{"title":"Real-time PI controller tuning via unfalsified control","authors":"Tanet Wonghong, S. Engell","doi":"10.1109/ECTICON.2012.6254111","DOIUrl":"https://doi.org/10.1109/ECTICON.2012.6254111","url":null,"abstract":"In this paper, we present a new unfalsified adaptive control algorithm. This algorithm leads to a real-time controller tuning method. The algorithm consists of two main elements: 1) Switching of controllers in a controller set by the e -hysteresis switching algorithm and 2) Optimization of the controller set via an evolutionary algorithm (EA). The real-time controller tuning is demonstrated for a nonminimum-phase continuous stirred tank reactor (CSTR) model.","PeriodicalId":285096,"journal":{"name":"2011 IEEE International Symposium on Intelligent Control","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114295820","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":"Probabilistic fault detection and handling algorithm for testing stability control systems with a drive-by-wire vehicle","authors":"Peter Bergmiller, M. Maurer, Bernd Lichte","doi":"10.1109/ISIC.2011.6045395","DOIUrl":"https://doi.org/10.1109/ISIC.2011.6045395","url":null,"abstract":"This paper presents a probabilistic fault detection and handling algorithm (PFDH) for redundant and deterministic X-by-wire systems. The algorithm is specifically designed to guarantee safe operation of an experimental drive-by-wire vehicle used as test platform and development tool in research projects focusing on vehicle dynamics. The required flexibility of the overall system for use as a test bed influences significantly the redundancy structure of the onboard network. A “black box” approach to integrate newly developed user algorithms is combined with a hot-standby architecture controlled by PFDH. This way, functional redundancy for basic driving operations can be achieved despite unknown software components. PFDH is based on monitoring multiple criteria over time, including vehicle dynamics and relative error probabilities of hard- and software components provided by experts or statistical data.","PeriodicalId":285096,"journal":{"name":"2011 IEEE International Symposium on Intelligent Control","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126964134","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":"Satellite formation flying with input saturation: An LMI approach","authors":"Y. Lim, H. Ahn, D. Chung","doi":"10.1109/ISIC.2011.6045391","DOIUrl":"https://doi.org/10.1109/ISIC.2011.6045391","url":null,"abstract":"In this paper, we consider a relative position control problem for a satellite formation flying system in a noncoplarnar and elliptical orbit. It is assumed that the angular rate and angular acceleration are not known, but they are bounded. The system dynamics is designed with the bounded uncertain parameters. In the presence of input saturation, we develop a state feedback controller that guarantees stability of the system. Linear matrix inequality (LMI) conditions are proposed to design the feedback controller. Finally, numerical simulation is presented to demonstrate the validity of the proposed controller.","PeriodicalId":285096,"journal":{"name":"2011 IEEE International Symposium on Intelligent Control","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124605230","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":"A cell decomposition approach to online evasive path planning and the video game Ms. Pac-Man","authors":"Greg Foderaro, V. Raju, S. Ferrari","doi":"10.1109/ISIC.2011.6045414","DOIUrl":"https://doi.org/10.1109/ISIC.2011.6045414","url":null,"abstract":"This paper presents an approach for optimizing paths online for a pursuit-evasion problem where an agent must visit several target positions within a region of interest while simultaneously avoiding one or more actively-pursuing adversaries. This is relevant to applications such as robotic path planning, mobile-sensor applications, and path exposure. The methodology described utilizes cell decomposition to construct a modified decision tree to achieve the objective of minimizing the risk of being caught by an adversary and maximizing a reward associated with visiting the target locations. By computing paths online, the algorithm can quickly adapt to unexpected movements by the adversaries or dynamic environments. The approach is illustrated through a modified version of the video game Ms. Pac-Man which is shown to be a benchmark example of the pursuit-evasion problem. The results show that the approach presented in this paper runs in real-time and outperforms several other methods as well as most human players.","PeriodicalId":285096,"journal":{"name":"2011 IEEE International Symposium on Intelligent Control","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132234056","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":"An interpolation method of multiple terminal iterative learning control","authors":"Tong Duy Son, D. H. Nguyen, H. Ahn","doi":"10.1109/ISIC.2011.6045393","DOIUrl":"https://doi.org/10.1109/ISIC.2011.6045393","url":null,"abstract":"In this paper, we present an iterative learning control (ILC) algorithm to track specified desired multiple terminal points at given time instants. A framework to update the desired trajectories from given points is developed based on the interpolation technique. The approach shows better rate of convergence of the errors. The simulation with a satellite antenna control model is demonstrated to show the effectiveness of our approach.","PeriodicalId":285096,"journal":{"name":"2011 IEEE International Symposium on Intelligent Control","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129994231","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":"Model-free H∞ stochastic optimal design for unknown linear networked control system zero-sum games via Q-learning","authors":"Hao Xu, S. Jagannathan","doi":"10.1109/ISIC.2011.6045415","DOIUrl":"https://doi.org/10.1109/ISIC.2011.6045415","url":null,"abstract":"In this paper, stochastic optimal strategy for unknown linear networked control system (NCS) quadratic zero-sum games related to H∞ optimal control in the presence of random delays and packet losses is solved in forward-in-time manner. This approach does not require the knowledge of the system matrices since it uses Q-learning. The proposed stochastic optimal control approach, referred as adaptive dynamic programming (ADP), involves solving the action dependent Q-function Q(z,u, d) of the zero-sum game instead of solving the state dependent value function J (z) which satisfies a corresponding Game Theoretic Riccati equation (GRE). An adaptive estimator (AE) is proposed to learn the Q-function online and value and policy iterations are not needed unlike in traditional ADP schemes. Update laws for tuning the unknown parameters of adaptive estimator (AE) are derived. Lyapunov theory is used to show that all signals are asymptotic stable (AS) and that the approximated control and disturbance signals converge to optimal control and disturbance inputs. Simulation results are included to show the effectiveness of the scheme.","PeriodicalId":285096,"journal":{"name":"2011 IEEE International Symposium on Intelligent Control","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134023831","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}
J. Na, G. Herrmann, X. Ren, M. Mahyuddin, P. Barber
{"title":"Robust adaptive finite-time parameter estimation and control of nonlinear systems","authors":"J. Na, G. Herrmann, X. Ren, M. Mahyuddin, P. Barber","doi":"10.1109/ISIC.2011.6045402","DOIUrl":"https://doi.org/10.1109/ISIC.2011.6045402","url":null,"abstract":"This paper exploits an alternative adaptive parameter estimation and control approach for nonlinear systems. An auxiliary filter is developed to derive a representation of the parameter estimation error, which is combined with an adaptive law to guarantee the exponential convergence of the control error as well as the estimation error. The proposed method is further improved via a sliding mode technique to achieve the finite-time (FT) error convergence. The traditional persistent excitation (PE) is simplified as an a priori verifiable sufficiently rich (SR) requirements on the demand signal. The robustness of the control schemes with bounded disturbances is also investigated. The developed methods are finally tested via simulations.","PeriodicalId":285096,"journal":{"name":"2011 IEEE International Symposium on Intelligent Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130997962","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":"Performance information in risk-averse control of model-following systems","authors":"K. Pham","doi":"10.1109/ISIC.2011.6045390","DOIUrl":"https://doi.org/10.1109/ISIC.2011.6045390","url":null,"abstract":"The paper presents an extension of the theory of risk-averse control of a linear-quadratic class of model-following control systems with incomplete state feedback. It is shown that performance information can improve control decisions with only available output measurements for system performance reliability but information structures can also be costly. Many of the results entail measures of the amount, value, and cost of performance information, and the design of model-following control strategy with risk aversion. It becomes clear that the topic of performance information in control is of central importance for future research and development of correct-by-design of high performance and reliable systems.","PeriodicalId":285096,"journal":{"name":"2011 IEEE International Symposium on Intelligent Control","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115634943","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":"Variable neural adaptive robust observer for uncertain systems","authors":"Jianming Lian, Jianghai Hu, S. Żak","doi":"10.1109/ISIC.2011.6045403","DOIUrl":"https://doi.org/10.1109/ISIC.2011.6045403","url":null,"abstract":"The design of variable neural adaptive robust observer is proposed for the state estimation of a class of uncertain systems. The proposed observer incorporates a variable-structure radial basis function (RBF) network to approximate unknown system dynamics. The RBF network can determine its structure on-line dynamically by adding or removing RBFs. The observer gain matrix is obtained by solving an optimization problem subject to linear matrix inequalities. The structure variation of the RBF network is taken into account in the stability analysis through the use of the piecewise quadratic Lyapunov function. The effectiveness of the proposed observer is illustrated with a simulation example.","PeriodicalId":285096,"journal":{"name":"2011 IEEE International Symposium on Intelligent Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131220229","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":"Mean-square state and parameter estimation for stochastic linear systems with Poisson noise","authors":"M. Basin, J. Maldonado","doi":"10.1109/ISIC.2011.6045419","DOIUrl":"https://doi.org/10.1109/ISIC.2011.6045419","url":null,"abstract":"This paper presents the mean-square state and parameter estimation problem for stochastic linear systems with unknown multiplicative and additive parameters over linear observations, where unknown parameters are considered Poisson processes. The original problem is reduced to the filtering problem for an extended state vector that incorporates parameters as additional states. The obtained optimal filter for the extended state vector also serves as the optimal identifier for the unknown parameters. Performance of the designed optimal state filter and parameter identifier is verified for both, stable and unstable, stochastic linear systems and compared against the mean-square estimator designed for polynomial systems with white Gaussian noises.","PeriodicalId":285096,"journal":{"name":"2011 IEEE International Symposium on Intelligent Control","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124761441","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}