• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.49 no.2
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• pp.153-160
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• 2013

Control allocation is an important part of a system. It implements the function that map the desired command forces from the controller into the commands of the different actuators. In this paper, the authors present an approach for solving constrained control allocation problem in vessel system by using multi-parametric quadratic programming (mp-QP) algorithm. The goal of mp-QP algorithm applied in this study is to compute a solution to minimize a quadratic performance index subject to linear equality and inequality constraints. The solution can be pre-computed off-line in the explicit form of a piecewise linear (PWL) function of the generalized forces and constrains. The efficiency of mp-QP approach is evaluated through a dynamic positioning simulation for a vessel by using four tugboats with constraints about limited pushing forces and found to work well.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.4
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• pp.145-154
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• 1983

This paper presents a new method for optimal real and reactive power dispatch for the economic operation of a power system. Unlike the usual approach of minimizing the transmission loss, this method minimizes the total production cost not only for the real power optimization problem, but also for the reactive power optimization. The control variables are real power generation of units for real power optimization, and reactive power optimization. The constraints are the operating limits on these control variables and the limits on the bus voltages. Methematical models are developed to represent the sensitivity relationships between dependent and control variables for both real and reactive power optimization modules, and thus eliminate the use of B-coefficients. In order to handle many functional inequality constraints, a modified version of the gradient projection method is developed for optimization procedure, and has shown a remarkable advantage in computation efficiency.

• Journal of Navigation and Port Research
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• v.47 no.2
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• pp.66-74
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• 2023

With the increase of interest in developing Maritime Autonomous Surface Ships (MASS), an optimal ship route planning is gradually gaining popularity as one of the important subsystems for autonomy of modern marine vessels. In the present paper, an optimal ship route planning model for MASS is proposed using a nonlinear MPC approach together with a nonlinear MMG model. Results drawn from this study demonstrated that the optimization problem for the ship route was successfully solved with satisfaction of the nonlinear dynamics of the ship and all constraints for the state and manipulated variables using the nonlinear MPC approach. Given that a route generation system capable of accounting for nonlinear dynamics of the ship and equality/inequality constraints is essential for achieving fully autonomous navigation at sea, it is expected that this paper will contribute to the field of autonomous vehicles by demonstrating the performance of the proposed optimal ship route planning model.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.581-589
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• 1998

An algorithm is proposed for the $L_1$-estimation with linear equality and inequality constraints in linear regression model. The algorithm employs a linear scaling transformation to obtain the optimal solution of linear programming type problem. And a special scheme is used to maintain the feasibility of the updated solution at each iteration. The convergence of the proposed algorithm is proved. In addition, the updating and orthogonal decomposition techniques are employed to improve the computational efficiency and numerical stability.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.233-236
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• 2015

In this paper, a nonlinear optimization problem is proposed to obtain a structured static output feedback controller for discrete time linear systems. The proposed optimization problem has LMI (Linear Matrix Inequality) constraints and a non-convex objective function. Using the conditional gradient method, we can obtain suboptimal solutions of the proposed optimization problem. Numerical examples show the effectives of the proposed approach.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.13 no.2
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• pp.101-108
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• 2009

In this paper, we introduce generalized multiobjective fractional programming problem with two kinds of inequality constraints. Kuhn-Tucker sufficient and necessary optimality conditions are given. We formulate a generalized multiobjective dual problem and establish weak and strong duality theorems for an efficient solution under generalized convexity conditions.

• Journal of applied mathematics & informatics
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• v.7 no.3
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• pp.719-738
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• 2000

A dual algorithm based on the smooth function proposed by Polyak (1988) is constructed for solving nonlinear programming problems with inequality constraints. It generates a sequence of points converging locally to a Kuhn-Tucker point by solving an unconstrained minimizer of a smooth potential function with a parameter. We study the relationship between eigenvalues of the Hessian of this smooth potential function and the parameter, which is useful for analyzing the effectiveness of the dual algorithm.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.5
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• pp.801-810
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• 2004

The Primal-Dual Interior-Point (PDIP) method is currently one of the fastest emerging topics in optimization. This method has become an effective solution algorithm for large scale nonlinear optimization problems. such as the electric Optimal Power Flow (OPF) and natural gas and electricity OPF. This study describes major theoretical developments of the PDIP method as well as practical issues related to implementation of the method. A simple quadratic problem with linear equality and inequality constraints

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.29-40
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• 2000

Process planning (selection and ordering of processes) and material selection for product manufacturing are two key things determined before taking full-scale manufacturing. Knowledge on product design. material characteristics, processes, time and cost all-together are mutually related and should be considered concurrently. Due to the complexity of problem, human experts have got only one of the feasilbe solutions with their field knowledge and experiences. We propose a hierarchical expert system framework of knowledge representation and reasoning in order to overcome the complexity. Manufacturing processes have inherently hierarchical relationships, from top level processes to bottom level operation processes. Process plan of one level is posted in process blackboard and used for lower level process planning. Process information on blackboard is also used to adjust the process plan in order to resolve the dead-end or inconsistency situation during reasoning. Decision variables for process, material, tool, time and cost are represented as object frames, and their relationships are represented as constraints and rules. Constraints are for relationship among variables such as compatibility, numerical inequality etc. Rules are for causal relationships among variables to reflect human expert\`s knowledge such as process precedence. CRSP(Constraint and Rule Satisfaction Problem) approach is adopted in order to obtain solution to satisfy both constraints and rules. The trade-off procedure gives user chances to see the impact of change of important variables such as material, cost, time and helps to determine the preferred solution. We developed the prototype system using visual C++ MFC, UNIK, and UNlK-CRSP on PC.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.9
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• pp.609-615
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• 1987

The optimal operation of power system is developed by alternately using real power dispatch and reactive power dispatch problem. The real power system scheduling process is formulated as an optimization problem with linear inequality constraints. A.C. loadflow method is used for the problem solution and line losses are considered. The constraints under consideration are generator power limits, load scehdling limits and line capacity limits. In solving the objective function the Dual Relaxation method is adopted. Tests indicate that the method is practical for real time application. The reactive power control problem uses the Dual Simplex Relaxation method as in the real scheduling case. Insted of minimizing the cost of power system, the objective is selected as to determine the highest possible voltage schedule. The constraints under consideration are the voltage limits at each node and the possibilities of supply or absobtion of reactive energy by generator units and the compensation facilities. Tests indicate that the method is practical for real time applications. The overall optimization methods developed in this paper proved to obtained fine results in minimizing object function compared with the method without using voltage control. And the overall voltage profiles were also improved.