• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.7A
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• pp.562-567
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• 2009

In this paper, two-hop beamforming relay system, with source, relay, and destination nodes, is considered and the transmit- and receive-beamforming vectors and the relay processing matrix are designed for minimizing a mean square error (MMSE) between the transmit and receive signals. Here, to reduce the transmit power of the source or the relay, two local inequality constraints are involved with MMSE problem. By adopting the Lagrange method, closed formed Karush-Kuhn-Tucker (KKT) conditions (equalities) are derived and an iterative algorithm is developed to solve the entangled KKT equalities. Due to the inequality power constraints, the source or the relay can reduce its transmit power when the received signal-to-noise ratios (SNRs) of the first- and the second-hop are different. Meanwhile, the destination can achieve almost identical bit-error-rate performance compared to an optimal beamforming system maximizing the received SNR. This claim is supported by a computer simulation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.10-18
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• 2006

We propose a new approach to compute possible acceleration boundary, so is called dynamic manipulability, for multiple robotic systems with frictional contacts between robot end-effectors and object. As the frictional contact condition which requires each contact force to lie within a friction cone is based on the nonlinear inequality formalism is not easy to handle the constraint in manipulability analysis. To include the frictional contact condition into the conventional manipulability analysis we approximate the friction cone to a pyramid which is described by linear inequality constraints. And then achievable acceleration boundaries of manipulated object are calculated conventional linear programming technique under constraints for torque capability of each robot and the approximated contact condition. With the proposed method we find some solution to which conventional approaches did not reach. Also, case studies are Presented to illustrate the correctness of the proposed approach for two robot systems of simple planar robots and PUMA560 robots.

• Journal of Mechanical Science and Technology
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• v.20 no.5
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• pp.612-620
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• 2006

This paper proposes an optimal trajectory generation method for biped robots for walking up-and-down stairs using a Real-Coded Genetic Algorithm (RCGA). The RCGA is most effective in minimizing the total consumption energy of a multi-dof biped robot. Each joint angle trajectory is defined as a 4-th order polynomial of which the coefficients are chromosomes or design variables to approximate the walking gait. Constraints are divided into equalities and inequalities. First, equality constraints consist of initial conditions and repeatability conditions with respect to each joint angle and angular velocity at the start and end of a stride period. Next, inequality constraints include collision prevention conditions of a swing leg, singular prevention conditions, and stability conditions. The effectiveness of the proposed optimal trajectory is shown in computer simulations with a 6-dof biped robot model that consists of seven links in the sagittal plane. The optimal trajectory is more efficient than that generated by the Modified Gravity-Compensated Inverted Pendulum Mode (MGCIPM). And various trajectories generated by the proposed GA method are analyzed from the viewpoint of the consumption energy: walking on even ground, ascending stairs, and descending stairs.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.659-664
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• 2000

Fuel-consumption and catalyst-out emissions of a parallel hybrid electric vehicle are affected by operating region of an engine. In many researches, It is generally known that it is profitable in fuel- consumption to operate engine in OOL(Optimal Operating Line). We established the mathematical model of a parallel hybrid electric vehicle, which is linear time-invariant. To operate an engine in OOL, we applied RHC(Receding Horizon Control) to the driving control of a parallel hybrid electric vehicle. And it is known that the RHC has advantages such as good tracking performance under state and control constraints. This RHC is obtained by using linear matrix inequality (LMI) optimization. In this paper, there are three main topics. First, without state and control constraints, the optimal tracking of OOL was simulated. Second, with state and control constraints by engine and motor performances, the optimal tracking of OOL was simulated. In the last, we studied on the optimal gear ratio. That is to say, we combined the RHC and the iterative simulation to extract the optimal gear ratio. In this simulation, the vehicle is commanded to track the reference vehicle trajectory and the engine is operated in the optimal operating region which is made by the state constraints.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.143-151
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• 2005

This paper considers a robust decentralized $H_\infty$ control problem for uncertain large-scale interconnected systems. The uncertainties are assumed to be time-invariant, norm-bounded, and exist in subsystems. A design method based on the bounded real lemma is developed for a dynamic output feedback controller, which is reduced to a feasibility problem for a nonlinear matrix inequality (NMI). It is proposed to solve the NMI iteratively by the idea of homotopy, where some of the variables are fixed alternately on each iteration to reduce the NMI to a linear matrix inequality (LMI). A decentralized controller for the nominal system is computed first by imposing structural constraints on the coefficient matrices gradually. Then, the decentralized controller is modified again gradually to cope with the uncertainties. A given example shows the efficiency of this method.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.8
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• pp.1212-1219
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• 2009

In this paper, a state feedback gain controller using linear matrix inequality(LMI) for the multi-objective synthesis is designed, in the multi-layer structure with integral type servo system. The design objectives include $H_{\infty}$ performance, asymptotic disturbance rejection, time-domain constraints, on the closed-loop pole location. The results of computer simulation show the validity of the designed controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.60-63
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• 1996

This study deals with the nonlinear H$_{\infty}$ control problem of linear system using nonlinear weight. Generally the solvable condition of nonlinear H$_{\infty}$ control problem is given by the Hamilton Jacobi equality or inequality, but it is very difficult to solve. In this study, some constraints of nonlinear weight reduce the solvable condition to linear Riccati equation. Some examples of the control system design using nonlinear weight are shown.n.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.557-562
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• 2004

This paper deals with a robust mixed ${H_2}/{H_{\infty}}$ filter design problem for a nonlinear dynamic system modeled as a T-S fuzzy system. Integral quadratic constraints are used to describe various kinds of uncertainties of the plant. A sufficient condition for solvability is given in terms of linear matrix inequality problem which can be efficiently solved using a convex optimization technique. In order to demonstrate the Proposed method, a numerical design example is provided.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.155-159
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• 2013

In this paper, a nonlinear matrix inequality problem and a nonlinear optimization problem are proposed for obtaining a structured static output feedback controller. The proposed nonlinear optimization problem has LMI (Linear Matrix Inequality) constraints and a nonlinear objective function. Using the conditional gradient method, the nonlinear optimization problem can be solved. A numerical example shows the effectiveness of the proposed approach.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1255-1263
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• 2015

In the previous work, the authors studied the problem of robust discretization of linear time-invariant systems with polytopic uncertainties, where linear matrix inequality (LMI) conditions were developed to find an approximate discrete-time (DT) model of a continuous-time (CT) system with uncertainties in polytopic domain. The system matrices of obtained DT model preserved the polytopic structures of the original CT system. In this paper, we extend the previous approach to solve the problem of robust discretization of polytopic uncertain systems with aperiodic sampling. In contrast with the previous work, the sampling period is assumed to be unknown, time-varying, but contained within a known interval. The solution procedures are presented in terms of unidimensional optimizations subject to LMI constraints which are numerically tractable via LMI solvers. Finally, an example is given to show the validity of the proposed techniques.