Adaptive Robust Weighting Input Estimation Method for the 1-D Inverse Heat Conduction Problem

Journal of the Chinese Institute of Engineers

2008

This study analyzes a load torque estimation problem for an electric machinery servo system using the recursive input estimation (RIE) algorithm. This study also presents four novel estimation models, which have different numbers of sensors, considers the process and the measurement noise to estimate load torque without a torque sensor. The proposed algorithm is a novel estimation technique for solving force feedback and state estimation problems for an electric machinery servo control system. In this work, the DC servomotor system is utilized as an electric machinery servo system, and four important RIE characteristics were verified by numerical simulation results: (i) Adaptive forgetting factor in the RIE algorithm can estimate load torque more effectively than constant forgetting factor; (ii) The significant variation of control input and/or load torque impact affects the estimation precision; (iii) The highperformance load torque estimation model can be established based only on the given control input and techogenerator sensor; (iv) Using different degrees of model errors, the estimation of the performance tendency toward good or bad for control input and load torque are the same. This characteristic shows that good estimated performance for an unknown load torque can be confirmed based on the strength of good control input estimated performance. In other words, model error can be identified via the deterministic control input and its estimation results. The proposed models may have practical applications for solving disturbance compensation problems in electric machinery servo systems.

Section: Recursive Input-estimation Approach (Rie)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Load torque estimation problem for electric machinery servo systems by an inverse method

Chen¹,

Journal of the Chinese Institute of Engineers

2008

“…In this study, the adaptive weighting function is presented. The detailed derivation of this function are given by Tuan 15 , et al…”

Section: Adaptive Weighted Recursive Input Estimation Methodsmentioning

confidence: 99%

“…Tuan 5,6 adopted the input estimation method to inversely solve the 1-D and 2-D heat conduction problems. Ma [7][8][9] and Deng 10 as well used this method to estimate the force input to the structure system.…”

Section: Introductionmentioning

confidence: 99%

Blast Load Input Estimation of the Medium Girder Bridge using Inverse Method

Chen

2008

DSJ

Innovative adaptive weighted input estimation inverse methodology for estimating the unknown time-varying blast loads on the truss structure system is presented. This method is based on the Kalman filter and the recursive least square estimator (RLSE). The filter models the system dynamics in a linear set of state equations. The state equations of the truss structure are constructed using the finite element method. The input blast loads of the truss structure system are inverse estimated from the system responses measured at two distinct nodes. This work presents an efficient weighting factor γ applied in the RLSE, which is capable of providing a reasonable estimation results. The results obtained from the simulations show that the method is effective in estimating input blast loads, so has great stability and precision.

“…It has been successfully adopted to solve the inverse heat conduction problems (IHCPs) [22][23][24], the trajectory estimation of vehicles [25], and the input force estimation of the beam structures [26] in recent years. The on-line inverse methodology using the KF technique and the RLSE can effectively estimate the unknown inputs of the physical system.…”

mentioning

confidence: 99%

Joint control for flexible‐joint robot with input‐estimation approach and LQG method

Chen

Optim Control Appl Methods

2007

In this work, the input-estimation (IE) algorithm and the linear quadratic Gaussian (LQG) controller are adopted to design a control system. The combined method can maintain higher control performance even when the system variation is unknown and under the influence of disturbance input. The IE algorithm is an on-line inverse estimation method involving the Kalman filter (KF) and the least-square method, which can estimate the system input without additional torque sensor, while the LQG control theory has the characteristic of low sensitivity of disturbance. The design and analysis processes of the controller will also be discussed in this paper. The joint control of the flexible-joint robot system is utilized to test and verify the effectiveness of the control performance. According to the simulation results, the IE algorithm is an effective observer for estimating the disturbance torque input, and the LQG controller can effectively cope with the situation that the disturbance exists. Finally, higher control performance of the combined method for joint control of the robotic system can be further verified.to the external force into account. However, the operating environments for many robot manipulator systems become more and more complicated, and the requirements for accurate control performance are getting higher. For example, the recoil acting on the weapon manipulator of the unmanned ground vehicles (UGVs) is dynamic when the weapon is in action. If the disturbance torque input cannot be adequately handled, the system will have difficulties to achieve higher control performance [1]. Moreover, when the auxiliary rehabilitation robot cannot sense the reaction force from the patients and provide feedback to its control system for adaptive adjustments, an adequate reaction will not be made to provide a comfortable rehabilitation process to the patients [2]. In view of the above issues, it is fairly important to improve the ability of the estimation of the disturbance torque and to design a robust control system for the robot manipulator system.In the design of the robotic manipulator system, two methods for solving the force feedback problems are commonly used. One is using the torque sensor to measure the disturbance torque [3], and the other is utilizing an inverse method to estimate the disturbance torque according to the observation of the system states (without torque sensor) [4][5][6][7][8]. Although the torque sensor can measure the disturbance torque accurately, it is very expensive and will extensively increase the cost of the overall system, especially when the machinery system is more complex. Various kinds of controllers without the torque sensors have been proposed in the literature. The disturbance observer [9-13], the adaptive robust control (ARC) [14,15], and the enhanced internal model control (EIMC) [16] are several main examples. Recently, the generalized disturbance compensating framework based on the Lyapunov redesign, named the robust internal loop compensator (RIC), was proposed to show th...