• 한국해양공학회지
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• 제19권1호
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• pp.87-94
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• 2005

This paper investigates the constant-level luffing and time optimal control of jib cranes. The constant-level luffing, which is the sustainment of the load at a constant height during luffing, is achieved by analyzing the kinematic relationship between the angular displacement of a boom and that of the main hoist motor of a jib crane. Under the assumption that the main body of the crane does not rotate, the equations of motion of the boom are derived using Newton's Second Law. The dynamic equations for the crane system are highly nonlinear; therefore, they are linearized under the small angular motion of the load to apply linear control theory. This paper investigates the time optimal control from the perspective of no-sway at a target point. A stepped velocity pattern is used to design the moving path of the jib crane. Simulation results demonstrate the effectiveness of the time optimal control, in terms of anti-sway motion of the load, while luffing the crane.

• 대한수학회보
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• 제50권1호
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• pp.321-341
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• 2013

In this paper, we discuss the a posteriori error estimates of the semidiscrete mixed finite element methods for quadratic optimal control problems governed by linear hyperbolic equations. The state and the co-state are discretized by the order $k$ Raviart-Thomas mixed finite element spaces and the control is approximated by piecewise polynomials of order $k(k{\geq}0)$. Using mixed elliptic reconstruction method, a posterior $L^{\infty}(L^2)$-error estimates for both the state and the control approximation are derived. Such estimates, which are apparently not available in the literature, are an important step towards developing reliable adaptive mixed finite element approximation schemes for the control problem.

• Journal of applied mathematics & informatics
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• 제31권3_4호
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• pp.479-490
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• 2013

In this paper, we investigate a priori error estimates and superconvergence of varitional discretization for nonlinear parabolic optimal control problems with control constraints. The time discretization is based on the backward Euler method. The state and the adjoint state are approximated by piecewise linear functions and the control is not directly discretized. We derive a priori error estimates for the control and superconvergence between the numerical solution and elliptic projection for the state and the adjoint state and present a numerical example for illustrating our theoretical results.

• 한국산업융합학회 논문집
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• 제18권2호
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• pp.67-74
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• 2015

Two-wheeled driying mobild robots are precise controlled in terms of linear contol methods without considering the nonlinear dynamical characteristics. However, in the high maneuvering situations such as fast turn and abrupt start and stop, such neglected terms become dominant and heavy influence the overall driving performance. This study describes the nonlinear optimal control method to take advantage of the exact nonlinear dynamics of the balancing robot. Simulation results indicate that the optimal control outperforms in the respect of transient performance and required wheel torques. A design example is suggested for the state matrix that provides design flexibility in the control. It is shown that a well-planned state matrix by reflecting the physics of a balancing robot greatly conrtibutes to the driving performance and stability.

• ETRI Journal
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• 제39권3호
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• pp.345-352
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• 2017

Traffic management is a highly beneficial mechanism for satisfying quality-of-service requirements and overcoming the resource scarcity problems in networks. This paper introduces an optimal connection admission control mechanism to decrease the packet loss ratio and end-to-end delay in cognitive radio sensor networks (CRSNs). This mechanism admits data flows based on the value of information sent by the sensor nodes, the network state, and the estimated required resources of the data flows. The number of required channels of each data flow is estimated using a proposed formula that is inspired by a graph coloring approach. The proposed admission control mechanism is formulated as a semi-Markov decision process and a linear programming problem is derived to obtain the optimal admission control policy for obtaining the maximum reward. Simulation results demonstrate that the proposed mechanism outperforms a recently proposed admission control mechanism in CRSNs.

• 제어로봇시스템학회논문지
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• 제5권7호
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• pp.862-867
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• 1999

In general, the realization of high performance brushless permanent magnet motors which are widely used in servo drive is focused on the linear control for ripple-free torque. This is also the main problem that should be solved in all AC motors including induction motor to achieve high performance control, and recent papers deal with this problem. In this paper, the novel optimal excitation scheme of brushless permanent magnet motor producing loss-minimized ripple-free torque based on the d-q-0 reference frame is presented including 3 phase unbalanced condition. The optimized phase current waveforms that are obtained by the proposed method can be a reference values and the motor winding currents are forced to track it by delta modulation technique. As a results, it can be shown that the proposed work can minimize the torque ripple by the optimal excitation current for brushless permanent magnet motor with any arbitrary phase back EMF waveform. Simulation and experimental results prove the validity and practical applications of the proposed control scheme.

• 대한전기학회논문지
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• 제43권7호
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• pp.1186-1192
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• 1994

This paper presents an algorithm to design a robust digital model following servo control system in which optimal linear quadratic regulator problem is used to design the control system that make the step/ramp response of the plant kept close to a specified ideal step/ramp response of the model. The quadratic criterion function for a continuous system is used to design the robust digital servo control system. The feasibility of the design technique is shown by the simulation and the proposed method is applied to the speed control of DC servo motor.

• Structural Engineering and Mechanics
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• 제88권6호
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• pp.535-549
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• 2023

Deep extreme learning machine (DELM) and multi-verse optimization algorithms (MVO) are hybridized for designing an optimal and adaptive control framework for uncertain buildings. In this approach, first, a robust model predictive control (RMPC) scheme is developed to handle the problem uncertainty. The optimality and adaptivity of the proposed controller are provided by the optimal determination of the tunning weights of the linear programming (LP) cost function for clustered external loads using the MVO. The final control policy is achieved by collecting the clustered data and training them by DELM. The efficiency of the introduced control scheme is demonstrated by the numerical simulation of a ten-story benchmark building subjected to earthquake excitations. The results represent the capability of the proposed framework compared to robust MPC (RMPC), conventional MPC (CMPC), and conventional DELM algorithms in structural motion control.

• 전기의세계
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• 제28권6호
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• pp.47-51
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• 1979

An optimal problem in which the dynamics is nonlinear and the cost functional includes a discontinuous integrand is investigated. By using Neustadt's abstract maximum principle, a necessary conditions in the form of Pontryagin's maximum principle is derived and it is further shown that this necessary condition is also a sufficient condition for normal problems with linear-in-the-state systems.

• Structural Engineering and Mechanics
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• 제39권1호
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• pp.147-168
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• 2011

To ensure safety and long term performance, structural control has rapidly matured over the past decade into a viable means of limiting structural responses to strong winds and earthquakes. Nonlinear response history analysis requires rigorous procedure to compute seismic demands. Therefore the simplified nonlinear analysis procedures are useful to determine performance of the structure. In this investigation, application of improved capacity demand diagram method in the control of structural system is presented for the first time. Developed pole assignment method (DPAM) in structural systems control is introduced. Genetic algorithm (GA) is employed as an optimization tool for minimizing a target function that defines values of coefficient matrices providing the placement of actuators and optimal control forces. The ground acceleration is modified under induced control forces. Due to this, performance of structure based on improved nonlinear demand diagram is selected to threshold of nonlinear behavior of structure. With small energy consumption characteristics, semi-active devices are especially attractive solutions for limiting earthquake effects. To illustrate the efficiency of DPAM, a 30-story steel moment frame structure employing the semi-active control devices is applied. In comparison to the widely used linear quadratic regulation (LQR), the DPAM controller was shown to be just as effective and better in the reduction of structural responses during large earthquakes.