• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.6
• /
• pp.411-416
• /
• 2016

This paper presents the design methodology of an unknown input observer for Lipschitz nonlinear systems with unknown inputs in the framework of convex optimization. We use an unknown input observer (UIO) to consider both nonlinearity and disturbance. By deriving a sufficient condition for exponential stability in the linear matrix inequality (LMI) form, existence of a stabilizing observer gain matrix of UIO will be assured by checking whether the quadratic stability margin of the error dynamics is greater than the Lipschitz constant or not. If quadratic stability margin is less than a Lipschitz constant, the coordinate transformation may be used to reduce the Lipschitz constant in the new coordinates. Furthermore, to reduce the maximum singular value of the observer gain matrix elements, an object function to minimize it will be optimally designed by modifying its magnitude so that amplification of sensor measurement noise is minimized via multi-objective optimization algorithm. The performance of UIO is compared to a nonlinear observer (Luenberger-like) with an application to a flexible joint robot system considering a change of load and disturbance. Finally, it is validated via simulations that the estimated angular position and velocity provide true values even in the presence of unknown inputs.

• Smart Structures and Systems
• /
• v.26 no.6
• /
• pp.693-701
• /
• 2020

The conventional Kalman Filter (KF) provides a promising way for structural state estimation. However, the physical parameters of structural systems or models should be available for the estimation. Moreover, it is not applicable when the loadings applied to the structures are unknown. To circumvent the aforementioned limitations, a two-stage KF with unknown input approach is proposed for the simultaneous identification of structural parameters and unknown loadings. In stage 1, a modified observation equation is employed. The structural state vector is estimated by KF on the basis of structural parameters identified at the previous time-step. Then, the unknown input is identified by Least Squares Estimation (LSE). In stage 2, based on the concept of sensitivity matrix, the structural parameters are updated at the current time-step by using the estimated structural states obtained from stage 1. The effectiveness of the proposed approach is numerically validated via a five-story shearing model under random and earthquake excitations. Shaking table tests on a five-story structure are also employed to demonstrate the performance of the proposed approach. It is demonstrated from numerical and experimental results that the proposed approach can be used for the identification of parameters of structure and the external force applied to it with acceptable accuracy.

• Structural Engineering and Mechanics
• /
• v.34 no.1
• /
• pp.1-14
• /
• 2010

The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthesize the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The capability of this inverse method are demonstrated in the input force estimation cases of the plate structure system. The proposed algorithm is further compared by alternating between the constant and adaptive weighting factors. The results show that this method has the properties of faster convergence in the initial response, better target tracking capability, and more effective noise and measurement bias reduction.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.53.6-53
• /
• 2001

In this paper, we are concerned with a tracking filter algorithm which can track a maneuvering target. Among the novel tracking filter algorithms, the input estimation (IE) filter can be summarized as estimating the unknown maneuver input and compensating the state according to the estimated input, and the variable dimension filter (VDF) can be summarized as detecting the maneuver of target and changing the dimension of the target dynamics to accomodate the maneuver of target They have some goods and bads with respect to each other. The variable dimension filter with input estimation (VDIEF) is constructed by combining the two filtering algorithms. However, it requires too much computational burden while it has good performance. We propose another variable dimension with input estimation ...

• Transactions on Control, Automation and Systems Engineering
• /
• v.4 no.1
• /
• pp.62-74
• /
• 2002

The tracking of a reference temperature trajectory in a polymerization batch reactor is a common problem and has critical importance because the quality control of a batch reactor is usually achieved by implementing the trajectory precisely. In this study, only energy balances around a reactor are considered as a design model for control synthesis, and material balances describing concentration variations of involved components are treated as unknown disturbances, of which the effects appear as time-varying parameters in the design model. For the synthesis of a tracking controller, a method combining the input-output linearization of a time-variant system with the parameter estimation is proposed. The parameter estimation method provides parameter estimates such that the estimated outputs asymptotically follow the measured outputs in a specified way. Since other unknown external disturbances or uncertainties can be lumped into existing parameters or considered as another separate parameters, the method is useful in practices exposed to diverse uncertainties and disturbances, and the designed controller becomes robust. And the design procedure and setting of tuning parameters are simple and clear due to the resulted linear design equations. The performances and the effectiveness of the proposed method are demonstrated via simulation studies.

• Journal of the Korean Society of Safety
• /
• v.18 no.4
• /
• pp.169-175
• /
• 2003

In this paper, and estimation method for estimating the states of underwater vehicle systems with external unknown disturbance is proposed. First, the dynamics of underwater vehicle are induced by Taylor series expansion in the vertical plane and horizontal plane, respectively. For constructing the system model, the external efforts, i.e., the sea surface disturbances, the current, wave and etc., are regarded as external unknown disturbances. Thus the disturbance is added as external input into state-space form of underwater vehicle system. To estimate the state of systems with unknown disturbance, a disturbance observer which does not effected the external unknown input is proposed, and the existence condition for the observer is given. Finally, the effectiveness of the proposed disturbance observer for robust control of underwater vehicle systems is verified by using numerical simulation.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1432-1435
• /
• 1997

This paper proposes a fault detection and isolation metho by combining the parameter estimation method[4] with the observer method[2] to use merits of both methods. To verify the performance of the method proposed some simulations applied to remotely piloted vehicle are performed.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.4
• /
• pp.646-647
• /
• 2005

A recently published paper demonstrates the utility of allowing delays in state estimators for linear systems with unknown inputs. In this note, we point out an error in the derivation of the estimator.

• Smart Structures and Systems
• /
• v.10 no.1
• /
• pp.15-31
• /
• 2012

This study proposes an active control method to suppress beam-rotating machinery system vibrations. The present control method is a combination of the fuzzy input estimation method (FIEM) and linear quadratic Gaussian problem (LQG) algorithms. The FIEM can estimate the unknown input and optimal states by measuring the dynamic displacement, the optimal estimated states into the feedback control; thereby obtaining the optimal control force for a random linear system. Active vibration control of a beam-rotating machinery system is performed to verify the feasibility and effectiveness of the proposed algorithm. The simulation results demonstrate that the proposed method can suppress vibrations in a beam-machine system more efficiently than the conventional LQG method.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.158-163
• /
• 1992

This paper describes the estimation of the solid friction in mechanical systems by using the extended Kalman filtering techniques. We proposed two stochastic model for the estimation. The one is the 'parametric model' which represents the friction characteristics by an exponential function with unknown parameters. The other is the 'blind model' which does not assume an explicit model but regard the effect of the friction as an unknown input to a known dynamic system. For both models, we give estimation algorithms to generate the filtered estimate and the smoothed estimate with a fixed lag. The filtered estimate can be generated on-line for compensating the solid friction in mechanical systems. Although on-line applications are impossible, the smoothed estimate is more accurate and can be used to grasp precise friction characteristics. Simulation and experimental results arc presented to show the effectiveness of the proposed techniques.