p.a. The controller iteratively improves the system performance along a repetitive trajectory by building the required off-line input processing the recorded error. It has been realized with minor additions to the existing controller, which remains fully operational. Experimental results demonstrate its effectiveness.<<ETX>>","PeriodicalId":276779,"journal":{"name":"Proceedings of IEEE International Conference on Control and Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124773864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Flight test development and evaluation of a Kalman filter state estimator for low-altitude flight","authors":"R.E. Zelenka, Z. Yee, A. Zirkler","doi":"10.1109/CCA.1993.348235","DOIUrl":"https://doi.org/10.1109/CCA.1993.348235","url":null,"abstract":"Flight operations dependent on digitized terrain elevation data for navigational reference or trajectory generation are constrained in minimum flight altitude, due to airborne navigation errors and inaccuracies of the reference terrain elevation data. This limitation is not restrictive in traditional medium-altitude implementations of such databases, such as in unmanned aerial vehicles, missiles, or high-performance, high-speed aircraft. In low-altitude, lower speed terrain hugging helicopter missions, however, such constraints on minimum flight altitudes greatly reduce the effectiveness of their missions and diminish the benefits of employing terrain elevation maps. A Kalman filter state estimator has been developed which blends airborne navigation, stored terrain elevation data, and a radar altimeter in estimating above-ground-level (AGL) altitude. This AGL state estimator was integrated in a near-terrain guidance system aboard a research helicopter and flight tested in moderately rugged terrain over a variety of flight and system conditions. The minimum operating altitude of the terrain database referenced guidance system was reduced from 300 ft to 150 ft with the addition of this Kalman filter state estimator.<<ETX>>","PeriodicalId":276779,"journal":{"name":"Proceedings of IEEE International Conference on Control and Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127867650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hybrid velocity/force control of manipulators using PID controller and feedforward compensation","authors":"Pei-Chieh Chin, K. Waldron","doi":"10.1109/CCA.1993.348258","DOIUrl":"https://doi.org/10.1109/CCA.1993.348258","url":null,"abstract":"An effort has been made to use PID control technique and feedforward compensation to control robotic manipulators under a combined task of velocity and force. This approach is based on the assumption that the task configuration is homogeneous. However, the proposed scheme is still applicable for a nonhomogeneous task configuration with frictional forces being the only nonhomogeneous constraints. The controller proposed is applied to a planar 3R manipulator to demonstrate system responses and simulation results.<<ETX>>","PeriodicalId":276779,"journal":{"name":"Proceedings of IEEE International Conference on Control and Applications","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124469594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Skill acquisition and transfer system as approach to the intelligent assisting system-IAS","authors":"M. Buss, H. Hashimoto","doi":"10.1109/CCA.1993.348249","DOIUrl":"https://doi.org/10.1109/CCA.1993.348249","url":null,"abstract":"In this paper we propose a new forthcoming research topic, the Intelligent Assisting System-IAS. Using this system we are approaching identification and analysis of human manipulation skill to be used for intelligent human operator assistance. A manipulation skill database enables the IAS to perform complex manipulations on the motion control level. Through repeated interaction with the operator for unknown environment states, manipulation skill in the database can be increased online. A model for manipulation skill based on the grip transformation matrix is proposed, which describes the transformation between the object trajectory and contact conditions. We describe the experimental system setup of a skill acquisition and transfer system as a first approach to the IAS and some results confirming the calibration method of the developed sensor glove using an artificial neural network. A simple manipulation example shows the feasibility of the proposed manipulation skill model. Further this paper derives a control algorithm realizing object task trajectories and its efficiency is shown by simulation.<<ETX>>","PeriodicalId":276779,"journal":{"name":"Proceedings of IEEE International Conference on Control and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121662318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Neural network control systems incorporating parallel adaptive enhancements","authors":"Tong Heng Lee, W. Tan, Marcelo H. Ang","doi":"10.1109/CCA.1993.348270","DOIUrl":"https://doi.org/10.1109/CCA.1993.348270","url":null,"abstract":"Neural networks can provide a suitable approach to control of certain classes of nonlinear dynamical systems, and adaptive techniques for neural network control can be incorporated. In this paper, we present neural network control strategies which incorporate an additional parallel neural network to provide adaptive enhancements to the basic fixed neural network-based controllers. These proposed adaptive neural network control systems are applicable to nonlinear dynamical systems of the type commonly encountered in many practical position control servomechanisms. The effectiveness of these controllers are demonstrated in real-time implementation experiments for position control in a servomechanism with asymmetrical loading and changes in the load.<<ETX>>","PeriodicalId":276779,"journal":{"name":"Proceedings of IEEE International Conference on Control and Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122971887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Adaptive fuzzy control with model reference-based fuzzy adaptation mechanism","authors":"Z. Kovačić","doi":"10.1109/CCA.1993.348283","DOIUrl":"https://doi.org/10.1109/CCA.1993.348283","url":null,"abstract":"The paper presents a new model reference adaptive control scheme containing a fuzzy algorithm for tuning the gain coefficient which adjusts the level of the fuzzy controller output. The synthesis of a fuzzy tuning algorithm has been performed for high-order processes of well-known structures. The proposed adaptive control scheme has been applied to the angular speed control of a vector controlled permanent magnet synchronous motor (PMSM) drive. The computer simulation results have proved the efficiency of the proposed method, showing stable system responses almost insensitive to large parameter variations.<<ETX>>","PeriodicalId":276779,"journal":{"name":"Proceedings of IEEE International Conference on Control and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126692868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}