查看更多>>摘要：In this paper, we investigate the problem of robust H_∞ filter design for a class of discrete-time nonlinear systems. The systems under consider involves time-varying delays and parameters uncertainties. The main objective is to design a linear full-order filter to ensure that the resulting filtering error system is asymptotically stable with a prescribed H_∞ performance level. By constructing an appropriate Lyapunov-Krasovskii functional, some novel sufficient conditions are established to guarantee the filter error dynamics system is robust asymptotically stable H_∞ with performance γ, and the H_∞ filter is designed in term of linear matrix inequalities. Finally, a numerical example is provided to illustrate the efficiency of proposed method.
查看更多>>摘要：Linear time-invariant descriptor-type time-delay systems are considered. A robust stabilizing controller design approach for such systems is introduced. Uncertainties both in the time-delays and in other system parameters are considered. A frequency-dependent scalar bound on such uncertainties is first derived. Once this bound is found, the controller design is completely based on the nominal model. However, satisfying a scalar frequency-dependent condition, which uses the derived bound, guarantees robust stability. An example is also presented to illustrate the proposed approach.
查看更多>>摘要：The failure tree and J-M model method are lack of analysis of the importance of each component model, which leads to the low reliability of the analysis results. In view of this problem, a Monte Carlo method based on the shape of the English long-distance robot is proposed. In view of the configuration of the robot, the realization process of the robot shape fluid dynamics system is analyzed. The frequency of accident is determined by Monte Carlo simulation, which is used as the reliability index of the system. In MATLAB, the reliability of the shape fluid dynamic system of robot is analyzed by Monte Carlo method. The system importance name and parameters are determined. The parameter conforms to the statistical function of random variables of each corresponding probability distribution function. According to the parameters, the function of the structure is established. The system is divided into reliable state, failure state and limit state with 0 as the dividing point, and the actual failure probability of the system is calculated. The numerical solution of log domain is simulated by the method of statistical calculation of random variables, and the actual failure probability is expressed by normal distribution function. The experimental results show that the actual failure probability of the method is lower than 5% under any working load, and the reliability of the analysis results is high.
查看更多>>摘要：This paper presents an algorithm for all achievable coefficients of Proportional Integral Derivative (PID) controllers in an integral-derivative plane that stabilizes and satisfies additive mixed sensitivity constraint with an uncertain time delay for a continuous-time system. This algorithm solves the singularity problem of designing PID controllers in the integral and derivative plane and estimates achievable ranges of proportional gain of the PID controllers. A numerical cascaded ball and beam with unity feedback control of an SRV-DC motor and uncertain communication time delays in the system process demonstrate the application of this methodology. In this application, the additive weight bounds the additive errors for the cascaded ball and beam and the closed-loop SRV-DC motor system transfer function with the internal communication time delays.
查看更多>>摘要：This paper is concerned with the problem of robust exponential stabilization for a class of nonlinear uncertain systems with time-varying delays. By using appropriately chosen Lyapunov-Krasovskii functional, together with the Finsler's lemma, sufficient conditions for exponential stability of nonlinear uncertain systems with time-varying delays are proposed in terms of linear matrix inequality (LMI). Then, novel sufficient conditions are developed to ensure the nonlinear uncertain system with time-varying delay is robust exponentially stabilizable in terms of linear matrix inequality with state feedback control. Finally, a numerical example is given to illustrate the efficiency of proposed methods.
查看更多>>摘要：Cubic parametric curves are used in many applications including the CAD/CAM systems. Especially the Hermite, Bezier and Coons formulations of a cubic parametric curve are used in E~2 and E~3 space. This paper presents efficient algorithm for the intersection computation of a cubic parametric curve with the Axis Aligned Bounding Box (AAB Box). Usual solution is to represent the cubic curve by a polyline, i.e. actually by sampled points of the given curve. However, this approach is dependent on the sampling frequency and can lead to problems especially in CAD/CAM systems and numerically controlled machines use.
查看更多>>摘要：The purpose of this paper is to stabilize the annular pressure profile throughout the wellbore continuously while drilling. A new nonlinear dynamical system is developed and a controller is designed to stabilize the annular pressure and achieve asymptotic tracking by applying feedback control of the main pumps. Hence, the paper studies the control design for the well known Managed Pressure Drilling system (MPD). MPD provides a closed-loop drilling process in which pore pressure, formation fracture pressure, and bottom-hole pressure are balanced and managed at the surface. Although, responses must provide a solution for critical downhole pressures to preserve drilling efficiency and safety. Our MPD scheme is elaborated in reference to a nontrivial back-stepping control procedure and the effectiveness of the proposed control laws are shown by simulations.
查看更多>>摘要：In this paper, interval prediction model is studied for model predictive control (MPC) strategy with unknown but bounded noise. After introducing the family of models and some basic information, some computational results are presented to construct interval predictor model, using linear regression structure whose regression parameters are included in a sphere parameter set. A size measure is used to scale the average amplitude of the predictor interval, then one optimal model that minimizes this size measure is efficiently computed by solving a linear programming problem. The active set approach is applied to solve the linear programming problem, and based on these optimization variables, the predictor interval of the considered model with sphere parameter set can be directly constructed. As for choosing a fixed non-negative number in our given size measure, a better choice is proposed by using the Karush-Kuhn-Tucker (KKT) optimality conditions. In order to apply interval prediction model into model predictive control, the midpoint of that interval is substituted in a quadratic optimization problem with inequality constrained condition to obtain the optimal control input. After formulating it as a standard quadratic optimization and deriving its dual form, the Gauss-Seidel algorithm is applied to solve the dual problem and convergence of Gauss-Seidel algorithm is provided too. Finally simulation examples confirm our theoretical results.
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