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175
Robust Nonlinear Fault Diagnosis in InputOutput Systems
, 1997
"... The design and analysis of fault diagnosis architectures using the modelbased analytical redundancy approach has received considerable attention during the last two decades. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performa ..."
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Cited by 16 (2 self)
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The design and analysis of fault diagnosis architectures using the modelbased analytical redundancy approach has received considerable attention during the last two decades. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performance. This paper describes a fault diagnosis algorithm for a class of nonlinear dynamic systems with modeling uncertainties when not all states of the system are measurable. The main idea behind this approach is to monitor the plant for any offnominal system behavior due to faults utilizing a nonlinear online approximator with adjustable parameters. The online approximator only uses the system input and output measurements. A nonlinear estimation model and learning algorithm are described so that the online approximator provides an estimate of the fault. The robustness, sensitivity, stability and performance properties of the nonlinear fault diagnosis scheme are rigorously established und...
Nonlinear Adaptive Control Using Neural Networks and Multiple Models
 Automatica, Special Issue on Neural Network Feedback Control
, 2001
"... In this paper, adaptive control of a class of nonlinear discrete time dynamical systems with boundedness of all signals is established by using a linear robust adaptive controller and a neural network based nonlinear adaptive controller, and switching between them by a suitably defined switching law ..."
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Cited by 16 (3 self)
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In this paper, adaptive control of a class of nonlinear discrete time dynamical systems with boundedness of all signals is established by using a linear robust adaptive controller and a neural network based nonlinear adaptive controller, and switching between them by a suitably defined switching law. The linear controller, when used alone, assures boundedness of all the signals but not satisfactory performance. The nonlinear controller may result in improved response, but may also result in instability. By using a switching scheme, it is demonstrated that improved performance and stability can be simultaneously achieved. Key words: nonlinear control, adaptive control, neural networks, multiple models, switching systems, stability, robustness, performance 1
Stable Adaptive Control and Recursive Identification Using Radial Gaussian Networks
 Proc. IEEE Conf. Decis. Control
, 1991
"... Previous work has provided the theoretical foundations of a constructive design procedure for uniform approximation of smooth functions to a chosen degree of accuracy using networks of gaussian radial basis functions. This construction and the guaranteed uniform bounds were then shown to provide the ..."
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Cited by 12 (0 self)
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Previous work has provided the theoretical foundations of a constructive design procedure for uniform approximation of smooth functions to a chosen degree of accuracy using networks of gaussian radial basis functions. This construction and the guaranteed uniform bounds were then shown to provide the basis for stable adaptive neurocontrol algorithms for a class of nonlinear plants. This paper details and extends these ideas in three directions: first some practical details of the construction are provided, explicitly illustrating the relation between the free parameters in the network design and the degree of approximation error on a particular set. Next, the original adaptive control algorithm is modified to permit incorporation of additional prior knowledge of the system dynamics, allowing the neurocontroller to operate in parallel with conventional fixed or adaptive controllers. Finally, it is shown how the gaussian network construction may also be utilized in recursive identificatio...
The fusion of computationally intelligent methodologies and slidingmode control—a survey
 IEEE Transactions on Industrial Electronics
, 2001
"... Abstract—This paper surveys how some “intelligence ” can be incorporated in slidingmode controllers (SMCs) by the use of computational intelligence methodologies in order to alleviate the wellknown problems met in practical implementations of SMCs. The use of variablestructure system theory in des ..."
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Abstract—This paper surveys how some “intelligence ” can be incorporated in slidingmode controllers (SMCs) by the use of computational intelligence methodologies in order to alleviate the wellknown problems met in practical implementations of SMCs. The use of variablestructure system theory in design and stability analysis of fuzzy controllers is also discussed by drawing parallels between fuzzy control and SMCs. An overview of the research and applications reported in the literature in this respect is presented. Index Terms—Computational intelligence, slidingmode control, soft computing.
From Theory to Practice: Distributed Coverage Control Experiments with Groups of Robots
"... Summary. A distributed algorithm is presented that causes a network of robots to spread out over an environment, while aggregating in areas of high sensory interest. The algorithm is a discretetime interpretation of a controller previously introduced by the authors. The algorithmic implications of ..."
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Summary. A distributed algorithm is presented that causes a network of robots to spread out over an environment, while aggregating in areas of high sensory interest. The algorithm is a discretetime interpretation of a controller previously introduced by the authors. The algorithmic implications of implementing this controller on a physical platform are discussed, and results are presented for 16 robots in two experiments. It is found that the algorithm performs well despite the presence of realworld complications. 1
Neural network adaptive robust control with application to precision motion control of linear motors
 International Journal of Adaptive Control and Signal Processing, 2000 (Accepted for the special issue on Developments in Intelligent Control for Industrial Applications
, 2001
"... In this paper, the recently proposed neural network adaptive robust control (NNARC) design are generalized to synthesize performance oriented control laws for a class of nonlinear systems transformable to the semistrict feedback forms through the incorporation of backstepping design techniques. Al ..."
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Cited by 11 (3 self)
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In this paper, the recently proposed neural network adaptive robust control (NNARC) design are generalized to synthesize performance oriented control laws for a class of nonlinear systems transformable to the semistrict feedback forms through the incorporation of backstepping design techniques. All unknown but repeatable nonlinearities in system are approximated by outputs of multilayer neural networks to achieve a better model compensation and an improved performance. Through the use of discontinuous projections with fictitious bounds, a controlled online training of all NN weights is achieved. Robust control terms can then be constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy. 1
Stable MultiInput MultiOutput Adaptive Fuzzy/Neural Control
, 1999
"... In this letter, stable direct and indirect adaptive controllers are presented that use Takagi–Sugeno (T–S) fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time multiinput multioutput (MI ..."
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Cited by 11 (1 self)
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In this letter, stable direct and indirect adaptive controllers are presented that use Takagi–Sugeno (T–S) fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time multiinput multioutput (MIMO) square nonlinear plants with poorly understood dynamics. The direct adaptive scheme allows for the inclusion of a priori knowledge about the control input in terms of exact mathematical equations or linguistics, while the indirect adaptive controller permits the explicit use of equations to represent portions of the plant dynamics. We prove that with or without such knowledge the adaptive schemes can “learn” how to control the plant, provide for bounded internal signals, and achieve asymptotically stable tracking of the reference inputs. We do not impose any initialization conditions on the controllers and guarantee convergence of the tracking error to zero.
Nonlinear Iterative Learning by an Adaptive Lyapunov Technique
, 1999
"... We consider the iterative learning control problem from an adaptive control viewpoint. It is shown that many standard adaptive designs can be modified in a straightforward manner to give a solution to either the feedback or feedforward ILC problem. In particular we show that many of the common assum ..."
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Cited by 10 (0 self)
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We consider the iterative learning control problem from an adaptive control viewpoint. It is shown that many standard adaptive designs can be modified in a straightforward manner to give a solution to either the feedback or feedforward ILC problem. In particular we show that many of the common assumptions of nonlinear iterative learning control can be relaxed: eg. we relax the common linear growth asssumption on the nonlinearities and handle systems of arbitrary relative degree. Furthermore it is shown that these new ILC designs have the power to solve a new ILC problem: the learning of unseen trajectories (generalisation) . It is shown that generally a linear rate of convergence of the MSE can be achieved, and some simple robustness analyses are given. For linear plants we show that a linear rate of MSE convergence can be achieved for nonminimum phase plants. 1 Introduction The purpose of this paper is to consider the Iterative Learning Control (ILC) problem from an adaptive control...
Multiscale Approximation With Hierarchical Radial Basis Functions Networks
, 2004
"... An approximating neural model, called hierarchical radial basis function (HRBF) network, is presented here. This is a selforganizing (by growing) multiscale version of a radial basis function (RBF) network. It is constituted of hierarchical layers, each containing a Gaussian grid at a decreasing sc ..."
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Cited by 10 (2 self)
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An approximating neural model, called hierarchical radial basis function (HRBF) network, is presented here. This is a selforganizing (by growing) multiscale version of a radial basis function (RBF) network. It is constituted of hierarchical layers, each containing a Gaussian grid at a decreasing scale. The grids are not completely filled, but units are inserted only where the local error is over threshold. This guarantees a uniform residual error and the allocation of more units with smaller scales where the data contain higher frequencies. Only local operations, which do not require any iteration on the data, are required; this allows to construct the network in quasireal time. Through harmonic analysis, it is demonstrated that, although a HRBF cannot be reduced to a traditional waveletbased multiresolution analysis (MRA), it does employ Riesz bases and enjoys asymptotic approximation properties for a very large class of functions. HRBF networks have been extensively applied to the reconstruction of threedimensional (3D) models from noisy range data. The results illustrate their power in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by MRA.
Optimal Control by Least Squares Support Vector Machines
 Neural Networks
, 2001
"... Support vector machines have been very successful in pattern recognition and function estimation problems. In this paper we introduce the use of least squares support vector machines (LSSVM's) for the optimal control of nonlinear systems. Linear and neural full static state feedback controller ..."
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Cited by 10 (3 self)
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Support vector machines have been very successful in pattern recognition and function estimation problems. In this paper we introduce the use of least squares support vector machines (LSSVM's) for the optimal control of nonlinear systems. Linear and neural full static state feedback controllers are considered. The problem is formulated in such a way that it incorporates the Nstage optimal control problem as well as a least squares support vector machine approach for mapping the state space into the action space. The solution is characterized by a set of nonlinear equations. An alternative formulation as a constrained nonlinear optimization problem in less unknowns is given, together with a method for imposing local stability in the LSSVM control scheme. The results are discussed for support vector machines with radial basis function kernel. Advantages of LSSVM control are that no number of hidden units has to be determined for the controller and that no centers have to be specied for the Gaussian kernels when applying Mercer's condition. The curse of dimensionality is avoided in comparison with dening a regular grid for the centers in classical radial basis function networks. This is at the expense of taking the trajectory of state variables as additional unknowns in the optimization problem, while classical neural network approaches typically lead to parametric optimization problems. In the SVM methodology the number of unknowns equals the number of training data, while in the primal space the number of unknowns can be innite dimensional. The method is illustrated both on stabilization and tracking problems including examples on swinging up an inverted pendulum with local stabilization at the endpoint and a tracking problem for a ball and beam system. Keywords. N...