in this research, an innovative numerical simulating approach for time domain analysis of multi degrees of freedom structures with uncertainty in dynamic properties is presented. a full scale finite element model of multi-story and multi bays of three sample structures has been constructed. the reduced order model of structure with holding the dominant and effective gramians in the balanced sta...

Query optimizers normally compile queries into one optimal plan by assuming complete knowledge of all cost parameters such as selectivity and resource availability. The execution of such plans could be sub-optimal when cost parameters are either unknown at compile time or change significantly between compile time and runtime [Loh89, GrW89]. Parametric query optimization [INS+92, CG94, GK94] opt...

The paper concerns optimal control problems for dynamic systems governed by a parametric family of discrete approximations of control systems with continuous time. Discrete approximations play an important role in both qualitative and numerical aspects of optimal control and occupy an intermediate position between discrete-time and continuous-time control systems. The central result in optimal ...

In this paper, a finite-horizon neuro-optimal tracking control strategy for a class of discrete-time nonlinear systems is proposed. Through system transformation, the optimal tracking problem is converted into designing a finite-horizon optimal regulator for the tracking error dynamics. Then, with convergence analysis in terms of cost function and control law, the iterative adaptive dynamic pro...

Optimal control methods are generally too expensive to be applied on-line and in real-time to the control of robots. An alternative method consists in tuning a parametrized reactive controller so that it converges to optimal behavior. In this paper we present such a method based on the “direct Policy Search” paradigm to get a cost-efficient control policy for a simulated two degrees-of-freedom ...

This paper proposes a method of robust optimal stabilization for balance systems such as rocket and missile systems, Segway human transportation systems, and inverted pendulum systems. Constant-gain controllers such as linear-quadratic regulators may not guarantee stability let alone optimality for balance systems affected by parametric variations. The robust stability and robust performance ac...

This paper considers the estimation and control of systems with parametric uncertainty. An approach that combines moving horizon estimation and model predictive control into a single min-max optimization is employed to estimate past and current values of the state, compute a sequence of optimal future control inputs, predict future values of the state, as well as estimate current values of unce...

Moat realistic control problems involve both some type of time-domain constraints and model uncertainty. However, there presently exist few design methods capable of simultaneously addressing both issues. We recently proposed to address this class of problem by using a "constrained robustness measure", generated by a constraint-set induced operator norm, to assess the stability ptoperties of a ...

Theory predicts that parametrically excited oscillators, tuned to operate under resonant condition, are capable of large-amplitude oscillation useful in diverse applications, such as signal amplification, communication, and analog computation. However, due to amplitude saturation caused by nonlinearity, lack of robustness to model uncertainty, and limited sensitivity to parameter modulation, th...

Optimal sequence alignments depend heavily on alignment scoring parameters. Given input sequences, parametric alignment is the well-studied problem that asks for all possible optimal alignment summaries as parameters vary, as well as the optimality region of alignment scoring parameters which yield each optimal alignment. But biologically correct alignments might be suboptimal for all parameter...