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131
Gaussian Networks for Direct Adaptive Control
 IEEE Transactions on Neural Networks
, 1991
"... A direct adaptive tracking control architecture is proposed and evaluated for a class of continuous time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs a network of gaussian radial ..."
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Cited by 133 (8 self)
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A direct adaptive tracking control architecture is proposed and evaluated for a class of continuous time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs a network of gaussian radial basis functions to adaptively compensate for the plant nonlinearities. Under mild assumptions about the degree of smoothness exhibited by the nonlinear functions, the algorithm is proven to be globally stable, with tracking errors converging to a neighborhood of zero. A constructive procedure is detailed, which directly translates the assumed smoothness properties of the nonlinearities involved into a specification of the network required to represent the plant to a chosen degree of accuracy. A stable weight adjustment mechanism is then determined using Lyapunov theory. The network construction and performance of the resulting controller are illustrated through simulations with example syst...
Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces
, 1996
"... A key element in the solution of reinforcement learning problems is the value function. The purpose of this function is to measure the longterm utility or value of any given state and it is important because an agent can use it to decide what to do next. A common problem in reinforcement learning w ..."
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Cited by 92 (6 self)
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A key element in the solution of reinforcement learning problems is the value function. The purpose of this function is to measure the longterm utility or value of any given state and it is important because an agent can use it to decide what to do next. A common problem in reinforcement learning when applied to systems having continuous states and action spaces is that the value function must operate with a domain consisting of realvalued variables, which means that it should be able to represent the value of infinitely many state and action pairs. For this reason, function approximators are used to represent the value function when a closeform solution of the optimal policy is not available. In this paper, we extend a previously proposed reinforcement learning algorithm so that it can be used with function approximators that generalize the value of individual experiences across both, state and action spaces. In particular, we discuss the benefits of using sparse coarsecoded funct...
Reinforcement Learning And Its Application To Control
, 1992
"... Learning control involves modifying a controller's behavior to improve its performance as measured by some predefined index of performance (IP). If control actions that improve performance as measured by the IP are known, supervised learning methods, or methods for learning from examples, can be us ..."
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Cited by 51 (2 self)
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Learning control involves modifying a controller's behavior to improve its performance as measured by some predefined index of performance (IP). If control actions that improve performance as measured by the IP are known, supervised learning methods, or methods for learning from examples, can be used to train the controller. But when such control actions are not known a priori, appropriate control behavior has to be inferred from observations of the IP. One can distinguish between two classes of methods for training controllers under such circumstances. Indirect methods involve constructing a model of the problem's IP and using the model to obtain training information for the controller. On the other hand, direct, or modelfree,...
Comparative Experiments with a New Adaptive Controller for Robot Arms
, 1992
"... This paper presents a new modelbased adaptive controller and proof of its global asymptotic stability with respect to the standard rigid body model of robot arm dynamics. Experimental data from a study of one new and several established globally asymptotically stable adaptive controllers on two ver ..."
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Cited by 31 (14 self)
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This paper presents a new modelbased adaptive controller and proof of its global asymptotic stability with respect to the standard rigid body model of robot arm dynamics. Experimental data from a study of one new and several established globally asymptotically stable adaptive controllers on two very different robot arms (i) demonstrates the superior tracking performance afforded by the modelbased algorithms over conventional PD control, (ii) demonstrates and compares the superior performance of adaptive modelbased algorithms over their nonadaptive counterparts, (iii) reconciles several previous contrasting empirical studies, and (iv) examines contexts which compromise their advantage.
Adaptive controller design for tracking and disturbance attenuation in parametricstrictfeedback nonlinear systems
 IEEE Transactions on Automatic Control
, 1996
"... Abstract — The authors develop a systematic procedure for obtaining robust adaptive controllers that achieve asymptotic tracking and disturbance attenuation for a class of nonlinear systems that are described in the parametric strictfeedback form and are subject to additional exogenous disturbance ..."
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Cited by 22 (4 self)
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Abstract — The authors develop a systematic procedure for obtaining robust adaptive controllers that achieve asymptotic tracking and disturbance attenuation for a class of nonlinear systems that are described in the parametric strictfeedback form and are subject to additional exogenous disturbance inputs. Their approach to adaptive control is performancebased, where the objective for the controller design is not only to find an adaptive controller, but also to construct an appropriate cost functional, compatible with desired asymptotic tracking and disturbance attenuation specifications, with respect to which the adaptive controller is “worst case optimal. ” In this respect, they also depart from the standard worst case (robust) controller design paradigm where the performance index is fixed priori. Three main ingredients of the paper are the backstepping methodology, worst case identification schemes, and singular perturbations analysis. Under full state measurements, closedform expressions have been obtained for an adaptive controller and the corresponding value function, where the latter satisfies a Hamilton–Jacobi–Isaacs equation (or inequality) associated with the underlying cost function, thereby leading to satisfaction of a dissipation inequality for the former. An important byproduct of the analysis is the finding that the adaptive controllers that meet the dual specifications of asymptotic tracking and disturbance attenuation are generally not certaintyequivalent, but are asymptotically so as the measure quantifying the designer’s confidence in the parameter estimate goes to infinity. To illustrate the main results, the authors include a numerical example involving a thirdorder system. Index Terms—Adaptive control, backstepping, disturbance attenuation, nonlinear systems, tracking. I.
Decentralized, Adaptive Coverage Control for Networked Robots
, 2007
"... A decentralized, adaptive control law is presented to drive a network of mobile robots to an optimal sensing configuration. The control law is adaptive in that it uses sensor measurements to learn an approximation of the distribution of sensory information in the environment. It is decentralized in ..."
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Cited by 22 (3 self)
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A decentralized, adaptive control law is presented to drive a network of mobile robots to an optimal sensing configuration. The control law is adaptive in that it uses sensor measurements to learn an approximation of the distribution of sensory information in the environment. It is decentralized in that it requires only information local to each robot. The controller is then improved upon by implementing a consensus algorithm in parallel with the learning algorithm, greatly increasing parameter convergence rates. Convergence and consensus of parameters is proven. Finally, several variations on the learning algorithm are explored with a discussion of their stability in closed loop. The controller with and without parameter consensus is demonstrated in numerical simulations. These techniques are suggestive of broader applications of adaptive control methodologies to decentralized control problems in unknown dynamical environments. 1
Decentralized, Adaptive Control for Coverage with Networked Robots
"... Abstract — A decentralized, adaptive control law is presented to drive a network of mobile robots to a nearoptimal sensing configuration. The control law is adaptive in that it integrates sensor measurements to provide a converging estimate of the distribution of sensory information in the environm ..."
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Cited by 21 (8 self)
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Abstract — A decentralized, adaptive control law is presented to drive a network of mobile robots to a nearoptimal sensing configuration. The control law is adaptive in that it integrates sensor measurements to provide a converging estimate of the distribution of sensory information in the environment. It is decentralized in that it requires only information local to each robot. A Lyapunovtype proof is used to show that the control law causes the network to converge to a nearoptimal sensing configuration, and the controller is demonstrated in numerical simulations. This technique suggests a broader application of adaptive control methodologies to decentralized control problems in unknown dynamical environments. I.
Sofge, editors. Handbook of intelligent control
, 1992
"... This book is an outgrowth of discussions that got started in at least three workshops sponsored by the National Science Foundation (NSF):.A workshop on neurocontrol and aerospace applications held in October 1990, under joint sponsorship from McDonnell Douglas and the NSF programs in Dynamic Systems ..."
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Cited by 18 (0 self)
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This book is an outgrowth of discussions that got started in at least three workshops sponsored by the National Science Foundation (NSF):.A workshop on neurocontrol and aerospace applications held in October 1990, under joint sponsorship from McDonnell Douglas and the NSF programs in Dynamic Systems and Control and Neuroengineering.A workshop on intelligent control held in October 1990, under joint sponsorship from NSF and the Electric Power Research Institute, to scope out plans for a major new joint initiative in intelligent control involving a number of NSF programs.A workshop on neural networks in chemical processing, held at NSF in JanuaryFebruary 1991, sponsored by the NSF program in Chemical Reaction Processes The goal of this book is to provide an authoritative source for two kinds of information: (1) fundamental new designs, at the cutting edge of true intelligent control, as well as opportunities for future research to improve on these designs; (2) important realworld applications, including test problems that constitute a challenge to the entire control community. Included in this book are a series of realistic test problems, worked out through lengthy discussions between NASA, NetJroDyne, NSF, McDonnell Douglas, and Honeywell, which are more than just benchmarks for evaluating intelligent control designs. Anyone who contributes to solving these problems may well be playing a crucial role in making possible the future development of hypersonic vehicles and subsequently the
Open Synchronous Cellular Learning Automata
 Journal of Computer Science and Engineering
, 2003
"... In this paper, we introduce open cellular learning automata and then study its convergence behavior. It is shown that for a class of rules called commutative rules, the open cellular learning automata in stationary external environments converges to a stable and compatible configuration. The numeric ..."
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Cited by 17 (10 self)
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In this paper, we introduce open cellular learning automata and then study its convergence behavior. It is shown that for a class of rules called commutative rules, the open cellular learning automata in stationary external environments converges to a stable and compatible configuration. The numerical results also confirm the theory. 1.