• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.486-490
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• 1993

In addtion to a basic motion task, redundant manipulators can achieve some additional tasks by optimizing proper performance criteria. Some of performance criteria can be transformed to inequality constraints. So the redundancy resolving problem can be reformulated as a local optimization problem with equality constraints for the end effector and inequality constraints for some performance criteria. In this article, we propose a method for solving the inverse kinematics of a manipulator with redundancy using the Kuhn-Tucker theorem to incorporate inequality constraints. With proper choice of inequality constraints, the proposed method gives a way of optimizing multiple criteria in redundant manipulators.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.69-72
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• 1996

This paper considers a global resolution of kinematic redundancy under inequality constraints as a constrained optimal control. In this formulation, joint limits and obstacles are regarded as state variable inequality constraints, and joint velocity limits as control variable inequality constraints. Necessary and sufficient conditions are derived by using Pontryagin's minimum principle and penalty function method. These conditions leads to a two-point boundary-value problem (TPBVP) with natural, periodic and inequality boundary conditions. In order to solve the TPBVP and to find a global minimum, a numerical algorithm, named two-stage algorithm, is presented. Given initial joint pose, the first stage finds the optimal joint trajectory and its corresponding minimum performance cost. The second stage searches for the optimal initial joint pose with globally minimum cost in the self-motion manifold. The effectiveness of the proposed algorithm is demonstrated through a simulation with a 3-dof planar redundant manipulator.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.777-780
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• 1995

Accounting for actuator nonlinearities in control loops has often been perceived as an implementation issue and usually excluded in the design of controllers. Nonlinearities treated in this paper are saturation, and they are modelled as an inequality constraint. The CRHPC(Constrained Receding Horizon Predictive Control) with inequality constraints algorithm is used to handle actuator rate and amplitude limits simultaneously or respectively. Optimum values of future control signals are obtained by quadratic programming. Simulated examples show that predictive control law with inequality constraints offers good performance as compared with input clipping.

• Kyungpook Mathematical Journal
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• v.58 no.2
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• pp.333-346
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• 2018

An approximation scheme utilizing Bezier curves is considered for solving time-delayed optimal control problems with terminal inequality constraints. First, the problem is transformed, using a $P{\acute{a}}de$ approximation, to one without a time-delayed argument. Terminal inequality constraints, if they exist, are converted to equality constraints. A computational method based on Bezier curves in the time domain is then proposed for solving the obtained non-delay optimal control problem. Numerical examples are introduced to verify the efficiency and accuracy of the proposed technique. The findings demonstrate that the proposed method is accurate and easy to implement.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.11 s.104
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• pp.1255-1261
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• 2005

The dynamic contact model of a beam that contacts to a rigid wall in a reactor core was studied. The gap between the beam and contact wall results in dynamic contact accompanying inequality constraints. The inequality constraints can be relieved to an equality constraint problem by introducing a convex Penalty function. In this work, a beam with contact condition is formulated using quasi-convex penalty function and numerically solved. General coordinate solution is adopted to raise computational efficiency. Also nonlinearity is examined In the beam contacting to a rigid wall.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.566-569
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• 2009

In this paper, we propose a less conservative a linear matrix inequality (LMI) condition for the constrained robust model predictive control of systems with input constraints and polytopic uncertainty. Systems with input constraints are represented as perturbed systems with sector bounded conditions. For the infinite horizon control, closed-loop stability conditions are obtained by using a parameter dependent Lyapunov function. The effectiveness of the proposed method is shown by an example.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1132-1138
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• 2012

The MPC (Model Predictive Control) is one of the techniques that can be used to control an unmanned vehicle. It predicts the future vehicle trajectory using the dynamic characteristic of the vehicle and generate the control value to track the reference path. If some obstacles are detected on the reference paths, the MPC can generate control value to avoid the obstacles imposing the inequality constraints on the MPC cost function. In this paper, we propose an obstacle modeling algorithm for MPC with inequality constraints for obstacle avoidance and a method to set selective constraint on the MPC for stable obstacle avoidance. Simulations with the field test data show successful obstacle avoidance and way point tracking performance.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.29-32
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• 2001

This paper presents matrix inequality conditions for H$_2$optimal control of linear time-invariant descriptor systems in continuous and discrete time cases, respectively. First, the necessary and sufficient condition for H$_2$control and H$_2$controller design method are expressed in terms of LMls(linear matrix inequalities) with no equality constraints in continuous time case. Next, the sufficient condition for H$_2$control and H$_2$controller design method are proposed by matrix inequality approach in discrete time case. A numerical example is given in each case.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.460-467
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• 2018

Metaheuristic optimization approach has become the new framework for control synthesis. The main purposes of the control design are command (input) tracking and load (disturbance) regulating. This article proposes an optimal proportional-integral-derivative (PID) controller design for the DC motor speed control system with tracking and regulating constrained optimization by using the cuckoo search (CS), one of the most efficient population-based metaheuristic optimization techniques. The sum-squared error between the referent input and the controlled output is set as the objective function to be minimized. The rise time, the maximum overshoot, settling time and steady-state error are set as inequality constraints for tracking purpose, while the regulating time and the maximum overshoot of load regulation are set as inequality constraints for regulating purpose. Results obtained by the CS will be compared with those obtained by the conventional design method named Ziegler-Nichols (Z-N) tuning rules. From simulation results, it was found that the Z-N provides an impractical PID controller with very high gains, whereas the CS gives an optimal PID controller for DC motor speed control system satisfying the preset tracking and regulating constraints. In addition, the simulation results are confirmed by the experimental ones from the DC motor speed control system developed by analog technology.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.515-515
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• 2000

The performance of dynamic path following of a wheeled mobile robot with nonholonomic constraints has some drawbacks such as the influence of the initial state. The drawbacks can be overcome by the temporary path generator and modified output. But with the previous input-output linearization method using them, it is difficult to tune the gains, and if there are some modeling errors, the low gain can make the system unstable. And if a high gain is used to overcome the model uncertainties, the control inputs are apt to be large so the system can be unstable. In this paper. an H$_{\infty}$ controller is designed to guarantee robustness to model parameter uncertainties and to consider the magnitude of control inputs. And the solution to Hamilton Jacobi (HJ) inequality, which is essential to H$_{\infty}$ control design, is obtained by nonlinear matrix inequality (NLMI).