Min–max control using parametric approximate dynamic programming

This study presents a computationally efficient approximate dynamic programming approach to control uncertain linear systems based on a min–max control formulation. The optimal cost-to-go function, which prescribes an optimal control policy, is estimated using piecewise parametric quadratic approximation. The approach requires simulation or operational data only at the bounds of additive disturbances or polyhedral uncertain parameters. This strategy significantly reduces the computational burden associated with dynamic programming and is not limited to a particular form of performance criterion as in previous approaches.     

Finite horizon optimal control of non-linear discrete-time switched systems using adaptive dynamic programming with ε-error bound

In this paper, we aim to solve the finite-horizon optimal control problem for a class of non-linear discrete-time switched systems using adaptive dynamic programming(ADP) algorithm. A new ε-optimal control scheme based on the iterative ADP algorithm is presented which makes the value function converge iteratively to the greatest lower bound of all value function indices within an error according to ε within finite time. Two neural networks are used as parametric structures to implement the iterative ADP algorithm with ε-error bound, which aim at approximating the value function and the control policy, respectively. And then, the optimal control policy is obtained. Finally, a simulation example is included to illustrate the applicability of the proposed method.     

Non-fragile robust H ∞ control for uncertain spacecraft rendezvous system with pole and input constraints

This article investigates the robust H _∞ control problem for a class of spacecraft rendezvous systems with parameter uncertainties and subject to input constraint, pole constraint and external and controller perturbations. Based on the Lyapunov theory, a sufficient condition for the existence of the non-fragile robust state-feedback controller is given in terms of linear matrix inequalities (LMIs). Then, proper non-fragile controller design can be cast as a convex optimisation problem subject to LMI constraints. With the obtained controller, the spacecraft rendezvous mission can be accomplished. An illustrative example is provided to show the effectiveness of the proposed control design method.     

Distributed aggregative games for Euler–Lagrange systems with system parameter uncertainties

In this paper, an aggregative game of Euler–Lagrange (EL) systems is studied, where the parameters of the EL systems are not available. To seek the Nash equilibrium of the game, a novel distributed Nash equilibrium seeking algorithm is proposed, where the system parameters are not used in the feedback control. Moreover, a Lyapunov function is constructed such that EL players are proved to exponentially converge to the Nash equilibrium of the game. Finally, an example in the electricity market is provided to illustrate our result.     

Predictor-based control for an uncertain Euler–Lagrange system with input delay

Controlling a nonlinear system with actuator delay is a challenging problem because of the need to develop some form of prediction of the nonlinear dynamics. Developing a predictor-based controller for an uncertain system is especially challenging. In this paper, tracking controllers are developed for an Euler–Lagrange system with time-delayed actuation, parametric uncertainty, and additive bounded disturbances. The developed controllers represent the first input delayed controllers developed for uncertain nonlinear systems that use a predictor to compensate for the delay. The results are obtained through the development of a novel predictor-like method to address the time delay in the control input. Lyapunov–Krasovskii functionals are used within a Lyapunov-based stability analysis to prove semi-globally uniformly ultimately bounded tracking. Experimental results illustrate the performance and robustness of the developed control methods.     

Method and system for adaptive network security using intelligent packet analysis

<美国专利>6,499,107Gleichauf, et a1.December 24,2002<发明人>Gleichauf;Robert E.(San Antonio,TX);TealDaniel M.(San Antonio,TX);Wiley;Kevin L(Elgin,TX)<代理人>Cisco Technology,Inc.(San Jose,CA)<公开号>223071<公开日>December 29,1998<美国分类>713/20]<国际分类>G06F 01l/30<检索号>713/200,201 709/223,224,229,225,100,102,103,104,226 705/8,9<摘要>A method and system for adaptlye net-work security using intelligent packet analysisare provided.The method comprises monitoringnetwork data traff…     

Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model （GFHM）. Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.     

An adaptive observer for a spacecraft–manipulator system using a camera mounted on its spacecraft

This paper deals with a motion control system for a space robot with a manipulator. Many motion controllers require the positions of the robot body and the manipulator hand with respect to an inertial coordinate system. In order to measure them, a visual sensor using a camera is frequently used. However, there are two difficulties in measuring them by means of a camera. The first one is that a camera is mounted on the robot body, and hence it is difficult to directly measure the position of the robot body by means of it. The second one is that the sampling period of a vision system with a general-purpose camera is much longer than that of a general servo system. In this paper, we develop an adaptive state observer that overcomes the two difficulties. In order to investigate its performance, we design a motion control system that is constructed by combining the observer with a PD control input, and then conduct numerical simulations for the control system. Simulation results demonstrate the effectiveness of the proposed observer.     

ℓ1-control using linear programming for systems with asymmetric bounds

In this paper, a novel way to handle the analysis and design of control systems with bounded asymmetrical signals is presented. Instead of the standard peak-to-peak norm, normally used in ?₁-optimal control, this paper proposes to use an asymmetric objective functional, which simplifies the consideration of asymmetrically bounded signals. Necessary and sufficient conditions are derived to check if some performance specifications, expressed using this objective functional, are fulfilled. The analysis and the synthesis problems are also formulated with respect to this objective functional: as in the standard ?₁ control, this approach leads to a linear programming problem which can be solved by available optimization tools. A numerical example is also studied to clarify this proposed approach.     

Wavelet based non-parametric NARX models for nonlinear input–output system identification

Wavelet based non-parametric additive NARX models are proposed for nonlinear input–output system identification. By expanding each functional component of the non-parametric NARX model into wavelet multiresolution expansions, the non-parametric estimation problem becomes a linear-in-the-parameters problem, and least-squares-based methods such as the orthogonal forward regression (OFR) approach, coupled with model size determination criteria, can be used to select the model terms and estimate the parameters. Wavelet based additive models, combined with model order determination and variable selection approaches, are capable of handling problems of high dimensionality.     

A simple algorithm for training fuzzy systems using input–output data

This paper proposes a simple algorithm for training fuzzy systems from numerical data. The main advantage of the method is the lack of complicated iterative mechanisms and therefore, its implementation is carried out easily. The suggested algorithm employs a fuzzy model with simplified rules, assuming a fuzzy partition of the input space into fuzzy subspaces. The output is inferred by expanding the model into fuzzy basis functions (FBFs), where each FBF corresponds to a certain fuzzy subspace. The number of rules and the respective premise parts (fuzzy subspaces) are determined using the nearest neighbor approach. Then, the optimal consequent parameters are obtained by the least-squares method. Finally, simulations show the validity of the method.     

Robust adaptive uniform exact tracking control for uncertain Euler–Lagrange system

This paper offers a solution to the robust adaptive uniform exact tracking control for uncertain nonlinear Euler–Lagrange (EL) system. An adaptive finite-time tracking control algorithm is designed by proposing a novel nonsingular integral terminal sliding-mode surface. Moreover, a new adaptive parameter tuning law is also developed by making good use of the system tracking errors and the adaptive parameter estimation errors. Thus, both the trajectory tracking and the parameter estimation can be achieved in a guaranteed time adjusted arbitrarily based on practical demands, simultaneously. Additionally, the control result for the EL system proposed in this paper can be extended to high-order nonlinear systems easily. Finally, a test-bed 2-DOF robot arm is set-up to demonstrate the performance of the new control algorithm.     

An integral–differential equation approach for the free vibration of a SDOF system with hysteretic damping

An integral–differential equation (IDE) in the time domain is proposed for the free vibration of a single-degree-of-freedom (SDOF) system with hysteretic damping which is different from the conventional complex stiffness model as employed in the frequency domain. The integral of the Hilbert transform is embedded in the IDE and is calculated in the Cauchy principal value sense by using a numerical folding technique. Numerical experiments show that the free vibration obtained by the frequency domain approach satisfies the IDE in the time domain. A successive iteration algorithm is employed to solve the IDE subject to forced vibration, and a convergent solution for the hysteresis loop is constructed, which matches the solution found by using the frequency domain approach. Both models, the time domain and frequency domain approaches, present the noncasual effect since they are equivalent in the mathematical sense.     

Recursive identification for Hammerstein–Wiener systems with dead-zone input nonlinearity

A new recursive algorithm is proposed for the identification of a special form of Hammerstein–Wiener system with dead-zone nonlinearity input block. The direct motivation of this work is to implement on-line control strategies on this kind of system to produce adaptive control algorithms. With the parameterization model of the Hammerstein–Wiener system, a special form of model estimation error is defined; and then its approximate formula is given for the following derivation. Based on these, a recursive identification algorithm is established that aims at minimizing the sum of the squared parameter estimation errors. The conditions of uniform convergence are obtained from the property analysis of the proposed algorithm and an adaptive setting method for a weighted factor in the algorithm is given, which enhances the convergence of the proposed algorithm. This algorithm can also be used for the identification of the Hammerstein systems with dead-zone nonlinearity input block. Three simulation examples show the validity of this algorithm.     

Experimental validation of rigid-flexible coupling dynamic formulation for hub–beam system

The aim of this paper is to compare the accuracy of the absolute nodal coordinate formulation and the floating frame of reference formulation for the rigid-flexible coupling dynamics of a three-dimensional Euler–Bernoulli beam by numerical and experimental validation. In the absolute nodal coordinate formulation, based on geometrically exact beam theory and considering the torsion effect, the material curvature of the beam is derived, and then variational equations of motion of a three-dimensional beam are obtained, which consist of three position coordinates, two slope coordinates, and one rotational coordinate. In the floating frame of reference formulation, the displacement of an arbitrary point on the beam is described by the rigid-body motion and a small superimposed deformation displacement. Based on linear elastic theory, the quadratic terms of the axial strain are neglected, and the curvatures are simplified to the first order. Considering both the linear damping and the quadratic air resistance damping, the equations of motion of the multibody system composed of air-bearing test bed and a cantilevered three-dimensional beam are derived based on the principle of virtual work. In order to verify the results of the computer simulation, two experiments are carried out: an experiment of hub–beam system with large deformation and a dynamic stiffening experiment. The comparison of the simulation and experiment results shows that in case of large deformation, the frequency result obtained by the floating frame of reference formulation is lower than that obtained by the experiment. On the contrary, the result obtained by the absolute nodal coordinate formulation agrees well with that obtained by the experiment. It is also shown that the floating frame of reference formulation based on linear elastic theory cannot reveal the dynamic stiffening effect. Finally, the applicability of the floating frame of reference formulation is clarified.     

Robust H∞ dynamic output feedback control for spacecraft rendezvous with poles and input constraint

This paper is concerned with the output feedback control problem for spacecraft rendezvous subject to target angular velocity uncertainty and controller uncertainty, external disturbance and input constraint. A general full-order dynamic output feedback (DOF) controller is proposed. As a stepping-stone, the H_∞ performance requirement, poles and input constraint are analysed separately via linear matrix inequalities (LMIs). Then, with the obtained results, the controller design problem is cast into a convex problem subject to a set of LMI constraints through a critical change of controller variables. Furthermore, when the system states are all available, a reduced sufficient condition of the non-fragile state feedback controller is given. Compared with existing results, the designed controller has overcome the disadvantage of strictly proper DOF controller, where the initial value of the control input is zero. Besides, the constraint on poles placement is relaxed. A numerical simulation is performed to verify the effectiveness of the proposed method.     

Local and global optimization for Takagi–Sugeno fuzzy system by memetic genetic programming

This work presents a method to incorporate standard neuro-fuzzy learning for Takagi–Sugeno fuzzy systems that evolve under a grammar driven genetic programming (GP) framework. This is made possible by introducing heteroglossia in the functional GP nodes, enabling them to switch behavior according to the selected learning stage. A context-free grammar supports the expression of arbitrarily sized and composed fuzzy systems and guides the evolution. Recursive least squares and backpropagation gradient descent algorithms are used as local search methods. A second generation memetic approach combines the genetic programming with the local search procedures. Based on our experimental results, a discussion is included regarding the competitiveness of the proposed methodology and its properties. The contributions of the paper are: (i) introduction of an approach which enables the application of local search learning for intelligent systems evolved by genetic programming, (ii) presentation of a model for memetic learning of Takagi–Sugeno fuzzy systems, (iii) experimental results evaluating model variants and comparison with state-of-the-art models in benchmarking and real-world problems, (iv) application of the proposed model in control.     

Continuous–discrete-time adaptive observers for nonlinear systems with sampled output measurements

This paper considers the continuous–discrete-time adaptive observer (CDAO) design for a class of nonlinear systems with unknown constant parameters and sampled output measurements. The proposed observer is actually an impulsive system, since the observer state flows according to a set of differential equations and with instantaneous state jumps corresponding to measured samples and their estimates, and an inter-sample output predictor is used to predict the output during sampling intervals. By assuming appropriate persistent excitation conditions and following a technical lemma, an upper bound of the sampling intervals is derived, with which the convergence of the observer state and unknown parameters can be ensured. Finally, the proposed observer is used in examples of chaotic oscillators and single-link flexible-joint robot manipulator to show the effectiveness.     

Predictor–corrector for non-linear differential and integral equation with fractal–fractional operators

Engineering with Computers - Fractal–fractional differential and integral operators have been recognized recently as superior operators as they are able to depict physical problem with both...     

Neural network-based adaptive multiuser detection schemes in SDMA–OFDM system for wireless application

Neural network applications in adaptive multiuser detection (MUD) schemes are suggested here in the context of space division multiple access–orthogonal frequency division multiplexing system. In this paper, various neural network (NN) models like feed forward network (FFN), recurrent neural network (RNN) and radial basis function network (RBFN) are adopted for MUD. MUD using NN models outperforms other existing schemes like genetic algorithm--assisted minimum bit error rate (MBER) and minimum mean square error MUDs in terms of BER performance and convergence speed. Among these NN models, the FNN MUD performs efficiently as RNN in full load scenario, where the number of users is equal to number of receiving antennas. In overload scenario, where the number of users is more than the number of receiving antennas, the FNN MUD performs better than RNN MUD. Further, the RBFN MUD provides a significant enhancement in performance over FNN and RNN MUDs under both overload and full load scenarios because of its better classification ability due to Gaussian nonlinearity. Extensive simulation analysis considering Stanford University Interim channel models applied for fixed wireless applications shows improvement in convergence speed and BER performance of the proposed NN-based MUD algorithms.