Papers by Keyword: System Identification

Abstract: In this paper, the author took an unmanned planting boat as the object of study and conducted a series of roll decay test on condition that the ship model was in different drafts and tilt angle. The author established eight kinds of mathematical model of roll decay motion model system identification by making a cross combination of linear or nonlinear righting moment and linear or nonlinear damping. Based on the principle of system identification, the author established the optimization calculation of the objective function. Then the author adapted the genetic algorithm of system identification program based on Visual Basic 6.0 and got eight kinds of identification programs. By identifying respectively the test data of the roll motion of the unmanned planting boat, the author confirmed the feasibility of the adapted program. Comparing large drafts and tilt angle identification results, the author found a reasonable hydrostatic roll motion equation of the unmanned planing boat in the case of large drafts and tilt angle, and made a preliminary analysis.

Abstract: This paper is the result of a research program which focused on the statistical dynamics of vehicles. Most of the inputs of man-machine-field system have a random variation, so a systemic and statistical analysis of vehicle dynamics is obvious. In our study, data were obtained by measuring the dynamic parameters of vehicles and engines. Testing program aimed to capture a large range of operating regimes. To analyze the data the authors have used neural networks. There was adopted a NNARX (Neural Network Auto-Regressive with eXogene inputs) model with 4 inputs, 5 hidden units and 1 output. It can be concluded that the development of mathematical modeling using non-linear neural network can ensure the desired accuracy, conveniently is obtained by increasing the number of neurons in the hidden laws.

Abstract: Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

Abstract: An identification method of a class of second-order continuous system is proposed. This method constructs a discrete-time identification model, forms a set of linear equations. The parameters can be obtained by least square method. Simulation results show that the method is effective for a class of second-order system, and is not only for step response but also for square wave signal.

Abstract: In order to quickly determine the performance of a transient voltage suppressor (TVS), improve time domain identification capability of Elman network, the simulation of electromagnetic pulse (EMP) inject effects based on improved Elman network is proposed. Derivation proved that improved Elman network trained by standard BP algorithm has a similar form with the basic Elman network trained dynamic BP algorithm. We establish and improve its Elman network predictive modeling based on the measured parameters of TVS and then demonstrate that improved Elman network has the characteristics of quick speed, high precision, good performance and strong generalization ability, and broad use of prospects.

Abstract: In order to control an unmanned helicopter accurately and reliably, it is necessary to have a precise mathematical model of its dynamics. This paper presents a new timedomain identification method and process for full state space model of small-scale unmanned helicopters. The identification method is called ISAcwPEM (Improved Simulated Annealing combined with Prediction Error Method), which is not sensitive to initial point selection and doesn’t require frequency-sweeping inputs. Firstly, the primary parameters to be identified are selected by model sensitivity analysis. After that, the improved simulated annealing algorithm runs in a distributed computing platform to figure out a 13-order state space model of the SJTU T-REX700E small-scale unmanned helicopter (consisting of a cruise modal and a hover modal). Then the iterative Prediction Error Method (PEM) is used to optimize the model. In addition, the time-delay term and the trim term are estimated and added to the model. Finally, the effectiveness of the identification method is well validated by real outdoor flight experimental results.

Abstract: Even though actual composition of engine exhaust gases varies across diverse types of engines, such as gasoline, diesel, gas turbine and natural gas engines, engine exhaust temperature is always a major factor with strong impact on emission levels and catalytic converting efficiency. For spark ignition engines, exhaust temperature depends on various engine parameters, such as engine speed, engine load, A/F ratio, intake air temperature, coolant temperature and spark timing, etc. Due to complexity, it is impossible to share a unique analytical model of engine exhaust temperature. Instead, it is mostly modeled as a complicated nonlinear system. The model complexity increases significantly however accuracy cannot be guaranteed. On the other hand, a simple linear model with accurate system identification could serve as a versatile alternative to represent the engine exhaust temperature, while engine parameters are subject to model identification to be adaptable across different types of engines. Combination of linear functions in terms of dominant engine parameters of engine speed and engine load is used for exhaust temperature modeling. To identify optimal parameters, Markov Chain Monte Carlo (MCMC) is applied. The discrete-time Markov chain is introduced where the stationary probability replaces posterior density in Monte Carlo integration for numerical integration. Compared with the high order nonlinear approaches, low computation cost is involved in the simplified model. Good agreement between the model prediction data and testing results is observed. The approach could be easily extended to other types of engines.

Abstract: The control parameter of shaking table is one of the key elements which significant influence on the system control performance. An algorithm which uses the model of system identification to theoretically calculate the control parameter is put forward, and then obtains the optimal three-parametric control parameter based on theoretical calculation. Tuning algorithm can get more ideal control parameter and could be applied in shaking table TVC parameter self-tuning.

Abstract: The wide bandwidth and high amplitude feature of the beating heart motion makes surgeon hard to achieve the coronary artery bypass graph surgery on his or her own. The robot could help to cancel the relative motion between the end effecter of the robotic tool and certain point on the heart surface. Therefore a stationary operation screen could be used for the surgeon. The precise robot modeling is the prerequisite for tracking control algorithm. In the paper, we process the input and output data by using the nonlinear optimization method to obtain the system model. Next, we discuss the system identification related problems and modify the model. Finally, a good system model is achieved.

Abstract: This paper discusses parameter estimations of step response from biodiesel transesterification process. The estimates signal generated from impedance measurement from sunflower oil transesterification process. 15 kHz AC signal has used to characterize the capacitance value during the process. The recorded signal then processed using Matlab system identification toolbox. The parametric identification method is selected to develop model structure. The model structure has modelled based on three different structures, ARMAX, BJ and ARX. The algorithm generated from identification toolbox as well as validation test. The validation test including correlation and cross correlation test. ARMAX structure appeared to be best between the others structure.