• Proceedings of the KSME Conference
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• 2001.11a
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• pp.723-729
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• 2001

Electro-hydraulic shift control of a vehicle automatic transmission has been predominantly carried out via an open-loop control based on numerous time-consuming calibrations. Despite remarkable success in practice, the variations of system characteristics inevitably deteriorate the performance of the tuned open-loop controller. As a result, the controller parameters need to be continuously updated in order to maintain satisfactory shift quality. This paper presents a self-learning algorithm for automatic transmission shift control in a construction vehicle during inertia phase. First, an observer reconstructs the turbine acceleration signal (impossible to measure in a construction vehicle) from the readily accessible turbine speed measurement. Then, a control algorithm based on a quadratic function of the turbine acceleration is shown to guarantee the asymptotic convergence (within a specified target bound) of the error between the actual and the desired turbine accelerations. A Lyapunov argument plays a crucial role in deriving adaptive laws for control parameters. The simulation and hardware-in-the-loop simulation (HILS) studies show that the proposed algorithm actually delivers the promise of satisfactory performance despite the system characteristics variations and uncertainties.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.3
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• pp.73-82
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• 1992

An industrial robot, installed real manufacturing processes an element of the system autmation, can be considered as an uncertain system due to dynamic uncertainties in inertial parameters and varying payloads. Most difficuties in controlling a robot manipulator are caused by the fact that the dynamic equations describing the motions of the manipulator are inherently nonlinear and heavily coupled effects between joints and associated links. Existing robot conrol systems have constant predefined gains and do not cover the complex dynamic interactions between manipulator joints. As a result, the manipulator is severly limited in range of application, speed of operation and variation of payload. The proposed controller is operated by adjusting its gains based on the response of the manipulator in such a way that the manipulator closely matches the reference model trajectories defined by the desinger. The proposed manipulator studied has two loops, an inner loop of model reference adaptive controller and an outer loop of state feedback controller with integral action to guarantee the stability of the adaptive scheme. This adaptation algorithm is based on the hyperstailiy approach with an improved Lyapunov function. The coupling among joints and the nonlinearity in the dynamic equation are explicitly considered. The designed manipulator controller shows good tracking performance in practical working environment, various load variations and parameter uncertainties.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.1 s.256
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• pp.18-25
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• 2007

Linear motor is easily affected by load disturbance, force ripple, friction, and parameter variations because there is no mechanical transmission to reduce the effects of model uncertainties and external disturbance. These nonlinear effects have been reduced for high-speed/high-accuracy position control either through the better motor design or via the better control algorithm that can compensate the nonlinear effects. In this paper, a nonlinear adaptive control algorithm is designed and applied for the position control of permanent magnet linear synchronous motor. In order to estimate and compensate the nonlinear effects such as friction and force ripple, the estimation and the nonlinear adaptive control laws are derived based on the virtual control input and a suitable Lyapunov function. The proposed controller is evaluated through the computer simulations. The control algorithm is also implemented to a DSP board and interfaced to the PMLSM for verifying the realtime control performance.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.6-11
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• 2014

Repetitive control is a specialized control scheme to track and/or attenuate a periodic reference trajectory and/or disturbance. Most researches about repetitive control have been performed in the frequency domain. Recently, several approaches to deal with repetitive control systems in the state space are developed by representing a q filter as a state-space equation. This paper presents a design method of a repetitive control system in the state space to satisfy $H_{\infty}$ performance. The overall system is composed of a plant, a repetitive controller, and a state-feedback controller, which can be converted to a standard form used in $H_{\infty}$ control. A LMI (Linear Matrix Inequality)-based stability condition is derived for fixed state-feedback gains. Under a given q filter, another LMI condition is derived to improve $H_{\infty}$ performance and is employed to find state-feedback gains by solving an optimization problem. Finally, to verify the feasibility of the proposed method, a numerical example is demonstrated.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.4
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• pp.1-6
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• 2009

In this paper, we design a dynamic state feedback smooth stabilizer for a nonlinear system whose Jacobian linearization may have uncontrollable mode because its eigenvalues are on the right half-plane. After designing an augmented system, a dynamic exponent scaling and backstepping enable one to explicitly design a smooth stabilizer and a continuously differentiable Lyapunov function which is positive definite and proper. The convergence of the designed controller is proved by the new notion 'degree indicator'.

• International Journal of Precision Engineering and Manufacturing
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• v.1 no.2
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• pp.24-34
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• 2000

In this paper we propose a controller design method, called Quasi-LQG/$H_{\infty}$/LTR for nonlinear servo systems with hard nonlinearities such as Coulomb friction, dead-zone. Introducing the RIDF method to model Coulomb friction and dead-zone, the statistically linearized system is built. Then, we consider $H_{\infty}$ performance constraint for the optimization of statistically linearized systems, by replacing a covariance Lyapunov equation into a modified Riccati equation of which solution leads to an upper bound of the LQG performance. As a result, the nonlinear correction term is included in coupled Riccati equation, which is generally very difficult to thave a numerical solution. To solve this problem, we use the modified loop shaping technique and show some analytic proofs on LTR condition. Finally, the Quasi-LQG/$H_{\infty}$/LTR controller for a nonlinear system is synthesized by inverse random input describing function techniques (ITIDF). It is shown that the proposed design method has a better performance robustness to the hard nonlinearity than LQG/$H_{\infty}$/LTR method via simulations and experiments for the timing-belt driving servo system that contains the Coulomb friction and dead-zone.

• Journal of Ocean Engineering and Technology
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• v.16 no.1
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• pp.8-15
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• 2002

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamic, and the basis function vector of network is constructed according to th AUV's physical properties. A sliding mode control scheme is introduced to attenuate the effect of the neural network's reconstruction errors and the disturbances in AUV's dynamics. Using Lyapunov theory, the stability of the presented control system is guaranteed as well as the uniformly boundedness of tracking errors and neural network's weights estimation errors. Finally, numerical simulations for motion control of an AUV are performed to illustrate the effectiveness of the proposed techniques.

• Journal of Ocean Engineering and Technology
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• v.11 no.4
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• pp.169-179
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• 1997

This paper presents a discrete-time sliding mode control of an autonomous underwater vehicle with parameter uncertainties and long sample interval based on discrete variable structure system. Although conventional sliding mode montrol techniques are robust to system uncertainties, in the case of the system with long sample interval, the sliding control system reveals chattering phenomenon and even makes the system unstable. This paper considers the AUV which acquires position informations from a surface ship through an acoustic telemetry system with a certain discrete interval. The control system is designed on the basis of a Lyapunov function and a sufficient condition of the switching gain to make the system stable is give. Each component of the switching gain can be determined separately one another. The controller is robust to the uncertainties, and reaching condition of the control system is satisfied for any initial condition. This control law is a generalized form of the discrete sliding mode control and reduce the chattering problem considerably. Motion control of the AUV in the vertical plane shows the effectiveness of the proposed technique.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.697-702
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• 2016

To control permanent magnet synchronous motors smoothly, it is important to know the exact parameter values of the stator resistance, various inductances, and the flux linkage of the permanent magnet. In practice, these parameters vary due to a variable operating point, temperature change, or a fault. This paper proposes a MRAS (Model Reference Adaptive System) based parameter estimator and adaptive control scheme. Owing to the non-linearity of the system equation with respect to these parameters, although many schemes proposed previously assumed that some parameters are known, all the parameters were assumed to be unknown. The simulation results revealed the effectiveness of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.50-55
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• 2016

This paper discusses a sampled-data controller design problem for nonlinear systems including singular perturbation. The concerned system is assumed to be modeled in Takagi--Sugeno (T--S) form. By introducing a novel Lyapunov function and an identity equation, the stability of the sampled-data closed-loop dynamics of the singularly perturbed T--S fuzzy system is analyzed. The design condition is represented in terms of linear matrix inequalities. A few discussions on the development are made that propose future research topics. Numerical simulation shows the effectiveness of the proposed method.