Shao Zhao

Precision motion control with disturbance observer for pulsewidth-modulated-driven permanent-magnet linear motors

In this paper, we address the problem of precision motion control of permanent-magnet linear motors (PMLMs) under the influence of significant disturbances. We establish a mathematical model of a PMLM driven by a sinusoidal pulsewidth-modulated (PWM) amplifier, obtaining it from a describing function analysis of the essentially nonlinear characteristics. The overall model (PWM+PMLM) inevitably inherits uncertainties in the face of load changes, system parameter perturbation, noise, and inherent system nonlinearities, etc., all of which constitute disturbances to the control system that will adversely affect the precision and accuracy. We propose a robust control scheme employing a disturbance observer to address the sensitivity of the control performance to the disturbances. Real-time experimental results are provided to verify and confirm the practical effectiveness of the proposed approach.

Iterative reference adjustment for high-precision and repetitive motion control applications

A learning control scheme is proposed which is suitable for high-precision and repetitive motion control applications. It comprises of a self-tuning radial basis function (RBF) network operating in parallel with an iterative learning control (ILC) component. Unlike the usual ILC scheme which adapts a feedforward control signal to achieve improved tracking performance over time, the proposed scheme iteratively adjusts the reference signal. The RBF network is employed as a nonlinear function estimator to model the tracking error over a cycle, and this error model is subsequently used implicitly in the iterative adaptation of the reference signal over the next cycle. The ILC component further enhances the tracking performance, particularly over the sections of the trajectory where the RBF network is less adequate in its modeling function. Simulation examples and real-time experimental results are fully furnished to elaborate the various highlights of the proposed method.

Force ripple suppression in iron-core permanent magnet linear motors using an adaptive dither

This paper presents the design and realization of an adaptive dither to reduce the force ripple in an iron-core permanent magnet linear motor (PMLM). A composite control structure is used, consisting of three components: a simple feedforward component, a PID feedback component and an adaptive feedforward compensator (AFC). The first two components are designed based on a dominant linear model of the motor. The AFC generates a dither signal with the motivation to eliminate or suppress the inherent force ripple, thus facilitating smooth precise motion while uncompromising on the maximum force achievable. An analysis is given in the paper to show the parameter convergence. Computer simulations and real-time experimental results verify the effectiveness of the proposed scheme for high precision motion trajectory tracking using the PMLM.

Adaptive force ripple suppression in iron-core permanent magnet linear motors

This paper presents the design and realization of an adaptive dither to reduce the force ripple in an iron-core permanent magnet linear motor (PMLM). A composite control structure is used, consisting of three components: a simple feedforward component, a PID feedback component and an adaptive feedforward compensator (AFC). The first two components are designed based on a dominant linear model of the motor. The AFC generates a dither signal with the motivation to eliminate or suppress the inherent force ripple, thus facilitating smooth precise motion while uncompromising on the maximum force achievable. Real-time experimental results verify the effectiveness of the proposed scheme for high precision motion trajectory tracking using the PMLM.

Repetitive control approach towards automatic tuning of Smith predictor controllers.

This paper proposes a new method for automatic tuning of the Smith predictor controller based on a Repetitive Control (RC) approach. The method requires the input of a periodic reference signal which can be derived from a relay feedback experiment. A modified repetitive control scheme repetitively changes the control signal to achieve tracking error convergence. Once a satisfactory performance is achieved through the learning control, the parameters of the Smith predictor controller can be computed from the signals using a nonlinear least squares algorithm. The same relay feedback experiment can provide an initial parameter vector for an efficient implementation of the parameter estimation. Simulations and experimental results will be furnished to illustrate the effectiveness of the proposed tuning method.

Iterative reference adjustment for high precision and repetitive motion control applications

In this paper, a new iterative learning control (ILC) scheme is proposed which is suitable for high precision and repetitive motion control applications. Unlike the usual ILC scheme which adapts a feedforward control signal to achieve improved tracking performance over time, the proposed scheme iteratively adjusts the reference signal. To achieve a higher convergence rate, a radial basis function neural network is employed to model the tracking error over a cycle, and subsequently used implicitly in the iterative adaptation of the reference signal over the next cycle. Simulation examples are furnished to elaborate the various highlights of the proposed method.

Intelligent compensation of friction, ripple, and hysteresis via a regulated chatter

In this paper, a hybrid control scheme utilizing a PID feedback control with an additional regulated chatter signal is developed to compensate motion impeding influences such as the effects due to friction, force ripples, and hysteresis in linear piezoelectric motor. The regulated chatter signal is a pulse sequence superimposed on the PID control signal. It has a fixed amplitude, and a pulse width regulated via iterative learning control. As such, the scheme is expected to be useful for applications involving iterative motion sequences. An analysis of the closed-loop performance is presented in the paper. Simulation and experimental results are also furnished to demonstrate that the proposed control scheme can reduce tracking errors significantly.

Autonomous robot navigation based on fuzzy sensor fusion and reinforcement learning

This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into a subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. Real-world application using a Khepera robot shows the robustness and flexibility of the developed system in dealing with robotic behavior such as target collecting in an ever-changing physical environment.

Direct measurement of beam size in a spectroscopic ellipsometry setup

Spectroscopic ellipsometry signals used in thin film analysis are dependent on the beam probe size. In this work, we report a technique to determine the beam size that uses the existing detection facilities in a spectroscopic ellipsometry setup without the need to rearrange the optical components. The intensity signal recorded with the technique comprises a coupled boundary diffraction and knife edge wave that can be isolated using nonlinear fitting. This then permitted an accurate measurement of the beam size with the stronger knife edge component. The technique has the added advantage of picking up chromatic aberration in the probing lens which may be a factor in ellipsometry measurement.

Predictive Ratio Control of Multizone Thermal Processing System in Lithography

Abstract Baking of semiconductor substrate is common and critical to photoresist processing in the lithography sequence. Temperature uniformity control is an important issue in photoresist processing with stringent specifications and has a significant impact on the linewidth or critical dimension (CD). In this work, we present the development of a ratio control strategy for controlling temperature uniformity of a silicon wafer substrate. Traditional approach in ratio control does not consider interaction among the different input, our approach takes into consideration the interaction between the different heating zone of the novel multizone thermal system developed by us. The resultant model-based GPC PID controller is designed and tested on the multizone thermal system. Simulation results shows that spatial temperature uniformity can be controlled to within 1°C and 0.1°C during transient and steady-state operating condition respectively.