Yi-Cheng Huang

The Phenomenon of Apparent Convergence Followed by Divergence in Learning and Repetitive Control

Abstract It is a common experimental experience with learning and repetitive control, that the error decreases very substantially in the fast few repetitions or periods, and then starts to grow. In some cases this takes the form of a long term instability, for example in experiments cited here instability became evident only after 2,500 repetitions. Here we develop both time domain and frequency domain approaches to both learning and repetitive control, including use of root locus and Nyquist concepts. And these different viewpoints are used to explain in different ways this phenomenon of apparent initial convergence followed by divergence. It is seen that with typical distributions of tracking errors in the frequency spectrum relative to the bandwidth of the feedback controller, this phenomenon is easily generated. Three classes of approaches aze summarized for addressing this problem, either by stopping the mechanism of divergence, or by causing the convergence to continue to zero tracking error.

Experiments of iterative learning control system using particle swarm optimization by new bounded constraints on velocity and positioning

Purpose – This paper utilizes the improved particle swarm optimization (IPSO) with bounded constraints technique on velocity and positioning for adjusting the gains of a proportional-integral-derivative (PID) and iterative learning control (ILC) controllers. The purpose of this paper is to achieve precision motion through bettering control by this technique. Design/methodology/approach – Actual platform positioning must avoid the occurrence of a large control action signal, undesirable overshooting, and preventing out of the maximum position limit. Several in-house experiments observation, the PSO mechanism is sometimes out of the optimal solution in updating velocity and updating position of particles, the system may become unstable in real-time applications. The proposed IPSO with new bounded constraints technique shows a great ability to stabilize nonminimum phase and heavily oscillatory systems based on new bounded constraints on velocity and positioning in PSO algorithm is evaluated on one axis of li...

Simulation and Experiment of Langevin-Type Piezoelectric Ultrasonic Horn for Micro Tool Motion

The principle of a piezoelectric transducer used in this chapter is to transfer the high frequency of electrical energy to mechanical energy. Since the output displacement of piezoelectric transducer is too small, the need of amplifying the displacement of the tool is based on the design of horn shape. Since resonance frequency of the horn should be compatible with the vibration frequency of the piezoelectric vibrator. This chapter is first to conduct experiment to measure the vibration frequency and amplitude of a piezoelectric transducer (PZT) for ultrasonic vibration. According to the experimental result, the resonant frequency of the horn for amplifying the displacement of the PZT is achieved thereafter. Theoretical calculation and ANSYS simulation can be used for figuring out the geometrical shape of the piezoelectric actuated horn. In the simulation, the horn modal frequency and harmonics analysis via the ANSYS simulation reach the error less than 3 %. Experimental results will be compared and validated with the simulated results.

Prognostic experiment for ball screw preload loss of machine tool through the Hilbert-Huang Transform and Multiscale entropy method

This paper attempts to detect and predict the preload loss and life time of the ball screw demands in the machine tool industry. Loss of preload leads to a lower natural frequency and the onset of the nonlinear spring characteristics of the ball screw. Such problems result in rapid downtime in the manufacturing process and gradually undetermined positioning errors. In this paper the preload features are extracted and discriminated using the Hilbert-Huang Transform (HHT) and Multiscale entropy (MSE) method. The starting torque and consecutive quasi steady state constant currents of the machine tool feeding table are conducted experimentally. The test results show the prognostic status of ball screw preload loss can be envisaged by the proposed methodology.

Diagnosis of Ball Screw Preload Loss by Vibration Signals through the Hilbert-Huang Transform and Multiscale Measure

This paper proposes a diagnosis method of ball screw preload loss through the Hilbert-Huang Transform (HHT) and Multiscale entropy (MSE) process when machine tool is in operation. Maximum dynamic preload of 2% and 4% ball screws are predesigned, manufactured and conducted experimentally. Vibration signal patterns are examined and revealed by Empirical Mode Decomposition (EMD) with Hilbert Spectrum. Different preload features are extracted and discriminated by using HHT. The irregularity development of ball screw with preload loss is determined and abstracting via MSE based on complexity perception. The experiment results successfully show preload loss can be envisaged by the proposed methodology.

Design and Experiment of Iterative Learning Controller Based on Particle Swarm Optimization Approach with New Bounded Constraints Technique

This paper utilizes the Improved Particle Swarm Optimization (IPSO) with bounded constraints technique for adjusting the gains of a Proportional-Integral-Derivative (PID) and Iterative Learning Control (ILC) controllers. This study compares the conventional ILC-PID controller with proposed IPSO-ILC-PID controller. A cycloid trajectory for mimicking the real industrial motion profile is applied. Two system plants with nonminimum phase are numerically simulated. Proposed IPSO with bounded constraints technique is evaluated on one axis of linear synchronous motor (LSM) with a PC-based real time controller. Simulations and experiment results show that the proposed controller can reduce the error significantly after two iterations.

Prognostic Diagnosis of Ball Screw Preload Loss for Machine Tool through the Hilbert-Huang Transform and Multiscale Entropy Measure

Detection of ball screw preload loss for machine tool has great significances in modern manufacturing processes for unexpected failures. This paper proposes a diagnostic method for ball screw preload loss through the Hilbert-Huang transform (HHT) and multiscale entropy(MSE) process. The method is used to diagnose a ball screw preload loss through the motor torque current signals when machine tool is in operation. Maximum dynamic preloads of 2%, 4%, and 6% ball screws were predesigned, manufactured, and conducted experimentally. Signal patterns are discussed and revealed by Empirical Mode Decomposition (EMD) with the Hilbert Spectrum. Different preload features are extracted and discriminated using HHT. The irregularity development of ball screw with preload loss is determined and abstracted via MSE based on complexity perception. The experiment results successfully show that the prognostic status of ball screw preload loss can be envisaged by the proposed methodology. The smart sensing for the health of the ball screw is available based on comparative evaluation of MSE by the signal processing and pattern matching of EMD/HHT. This diagnosis method realizes the purposes of prognostic effectiveness on knowing the preload loss and utilizing convenience.

Dynamic Motion and Modal Analysis Simulation for the Balance Mass of the Crankshaft of a Two-Cylinder Reciprocating Air Compressor

The purpose of this study is to suppress the problem of vibration which occurs in a running two-cylinder reciprocating compressor. Determining an optimal crankshaft counterweight and narrowing the trajectory orbit of the crankshaft connecting rod mechanism is achieved. Use of finite element software (ANSYS) was used to simulate the modality of the two cylinders. In additions, the inertial force influenced to the crankshaft under different counterweight design was simulated. Simulation results by using SolidWorks software show the new counterweight was able to reduce the crankshafts eccentric by 4mm and the orbit trajectory by 10mm. This study provides the procedures pertaining to the bettering performance and assessment for an existing reciprocating compressor.

Design the Bandwidth of Zero-Phase Filter of Iterative Learning Controller by Improved Particle Swarm Optimization

This chapter utilized the IPSO technique with bounded constraints for adjusting the gains of PID controller, ILC controller, and the bandwidth of zero-phase Butterworth filter. Simulation results show that IPSO-ILC-PID controller will have chance of producing high frequencies in the error signals when the filter bandwidth is fixed for every repetition. Since the conventional ILC iterative learning process has the potential to excite rich frequency contents and try to learn the error signals, however, the learnable and unlearnable error signals should be separated for bettering control process. Thus, the adaptive bandwidth of a zero-phase filter in ILC-PID controller with IPSO tuning characteristic is proposed here. Simulation results show that the new controller can cancel the errors as repetition goes. The frequency response of the error signals is analyzed by the EMD and the HHT method. Errors are reduced and validated by good ILC learning with adaptive filter bandwidth.

Prognostic Diagnosis of Hollow Ball Screw Pretension on Preload Loss Through Sensed Vibration Signals

The pretension for a ball screw is a way to improve the position accuracy. Hollow ball screw without a cooling system has the thermal deformation effect due to increase in temperature. It will reduce precision accuracy in machine tool when the ball screw nut preload or ball screw pretension is lost. The purpose of this study is to use vibration signals for the prognostic analysis for the ball screw pretension. Features of different pretension conditions by 0, 5, 10, and 20μm are discriminated by empirical mode decomposition (EMD), fast Fourier transform (FFT), and marginal frequency method. Temperature effects with long-term operation were discussed. This study experimentally extracts the characteristic frequencies for bettering pretension through the vibration signals. This diagnosis results realize the purpose of prognostic effectiveness on knowing the hollow ball screw preload loss based on pretension data and utilizing convenience.