Adaptive inverse control of random vibration based on the filtered-X LMS algorithm

Random vibration control is aimed at reproducing the power spectral density （PSD） at specified control points. The classical frequency-spectrum equalization algorithm needs to compute the average of the multiple frequency response functions （FRFs）, which lengthens the control loop time in the equalization process. Likewise, the feedback control algorithm has a very slow convergence rate due to the small value of the feedback gain parameter to ensure stability of the system. To overcome these limitations, an adaptive inverse control of random vibrations based on the filtered-X least mean-square （LMS） algorithm is proposed. Furthermore, according to the description and iteration characteristics of random vibration tests in the frequency domain, the frequency domain LMS algorithm is adopted to refine the inverse characteristics of the FRF instead of the traditional time domain LMS algorithm. This inverse characteristic, which is called the impedance function of the system under control, is used to update the drive PSD directly. The test results indicated that in addition to successfully avoiding the instability problem that occurs during the iteration process, the adaptive control strategy minimizes the amount of time needed to obtain a short control loop and achieve equalization.

Adaptive Pseudo Inverse Control for a Class of Nonlinear Asymmetric and Saturated Nonlinear Hysteretic Systems

This paper aims at eliminating the asymmetric and saturated hysteresis nonlinearities by designing hysteresis pseudo inverse compensator and robust adaptive dynamic surface control(DSC)scheme.The"pseudo inverse"means that an on-line calculation mechanism of approximate control signal is developed by applying a searching method to the designed temporary control signal where the true control signal is included.The main contributions are summarized as:1)to our best knowledge,it is the first time to compensate the asymmetric and saturated hysteresis by using hysteresis pseudo inverse compensator because the construction of the true saturated-type hysteresis inverse model is very difficult;2)by designing the saturated-type hysteresis pseudo inverse compensator,the construction of true explicit hysteresis inverse and the identifications of its corresponding unknown parameters are not required when dealing with the saturated-type hysteresis;3)by combining DSC technique with the tracking error transformed function,the"explosion of complexity"problem in backstepping method is overcome and the prespecified tracking performance is achieved.Analysis of stability and experimental results on the hardware-inloop platform illustrate the effectiveness of the proposed adaptive pseudo inverse control scheme.

Magnetotelluric extremum boundary inversion based on different stabilizers and its application in a high radioactive waste repository site selection

Geophysical inversion under different stabilizers has different descriptions of the target body boundary,especially in complex geological structures.In this paper,we present an extremum boundary inversion algorithm based on different stabilizers for electrical interface recognition.Firstly,we use the smoothest and minimum-support stabilizing functional to study the applicability of adaptive regularization inversion algorithm.Then,an electrical interface recognition method based on different stabilizers is developed by introducing extremum boundary inversion algorithm.The testing shows that the adaptive regularization inversion method does work for different stabilizers and has a low dependence on the initial models.The ratio of the smooth and focusing upper and lower boundaries obtained using the extremum boundary inversion algorithm can clearly demarcate electrical interfaces.We apply the inversion algorithm to the magnetotelluric(MT)data collected from a preselected area of a high-level-waste clay-rock repository site in the Tamusu area.We recognized regional structures with smooth inversion and the local details with focusing inversion and determined the thickness of the target layer combined with the geological and drilling information,which meets the requirement for the site of the high-level waste clay-rock repository.

Adaptive inverse control of air supply flow for proton exchange membrane fuel cell systems

To prevent the oxygen starvation and improve the system output performance, an adaptive inverse control （AIC） strategy is developed to regulate the air supply flow of a proton exchange membrane fuel cell （PEMFC） system in this paper. The PEMFC stack and the air supply system including a compressor and a supply manifold are modeled for the purpose of performance analysis and controller design. A recurrent fuzzy neural network （RFNN） is utilized to identify the inverse model of the controlled system and generates a suitable control input during the abrupt step change of external disturbances. Compared with the PI controller, numerical simulations are performed to validate the effectiveness and advantages of the proposed AIC strategy.

Improved filtered-ε adaptive inverse control and its application on nonlinear ship maneuvering

The drawbacks of common nonlinear Filtered-ε adaptive inverse control （AIC） method, such as the unreliability due to the change of delay time and the faultiness existing in its disturbance control loop, are discussed. Based on it, the diagram of AIC is amended to accommodate with the characteristic of nonlinear object with time delay. The corresponding Filtered-ε adaptive algorithm based on RTRL is presented to identify the parameters and design the controller. The simulation results on a nonlinear ship model of ＂The R.O.V Zeefakker＂ show that compared with the previous scheme and adaptive PID control, the improved method not only keeps the same dynamic response performance, but also owns higher robustness and disturbance rejection ability, and it is suitable for the control of nonlinear objects which have higher requirement to the maneuverability under complex disturbance environment.

Research into the inversion of the induced polarization relaxation time spectrum based on the uniform amplitude sampling method

The induced polarization relaxation time spectrum（RTS） reflects the distribution of rock pore size,which is a key factor in estimating the oil or water storage capacity of strata.However,as the data acquisition and transmission abilities of well logging instruments are much limited due to the underground environment,it is necessary to explore suitable sampling methods which can be used to obtain an accurate RST with less sampling data.This paper presents a uniform amplitude sampling method（UASM）,and compares it with the conventional uniform time sampling method（UTSM） and logarithm time sampling method（LTSM） in terms of the adaptability to different strata,RTS inversion accuracy,and stratum vertical resolution.Numerical simulation results show that the UASM can obtain high inversion accuracy of RTS with different kinds of pore size distribution formation,with high dynamic ranges of pore size,and with a small number of sampling points.The UASM,being able to adapt to the attenuation speed of polarization curve automatically,thus has the highest vertical resolution.The inversion results of rock samples also show that the UASM is superior to the UTSM and LTSM.

APPLICATION OF NEURAL NETWORK INVERSE CONTROL SYSTEM IN TURBO DECODING

Adaptive inverse control system can improve the performance of turbo decoding,and modeling turbo decoder is one of the most important technologies. A neural network model for the inverse model of turbo decoding is proposed in this paper. Compared with linear filter with its revi-sion,the general relationship between the input and output of the inverse model of turbo decoding system can be established exactly by Nonlinear Auto-Regressive eXogeneous input (NARX) filter. Combined with linear inverse system,it has simpler structure and costs less computation,thus can satisfy the demand of real-time turbo decoding. Simulation results show that neural network in-verse control system can improve the performance of turbo decoding further than other linear con-trol system.

Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks

An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.

Study on the servo control system of MWEDM

An ultra-precision servo control system for MWEDM (micro wire electrical discharge machining) based on piezoelectric ceramic motor drivers was developed. The servo discharge detection adopts an average voltage detection method, utilizes statistical methods to analyze the measured data, then controls discharge gap and makes the process more stable. The servo feed system based on DSP microprocessors applies a PID controller with incomplete derivation to reduce overshoot to improve machining accuracy, and also adopts adaptive dead-zone inverse compensation to eliminate dead-zone caused by piezoelectric ceramic motions in low servo feed speed. The simulation results show that the methods proposed in this paper can make the system recover its linearity and converge the dead-zone parameters. It is proved that the method proposed is efficient in solving this problem. Cooperating with a micro energy pulse generator, this equipment’s machining accuracy is smaller than ±0.2 micron, and surface roughness Ra is less than 0.1 micron. Punch die and cavity die (micro gear die, module 100 micron, thickness 3.5 millimeter) have been machined independently in two die steel work-pieces. These pieces can mate very well to work, which shows that the servo control system satisfies the need of MWEDM for high precision micro fabrication.

THE DESIGN OF DYNAMIC SIMULATION SYSTEM ON EARTHQUAKE SURROUNDINGS

Objective To design a system that can simulate earthquake surroundings. In the surroundings, people can be familiar with the omen, strong shock and aftershock of earthquake, thus make right choices and get away when the disaster occurs. Methods The system consists of an electro hydraulic servo system, a whole information sound system and some lighting device; By using the adaptive inverse control method and LMS algorithms, the inverse model (i.e. the controller) is convergent rapidly; The software based on LabVIEW makes the parameters can be modified easily; There is a double closed loop structure in the system: an analog closed loop and a digital closed loop, and their parameters can be inspected in real time. Results The system is of very high reliability, and the desired vibration signal can be tracked exactly by output. Conclusion Earthquake surroundings is simulated vividly. Through the system, people can be familiar with earthquake phenomena, and know lots of knowledge of earthquake.

H_∞ Inverse Optimal Adaptive Fault-Tolerant Attitude Control for Flexible Spacecraft with Input Saturation

An adaptive inverse optimal attitude controller for flexible spacecraft with fault-free actuator is designed based on adaptive control Lyapunov function and inverse optimal methodology subjected to unknown parameter uncertainties,external disturbances and input saturation.The partial loss of actuator effectiveness and the additive faults are considered simultaneously to deal with actuator faults,and the prior knowledge of bounds on the effectiveness factors of the actuators is assumed to be unknown.A fault-tolerant control version is designed to handle the system with actuator fault by introducing a parameter update law to estimate the lower bound of the partial loss of actuator effectiveness faults.The proposed fault-tolerant attitude controller ensures robustness and stabilization,and it achieves H_∞ optimality with respect to a family of cost functionals.The usefulness of the proposed algorithms is assessed and compared with the conventional approaches through numerical simulations.