Jongchul Jung

A Vehicle Roll-Stability Indicator Incorporating Roll-Center Movements

In the development of active-passive roll control systems, a vehicle model that can represent realistic roll behavior is essential for predicting the impending rollover and for accurately applying the control force to avoid vehicle rollover. The vehicle roll center is a key parameter that influences the vehicle roll dynamics. Since the roll center movement becomes important as the vehicle roll angle increases, it is desirable to include this effect in the roll control system. This paper proposes a dynamic roll stability indicator (RSI) incorporating roll center movement that generates rollover threshold in terms of lateral acceleration. A robust parameter identification algorithm using a disturbance observer is designed to estimate the lateral and vertical roll center movements. These estimates are later used in the RSI to update the rollover threshold. The effectiveness of the proposed method is demonstrated through simulations, and its performance is compared with other rollover warning algorithms.

Optimal Proportional-Integral Adaptive Observer Design for a Class of Uncertain Nonlinear Systems

Proportional adaptive observers, which have only a proportional feedback loop of the output estimation error, may suffer large estimation errors due to disturbances. To improve steady-state estimation performance, this paper presents a proportional-integral adaptive observer for a class of uncertain nonlinear systems, which includes both a proportional feedback loop and an integral feedback loop of the output observation error. The additional integral loop improves steady-state estimation performance and robustness against disturbances. The proportional and the integral observer gains are optimally chosen by solving the L2 gain minimization problem, which leads to the minimal effect of disturbances on the estimation error. The effectiveness of the proposed adaptive observer is demonstrated through a numerical example. This new design approach on a proportional-integral adaptive observer can provide not only better estimation performance, but also a straightforward way to choose optimal observer gains.

Optimal robust adaptive observer design for a class of nonlinear systems via an H-infinity approach

Existing adaptive observers may suffer parameter estimate drift due to disturbances even if state estimation errors remain small. To avoid such drift in the presence of bounded disturbances, several robust adaptive observers have been introduced providing bounds in state and parameter estimates. However, it is not easy for these observers to manipulate the size of the bounds with the selection of the observer gain. To reduce estimation errors, this paper introduces the H-infinity norm minimization problem in the adaptive observer structure, which minimizes the H-infinity norm between disturbances and estimation errors. The stability condition of the adaptive observer is reformulated as a linear matrix inequality, and the observer gain is optimally chosen by solving the resulting convex optimization problem. The estimation performance is demonstrated through a numerical example

Observer design methodology for stochastic and deterministic robustness

A unified observer with stochastic and deterministic robustness is developed in this paper so that an observer is less sensitive to both stochastic and deterministic uncertainties. For stochastic robustness, the norm of the observer gain and the lower bound of the observer decay rate are shown to be design factors which can minimize the upper bound of the estimation error variance. For deterministic robustness, the L 2 norm-based condition number of the observer eigenvector matrix is utilized to address robust estimation performance against deterministic uncertainties. In order to justify the proposed method, a graphical approach is first introduced, and then a multi-objective optimization problem including linear matrix inequality constraints is formulated to provide the unified robustness.

Metamodel-Based Shape Optimization of Connecting Rod Considering Fatigue Life

To optimize a connecting rod satisfying fatigue life, metamodel-based design optimization is proposed. To approximately predict both volume and fatigue life of connecting rod, kriging metamodel is constructed based on maximin eigenvalue sampling. Fatigue analysis is accomplished for the calculation of fatigue life. The results of metamodel-based design optimization are compared with those of classical optimization. The advantages of metamodel-based optimization are discussed.

Dissipative Proportional Integral Observer for a Class of Uncertain Nonlinear Systems

This paper proposes a dissipative proportional integral state observer for a class of uncertain nonlinear systems with sector-bounded nonlinearity. The L 2 gain between disturbances and weighted estimation errors is adopted as the supply rate in the dissipativity framework, which guarantees the boundedness of state estimation errors. This estimator also includes an integral feedback loop on the output estimation error to improve steady-state estimation performance. In order to determine the optimal observer gains, the L 2 gain minimization problem is formulated into a convex optimization problem subject to a linear matrix inequality. In addition, the concept of the exponential dissipativity is introduced to address the strict dissipativity in the observer structure.

Discrete-Time Well-Conditioned State Observer Design and Evaluation

Model-based monitoring systems based on state observer theory often have poor performance with respect to accuracy, bandwidth, reliability (false alarms), and robustness. The above limitations are closely related to the ill-conditioning factors such as transient characteristics due to unknown initial values and round-off errors, and steady-state accuracy due to plant perturbations and sensor bias. In this paper, by minimizing the effects of the ill-conditioning factors, a well-conditioned observer is proposed for the discrete-time systems. A performance index is determined to represent the quantitative effects of the ill-conditioning factors and two design methods are described for the well-conditioned observers. The estimation performance of the well-conditioned observers is verified in simulations where transient as well as steady-state error robustness to perturbations is shown to be better than or equal to Kalman filter performance depending on the nature of modeling errors. The estimation performance is also demonstrated on an experimental setup designed and built for this purpose.

Roll stability indicator incorporating roll center movement

Predicting impending vehicle rollover is essential for rollover prevention systems but it is not a simple task. In describing roll motion, the roll center movement becomes important as the vehicle roll angle increases, and thus affects the performance of rollover warning devices. This paper proposes a dynamic roll stability indicator incorporating roll center movement. A robust parameter identification algorithm is designed to estimate the horizontal and vertical movement of the roll center. This estimate is used in the roll stability indicator to update its rollover threshold value. The effectiveness of the proposed roll stability indicator is demonstrated through simulations.Copyright

Robust Kalman Filter Design via Selecting Performance Indices

In this paper, a robust stationary Kalman filter is designed by minimizing selected performance indices so that it is less sensitive to uncertainties. The uncertainties include not only stochastic factors such as process noise and measurement noise, but also deterministic factors such as unknown initial estimation error, modeling error and sensing bias. To reduce the effect on the uncertainties, three performance indices that should be minimized are selected based on the quantitative error analysis to both the deterministic and the stochastic uncertainties. The selected indices are the size of the observer gain, the condition number of the observer matrix, and the estimation error variance. The observer gain is obtained by optimally solving the multi-objectives optimization problem that minimizes the indices. The robustness of the proposed filter is demonstrated through the comparison with the standard Kalman filter.

Design Sensitivity Analysis and Optimization of Nonlinear Anisotropic Structures

Nonlinear analysis of anisotropic structures is described by using Hills yield criterion that anisotropic yield contour is assumed to be retained its shape during the hardening process. Nonlinear constitutive equation of anisotropic material is derived using plastic potential concept. Linear strain hardening model is utilized and forward Euler method is employed as a stress integration method. Newton-Raphson method is implemented for numerical nonlinear analysis. Direct differentiation method differentiating directly the equilibrium equation with respect to design variables is applied to design sensitivity analysis of nonlinear anisotropic plate. The results of design sensitivity analysis are compared with those of finite difference method to verify the accuracy. Optimization is accomplished for a rectangular plate using evaluated sensitivity coefficients.