• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.118-123
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• 1989

This work deals with fast-to-compute global control laws for time-optimal motion of strongly nonlinear dynamic systems like resolute robots. the theory of cell-to-cell mappings for dynamical systems offer the possibility of doing the vast majority of the control law computation offline in case of time optimization with constrained inputs. These cells result from a coarse discretization of likely swaths of state space into a set of nonuniform, contiguous volumes of relatively simple shapes. Once the cells have been designed, the bang-bang schedules for the inputs are determined for all likely starting cells and terminating cells. the resulting control law is an open-loop optimal control law with feedback monitoring and correction.

• Journal of the Chungcheong Mathematical Society
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• v.33 no.1
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• pp.19-32
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• 2020

We discuss the coexistence of positive solutions to certain strongly-coupled predator-prey elliptic systems under the homogeneous Dirichlet boundary conditions. The sufficient condition for the existence of positive solutions is expressed in terms of the spectral property of differential operators of nonlinear Schrödinger type which reflects the influence of the domain and nonlinearity in the system. Furthermore, applying the obtained results, we investigate the sufficient conditions for the existence of positive solutions of a predator-prey system with degenerate diffusion rates.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.372-377
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• 1996

A time-optimal control law for quick, strongly nonlinear systems like revolute robots has been developed and demonstrated. This procedure involves the utilization of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the known and unknown systems with constrained inputs and/or states. The nature of neural networks as a parallel processor would circumvent the problem of "curse of dimensionality".ity".uot;.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2283-2285
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• 2001

This paper proposed to a neural network based fuzzy control (neuro-fuzzy control) technique for attitude control of helicopter with strongly dynamic nonlinearities and derived a helicopter aerodynamic torque equation of helicopter and the force balance equation. A neuro-fuzzy system is a feedforward network that employs a back-propagation algorithm for learning purpose. A neuro-fuzzy system is used to identify nonlinear dynamic systems. Hence, this paper presents methods for the design of a neural network(NN) based fuzzy controller(that is, neuro-fuzzy control) for a helicopter of nonlinear MIMO systems. The proposed neuro-fuzzy control determined to a input-output membership function in fuzzy control and neural networks constructed to improve through learning of input-output membership functions determined in fuzzy control.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.274-279
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• 1991

The development of a guided weapon system, such as a tactical missile, requires a performance analysis of a nonlinear system. Generally, the Monte Carlo analysis method is used for this purpose. The limitation of this method, a large number of simulations, for a nonlinear system performance analysis strongly motivated the development of a more efficient analytic technique. In this paper, the statisfical linearization methods is used for the performance analysis to the guided weapon system with the help of covariance analysis technique. Because the statistical linearization methods cannot be used to the look-up table nonlinear form such as aerodynamic coefficients, the second order polynomial representations is obtained from the table using the Lagrange interpolating polynomial and linearized statistically. Simple simulations about initial state conditions and random component in guidance command shows the results of this technique.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.141-144
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• 2001

This paper proposes a decentralized control technique for 2-dimensional experimental helicopter systems. The decentralized control technique is especially suitable in large-scale control systems. We derive the stabilization condition for the interconnected Takagi-Sugeno (75) fuzzy system using the rigorous tool - Lyapunov stability criterion and formulate the controller design condition in terms of linear matrix inequality (LMI). To demonstrate the feasibility of the proposed method, we include the experiment result as well as a computer simulation one, which strongly convinces us the applicability to the industry.

• Coupled systems mechanics
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• v.9 no.1
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• pp.5-28
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• 2020

Despite a widely-held belief that the finite element method is the method for the solution of solid mechanics problems, which has for 30 years dissuaded solid mechanics scientists from paying any attention to the finite volume method, it is argued that finite volume methods can be a viable alternative. It is shown that it is simple to understand and implement, strongly conservative, memory efficient, and directly applicable to nonlinear problems. A number of examples are presented and, when available, comparison with finite element methods is made, showing that finite volume methods can be not only equal to, but outperform finite element methods for many applications.

• Journal of the Korean Institute of Navigation
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• v.23 no.3
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• pp.35-44
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• 1999

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.

• Structural Engineering and Mechanics
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• v.8 no.1
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• pp.27-38
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• 1999

Cables are used in many applications such as cable-stayed bridges, suspension bridges, transmission lines, telephone lines, etc. Generally, the linear relationship is inadequate to present the behavior of cable structure. In finite element analysis, cables have always been modeled as truss elements. For these types of model, the nonlinear behavior of cables has been always ignored. In order to investigate the importance of the nonlinear effect on the structural system, the effect of cable stiffness has been studied. The nonlinear behavior of cable is due to its sag. Therefore, the cable pretension provides a large portion of the inherent stiffness. Since a cable-stayed bridge has numerous degrees of freedom, analytical methods at present are not convenient to solve this type of structures but numerical methods may be feasible. It is necessary to provide a different and more representative analytical model in order to present the effect of cable stiffness on cable-stayed bridges in numerical analysis. The characteristics of cable deformation have also been well addressed. A formulation of modified modulus of elasticity has been proposed using a numerical parametric study. In order to investigate realistic bridges, a cable-stayed bridge having the geometry similar to that of Quincy Bayview Bridge is considered. The numerical results indicate that the characteristics of the cable stiffness are strongly nonlinear. It also significantly affects the structural behaviors of cable-stayed bridge systems.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.6
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• pp.90-98
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• 1997

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilzation of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the systems with constrained inputs and/or states. A neural identifier or a genetic algorithm identifier could be utilized for modeling the partially known systems and the unknown systems. The nature of neural networks as a parallel processor would circumvent the problem of "curwe of dimensionality". The control law has been demonstrated for both a torque input motor and a velocity input motor identified by a genetic algorithm called GENOCOPed GENOCOP.