This paper focuses on an electro-hydraulic servo system, which is derived from a shaking table. It proposes a control scheme based on a back propagation (BP) neural network, whose weights are trained by the particle swarm optimization (PSO) according to the fitness, which is determined by the input and the feedback signals. Each particle of PSO includes weights and thresholds of BP. The movement of each particle is adjusted by its local best-known position and the global best-known position in the searching space. With the update, a satisfactory solution can be achieved. In order to show the performance of the proposed control scheme, the designed network is also trained and tested by BP only. The comparisons between the PSO-BP and BP networks demonstrate that the PSO-BP one has better performance than that of BP, both in convergence speed and in convergence accuracy.

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Electro-hydraulic servo shaking table usually requires good control performance for acceleration replication. The poles of the electro-hydraulic servo shaking table are placed by three-variable control method using pole placement theory. The system frequency band is thus extended and the system stability is also enhanced. The phase delay and amplitude attenuation phenomenon occurs in electro-hydraulic servo shaking table corresponding to an acceleration sinusoidal input. The method for phase delay and amplitude attenuation elimination based on LMS adaptive filtering algorithm is proposed here. The task is accomplished by adjusting the weights using LMS adaptive filtering algorithm when there exits phase delay and amplitude attenuation between the input and its corresponding acceleration response. The reference input is weighted in such a way that it makes the system output track the input efficiently. The weighted input signal is inputted to the control system such that the output phase delay and amplitude attenuation are all cancelled. The above concept is used as a basis for the development of amplitude-phase regulation (APR) algorithm. The method does not need to estimate the system model and has good real-time performance. Experimental results demonstrate the efficiency and validity of the proposed APR control scheme.

SUMMARYTo obtain better performance on unstructured environments, such as in agriculture, forestry, and high-altitude operations, more and more researchers and engineers incline to study classes of biologically inspired robots. Since the natural inchworm can move well in various types of terrain, inchworm-like robots can exhibit excellent mobility. This paper describes a novel inchworm-type robot with simple structure developed for the application for climbing on trees or poles with a certain range of diameters. Modularization is adopted in the robot configuration. The robot is a serial mechanism connected by four joint modules and two grippers located at the front and rear end, respectively. Each joint is driven by servos, and each gripper is controlled by a linear motor. The simplified mechanism model is established, and then is used for its kinematic analysis based on screw theory. The dynamics of the robot are also analyzed by using Lagrange equations. The simulation of the robot gait imitating the locomotion of real inchworm is finally presented.

Non-linearities commonly exist in an electro-hydraulic servo shaking table, causing acceleration harmonics distortion when the shaking table is excited by a sinusoidal acceleration signal, because its acceleration response includes higher harmonics, which lower the control performance for an electro-hydraulic servo shaking table. To cancel the harmonics in the system response, thus to improve the shaking table performance, we need to know about the harmonics information. An identification algorithm is developed here based on a Kalman filter for dynamically tracking the acceleration harmonics for the electro-hydraulic servo shaking table. A linear system in state space is modelled. The system acceleration response is applied as an observation value and is imported to the Kalman filter, which recursively estimates the state vector of the linear system. The amplitude and phase of each harmonic are calculated from the estimated state vector, and their estimated values are validated. A simulation example is presented and experiments were performed on the electro-hydraulic servo shaking table. Both results show a good estimation performance of the proposed acceleration harmonic identification algorithm.

Shaking tables play a vital role in mechanical environmental simulation. Sinusoidal shaking tests are usually applied to specimens for simulating periodic motions. Due to the nonlinearities in the electro-hydraulic servo shaking table, its sinusoidal acceleration response contains higher harmonics, which lower the system control performance. To cancel those harmonics, the harmonic information should be firstly known. The paper proposes an acceleration harmonic identification scheme by using the extended Kalman filter. A nonlinear state space model of the acceleration response is then built for the extended Kalman filter. The harmonic information, including the amplitude and phase of each harmonic, is directly derived from the estimated states. The features of the algorithm are that the state transition equation is linear and the measurement equation is nonlinear. It also inherits the advantages of the traditional linear Kalman filter. Both simulation and experimentation are carried out to validate its efficiency and accuracy. The online estimated harmonic information can provide a basis for the further harmonic cancellation.

A photoelectric detection system is a typical type of device widely used for detecting purposes based on unmanned aerial vehicles (UAV). Stability accuracy is the key performance index. Compared to traditional analysis methods aimed at unpredictable error-causing sources, assembly errors can be easily controlled during the manufacturing processes. In this research, an analysis method of assembly error effect on stability accuracy is proposed. First, by using kinematics analysis of homogeneous coordinate transformation, stability accuracy is comprehensively modeled and simulated. Then, by analyzing the manufacturing process, assembly errors of axis perpendicularities, run-outs and gyroscopes are defined and modeled. By simulating different carrier movements, the effects caused by assembly errors under various environments are studied. Finally, error sensitivity is proposed by using standard deviation analysis. Results show that the most sensitive assembly errors are identified, and ranked in order of sensitivity as follows: x-component of pitch axis perpendicularity, y-component of the azimuth gyroscope assembly, and z-component of the pitch gyroscope assembly. In conclusion, the results can be used as standards of manufacturing process improvements, and the proposed methods can be used to provide valuable references for real application scenarios.

For an electro-hydraulic servo shaking table, there are nonlinearities, which cause acceleration harmonic distortion when they corresponds to a sine acceleration excitation signal. The work here is to develop an acceleration harmonic identification algorithm by using the normalized least-mean-square (LMS) adaptive algorithm, whose weights are updated by the error between the acceleration response and the estimated acceleration signal. The input vector is generated by the reference harmonics and the [Formula: see text] phase shift. When the identification algorithm converges, the amplitude and phase of each harmonic can be computed from the weight vector. Experimental results show that the proposed harmonic identification has good real-time performance and a fast convergence rate, and it can identify harmonics on-line with high precision both in amplitude and in phase.