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The following topics are dealt with: neural networks; evolutionary computing and genetic algorithms; fuzzy systems and soft computing; particle swarm optimization; artificial life and artificial immune systems; systems biology and neurobiology; support vector machine; rough and fuzzy rough set; knowledge discovery and data mining; kernel methods; supervised & semi-supervised learning; hybrid system;...
This paper presents an adaptive control method for a class of nonlinear systems with matched uncertainties. Firstly, radial basis function neural networks is adopted to approximate the unknown system perturbance, then an robust adaptive control law is developed to stabilize the system based on the so-called integral sliding mode design approach. The reachability of the sliding surface and the convergence...
This paper presents an extended Kalman filter for discrete-time nonlinear systems subject to uncertainties. The proposed filter considers that the linearization of the nonlinear functions are unknown, but within a known set. The nonlinear functions are assumed to belong to a conic region. This condition is characterized as a Lipschitz condition on the system state, control signal and the noise residuals...
In this paper, robust adaptive output feedback control is studied for a class of discrete-time nonlinear systems in output-feedback form perturbed by a class of functional nonlinear uncertainties of Lipschitz type. To construct output feedback control, the system is transformed into the form of nonlinear-auto-regressive-moving-average (NARMA), a novel future output prediction is designed based on...
This paper presents a probabilistic cutting plane technique for solving a robust feasibility problem which is to find a solution satisfying a parameter-dependent convex constraint for all possible parameter values. The proposed algorithm employs random samples of the parameter and maximum volume ellipsoid centers of candidates of the solution set. It is shown that the numbers of updates and random...
This work proposes a robust control framework to address the problem of practical tracking for a class of nonlinear systems described as hybrid automata. The framework reposes both on a suitable definition of the references to be tracked and on input-to-state stability properties of the feedback laws in order to guarantee a desired behavior of the hybrid automata in terms of robust transition between...
Neo-robust control is a new theory proposed by the authors, which utilizes both the gain and the phase information of uncertainty in robust control design. In this paper, we extend the idea of neo-robust control to a class of uncertain systems with factorized uncertainty. This class of systems is good at describing the uncertainties arising in process control systems. Conditions on robust stability,...
The nonlinear robust stability theory of Georgiou and Smith (IEEE Trans. Auto. Control, 42(9):1200-1229, 1997) is generalized to the case of notions of stability with bias terms. An example from adaptive control illustrates non trivial robust stability certificates for systems which the previous unbiased theory could not establish a non-zero robust stability margin. This treatment also shows that...
This paper aims to design a controller to robustly stabilize uncertain nonlinear systems with time-varying delay and norm bounded uncertainties via Takagi-Sugeno (T-S) fuzzy model. The stabilization conditions are given in the form of linear matrix inequalities using a single Lyapunov-Krasovskii functional (LKF) combining the introduction of some relaxation matrices and only one tuning parameter....
This paper proposes extensions to a recent control Liapunov function (CLF) based method for designing dynamical systems with trajectories that converge to the zeros of a nonlinear vector function f . Specifically, the CLF design method is extended to the case when the Jacobian of the vector function can be decomposed into a known part and a partially known part, for which certain norm bounds are known...
Our paper presents an explicit infinite-dimensional Luenberger type observer for a class of vibrating systems. Further numerical investigation is undertaken for the observer based on a rotating Euler elastic beam system, including the eigenvalue distribution of the error system and the robustness of the observer. By using the finite element method numerical simulations are carried out to illustrate...
In this paper, we develop two algorithms to robustify repetitive learning control (RLC), which deals with periodic tracking tasks for nonlinear dynamical systems with nonparametric uncertainties. The first robustification algorithm is to apply a projection operator to the control input signals directly. The second robustification algorithm is to add a damping term to the learning law. Both algorithms...
In this paper, a robust stabilization of the uncertain singularly perturbed system via a networked state feedback with the transmission time-delay is addressed. Taking its nominal system as a model plant, propagation unit and overall states chosen to overcome the difficulty of communication delay, the characterization of singular perturbation for the overall hybrid system is still preserved on each...
In recent work the authors combined integral-quadratic-constraint (IQC) based analysis with ??-gap metric based analysis to study the robustness of feedback interconnections of possibly unstable rational transfer functions. This is extended here to the case of irrational transfer functions without pure delays. Specifically, we restrict attention to the sub-algebra of Callier-Desoer class transfer...
In this paper we use bilevel programming to find the maximum difference between a reference controller and a low-complexity controller in terms of the infinity-norm difference of their control laws. A nominal MPC for linear systems with constraints, and a robust MPC for linear systems with bounded additive noise are considered as reference controllers. For possible low-complexity controllers we discuss...
Many model predictive control (MPC) schemes suffer from high computational complexity. Especially robust MPC schemes, which explicitly account for the effects of disturbances, can result in computationally intractable problems. So-called move-blocking is an effective method of reducing the computational complexity of MPC problems. Unfortunately move-blocking precludes the use of terminal constraints...
It is very necessary and meaningful to carry out research aiming at traffic signal control sub-area method. However, the accuracy and robustness of traffic signal control sub-area still can not meet the need of practice. First, this paper researches the popular methods and their key techniques. Second, the paper considered following four influencing factors: distance between intersections, major road...
This paper investigates the stabilization problem for continuous-time stochastic latency networked control systems (NCS) with non-accessible jumping modes. Based on mode-dependent Lyapunov functional and descriptor system approach, a new method for mode-independent control of such systems is developed in terms of the solvability of linear matrix inequalities (LMIs). Illustrative example demonstrates...
In this paper, a hybrid control architecture is proposed for the adaptive robust control of a class of nonlinear systems with uncertain parameter variation ranges. Specifically, the standard set-membership description of uncertainty is adopted - the bounds of the structural approximation errors associated with the parametrized models are assumed to be known but the variation ranges of model parameters...
In this paper, we consider the model reference adaptive control (MRAC) problem of switched linear systems in which the parameters and switching time instants are all unknown. We apply the output feedback variable structure (VS) based adaptive controller to switched linear systems with general relative degrees and show error convergence and signal boundedness properties by multiple Lyapunov functions...
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