Showing papers on "Robustness (computer science) published in 1970"
01 Dec 1970
TL;DR: It is proved that sequential reproductive plans are robust in the sense that, in environments with a well-defined measure of payoff, any such plan will acquire payoff at a rate which asymptotically approaches the optimum.
Abstract: This paper presents a general formalism for defining problems of adaptation. Within this formalism a class of algorithms, the sequential reproductive plans, is defined. It is then proved that sequential reproductive plans are robust in the sense that, in environments with a well-defined measure of payoff (utility, performance), any such plan will acquire payoff at a rate which asymptotically approaches the optimum.
29 citations
TL;DR: Control approaches for a two-link flexible manipulator are studied in the context of robust synthesis for Linear Parameter-Varying systems and show that this gain-scheduling technique maximizes both performance and robustness over the entire range of manipulator configurations.
Abstract: Control approaches for a two-link flexible manipulator are studied in the context of robust synthesis for Linear Parameter-Varying systems. Different treatments of the inertia matrix variations in the manipulator system are examined in three control law design formulations. The first two designs are based upon scaled H^ or structured singular value synthesis. The third design makes use of a new approach for robust gain-scheduled synthesis. Results show that this gain-scheduling technique maximizes both performance and robustness over the entire range of manipulator configurations.
19 citations
10 citations
TL;DR: In this article, the authors investigated tolerance intervals based on the assumption of normality for robustness with respect to departure from normality in kurtosis and showed that for a certain range of probabilities this asymptotic robustness is surprisingly good.
Abstract: SUMMARY Tolerance intervals based on the assumption of normality are investigated for robustness with respect to departure from normality in kurtosis. Two types of tolerance intervals and two types of robustness are considered. It is shown that, for the situation considered, the asymptotic robustness, as the number of observations tends to infinity, depends only on the form of the nonnormal distribution and the probabilities associated with the interval. It turns out that for a certain range of probabilities this asymptotic robustness is surprisingly good.
7 citations
DOI•
01 Jan 1970TL;DR: This paper is to present a different approach to solve the optimization problem by using evolutionary techniques, and Genetic Algorithms (GAs) probably is one of the most used evolutionary methods due to its robustness, easy implementation as well as flexibility.
Abstract: During the last years more and more research efforts have done for shape optimization due to its relevance in structural design. This method changes the boundary shape of the structure to find the optimal design by verifying the prescribed constraints. The conventional approach is usually to replace the original implicit design problem by solving a sequence of explicit subproblems, each one constructed by using local approximation concepts involving function values an derivatives at the current design point. This is done because the objective function and constraints depend implicitly upon design variables. The aim of this paper is to present a different approach to solve the optimization problem by using evolutionary techniques. Genetic Algorithms (GAs) probably is one of the most used evolutionary methods due to its robustness, easy implementation as well as flexibility. All these characteristic allows the application of this method for a wide variety of engineering nonconventional problems. Finally, some numerical examples are solved to demonstrate the applicability of the proposed approach.
4 citations
TL;DR: An asymptotic Linear Quadratic Gaussian (LQG) design procedure based on a modification of the Parameter Robust Linear quadraticGaussian (PRLqG) approach is developed for designing a robust controller that accounts for unmodelled dynamics and parameter uncertainty for multi-input multioutput systems.
Abstract: In this paper an asymptotic Linear Quadratic Gaussian (LQG) design procedure based on a modification of the Parameter Robust Linear Quadratic Gaussian (PRLQG) approach is developed for designing a robust controller that accounts for unmodelled dynamics and parameter uncertainty for multi-input multioutput systems. The unmodelled dynamics are assumed to be characterized as a single block dynamic uncertainty at a point in the closed-loop system. Plant parameter variations are represented as an internal feedback loop via the inputoutput decomposition. A direct structural relationship between parameter uncertainties and the weighting matrices in the design of the LQG controller is exploited. This procedure is then applied to design a robust controller for attitude control and vibration suppression of the MB-1 Space Station configuration taking into account this mixed uncertainty model. This technique yields considerable improvement in robustness with respect to parameter variation without affecting the level of nominal performance and robustness with respect to unmodelled dynamics achieved during the design. Simulations have shown that when the system is submitted to unit impulse the controller was able to impose quick convergence to the system.
3 citations
01 Jan 1970
TL;DR: The experimental results show that c2GA outperforms cGA and is a robust algorithm.
Abstract: Compressed compact genetic algorithm (c2GA) is an algorithm that utilizes the compressed chromosome encoding and compact genetic algorithm (cGA). The advantage of c2GA is to reduce the memory usage by representing population as a probability vector. In this paper, we analyze the performance in term of robustness of c2GA. Since the compression and decompression strategy employ two parameters, which are the length of repeating value and the repeat count, we vary these two parameters to see the performance affected in term of convergence speed. The experimental results show that c2GA outperforms cGA and is a robust algorithm.
3 citations
1 citations
TL;DR: A combination of the generalization of the computed torque method and the velocity gradient technique provides a simple way to obtain some known and unknown parameter adaptive control laws, in particular, the exponential path tracking adaptive control algorithms with the important properties of robustness to bounded disturbances and unmodeled dynamics.
Abstract: The paper is addressed to study the trajectory control of robotic mechanisms, particularly, to the transient performance of path tracking control systems. The coefficients and terms of the robot dynamic equations may be partly unknown and some dynamic effects may be unmodeled. One general scheme, which is a combination of the generalization of the computed torque method and the velocity gradient technique, provides a simple way to obtain some known and unknown parameter adaptive control laws, in particular, the exponential path tracking adaptive control algorithms with the important properties of robustness to bounded disturbances and unmodeled dynamics. The algorithms require the on-line measurements of the tracking error and its first derivative. The persistent excitation assumptions for desired paths are not required. The fuzzy logic control strategy is applied to obtain implement able algorithms on the base of the developed adaptive laws. The approach includes the attractive features of both strategies: robustness from direct adaptive control, and simple implementability from fuzzy logic control.
1 citations