Linear Piezoelectric Ceramic Motor Research Articles

Precise Position Tracking Control With an Improved Transient Performance for a Linear Piezoelectric Ceramic Motor

In this paper, an output feedback position tracking control problem relevant for a linear piezoelectric ceramic motor is studied. The main goals of the control design include achieving extremely high resolution, accuracy, stability, and improved transient performance when system uncertainties, unknown dynamics, and external disturbances act on the system. The proposed control approach is designed in such a way that the implementation of the high-precision micro/nanopositioning is possible. An adaptive sliding-mode-based scheme is adopted to synthesize a robust controller. Stability analysis of the control approach is performed and it is analytically shown that the position tracking error asymptotically converges to zero. The important advantages of this control approach are: 1) designing a controller without the requirement of prior knowledge about uncertainty/disturbance bounds; 2) having a chattering-free control input; and 3) obtaining a fast response without overshoot. The proposed control strategy was experimentally validated and compared to the existing control approaches.

Dynamic Petri Fuzzy Cerebellar Model Articulation Controller Design for a Magnetic Levitation System and a Two-Axis Linear Piezoelectric Ceramic Motor Drive System

This brief aims to propose a more efficient control algorithm for a magnetic levitation system (MLS) and a two-axis linear piezoelectric ceramic motor (LPCM) drive system. A dynamic Petri fuzzy cerebellar (DPFC) model articulation controller is developed to allow control of the position of the metallic sphere of an MLS and the trajectory tracking of a two-axis LPCM drive system. In the DPFC, the concept of a Petri net and a fuzzy frame are incorporated into a cerebellar model articulation controller (CMAC), which alleviates the computational burden that accompanies the learning of parameters and enhances the fuzzy reasoning inference of CMACs. The supervised gradient-descent method is used to develop the online-training algorithm for the DPFC. To guarantee the convergence of trajectory tracking errors, analytical methods that use a discrete-type Lyapunov function are proposed for the DPFC. The experimental results demonstrate the effectiveness of the proposed DPFC for different experimental systems.

Intelligent Control Using the Wavelet Fuzzy CMAC Backstepping Control System for Two-Axis Linear Piezoelectric Ceramic Motor Drive Systems

This study aims to propose a more efficient control algorithm to achieve precision trajectory tracking control for a two-axis linear piezoelectric ceramic motor (LPCM). Since the inherent nonlinear nature and cross-coupling effect of a two-axis LPCM, its accurate model is difficult to obtain; thus, an intelligent adaptive wavelet fuzzy cerebellar model articulation controller backstepping (AWFCB) control system is designed to achieve high precision trajectory tracking control for a two-axis LPCM drive system. A novel wavelet fuzzy cerebellar model articulation controller (CMAC) is proposed in this paper; in some special cases, it can be reduced to a fuzzy system, a fuzzy neural network, a wavelet fuzzy neural network, or a conventional CMAC. The developed wavelet fuzzy CMAC incorporates the wavelet decomposition property and a fuzzy CMAC fast learning ability; thus, it is used for the LPCM control. In the AWFCB control system, a wavelet fuzzy CMAC is used to imitate an ideal backstepping controller, and a smooth compensator is designed to eliminate the residual of the approximation error between the wavelet fuzzy CMAC and the ideal backstepping controller. In order to guarantee the convergence of the tracking error, analytical methods using the Lyapunov function are utilized to derive the adaptation laws to tune the parameters of the control system online. Thus, the stability of the two-axis LPCM control system can be guaranteed. Finally, the experimental results show the precision of the trajectory tracking using AWFCB control. Compared with PID control and adaptive fuzzy sliding-mode control, the AWFCB control can achieve tracking error reduction of about 80%~99% and 48%~97%, respectively.

Adaptive growing-and-pruning neural network control for a linear piezoelectric ceramic motor

In this paper, an adaptive growing-and-pruning neural network control (AGPNNC) system is developed for a linear piezoelectric ceramic motor. The AGPNNC system is composed of a neural controller and a robust controller. The neural controller uses a self-constructing neural network (SCNN) to mimic an ideal computation controller, and the robust controller is designed to achieve L 2 tracking performance with desired attenuation level. If the approximation performance of the SCNN is inadequate, the SCNN can create new hidden neurons to increase learning ability. If the hidden neuron of the SCNN is insignificant, it should be removed to reduce computation loading; otherwise, if the hidden neuron of the SCNN is significant, it should be retained. Moreover, the adaptive laws of controller parameters are derived in the sense of Lyapunov function and Barbalat's lemma; so the system stability can be guaranteed. Finally, experimental results show that a perfect tracking response can be achieved using the self-constructing network mechanism and the on-line parameter-learning algorithm.

Comparison of Voltage-Source Resonant Driving Schemes for a Linear Piezoelectric Ceramic Motor

Linear piezoelectric actuators are innovative manipulators that have shown a high potential in applications requiring operation within the submicrometer or even the nanometer range. The driving principle of a linear piezoelectric ceramic motor (LPCM) is based on the ultrasonic vibration force of piezoelectric ceramic elements and mechanical frictional force. Therefore, its dynamic model is complex, and the motor parameters are time varying due to increasing temperature and changing motor drive operating conditions, so that it is difficult to design a suitable driving strategy for the LPCM. This paper presents a comparative study of three voltage-source resonant driving schemes, including an inductance-capacitance (LC) resonant inverter, an .LLCC-resonant inverter, and an LC-resonant inverter with energy feedback for the LPCM. The effectiveness of the reported driving strategies is verified by numerical analyses and experimental results under the variation of loads. By way of experimental validation, the newly designed -LC-resonant inverter with energy feedback for driving the LPCM possesses good performances of high voltage gain and invariant output characteristic under different operating conditions.

Development of Lyapunov-Based Genetic Algorithm Control for Linear Piezoelectric Ceramic Motor Drive

In general, the role of genetic algorithm (GA) is operated offline as a minor compensator or tuner in the control engineering because the systematic design and the latent stability problem of a GA-based control scheme are required to be solved. This paper originally designs a Lyapunov-based GA control (LGAC) scheme, and it applies for a practical control engineering example of the online motion control of a linear piezoelectric ceramic motor driven by a hybrid resonant inverter. In this control scheme, a GA control system via backstepping design technique is utilized to be the major controller, and adaptation laws derived from Lyapunov stability analyses are manipulated to adjust appropriate evolutionary steps. As a result, the system stability can be guaranteed directly without strict constraint conditions and detailed system knowledge. The effectiveness of the proposed drive and control system is verified by experimental results in the presence of uncertainties. From the measured results, the LGAC system performs superior high-precision motion control under wide operation range than conventional backstepping control system.

Design of Total Sliding-Mode-Based Genetic Algorithm Control for Hybrid Resonant-Driven Linear Piezoelectric Ceramic Motor

This study presents a total sliding-mode-based genetic algorithm control (TSGAC) system for a linear piezoelectric ceramic motor (LPCM) driven by a newly designed hybrid resonant inverter. First, the motor configuration and driving circuit of an LPCM are introduced, and its hypothetical dynamic model is represented by a nonlinear function with unknown system parameters. In the hybrid resonant drive system, it has the merits of the high voltage gain from a parallel-resonant current source, and the invariant output characteristic from a two-inductance two-capacitance resonant driving circuit. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, a TSGAC system is therefore investigated based on direction-based genetic algorithm with the spirit of total sliding-mode control (TSC) and fuzzy-based evolutionary procedure to achieve high-precision position control under a wide operation range. In this control scheme, a GAC system is utilized to be the major controller, and the stability can be indirectly ensured by the concept of TSC without strict constraints and detailed system knowledge. In addition, the effectiveness of the proposed drive and control system is verified by numerical simulations and experimental results in the presence of uncertainties

Adaptive grey control for hybrid resonant driving linear piezoelectric ceramic motor

This paper presents an adaptive grey control (AGC) system for a linear piezoelectric ceramic motor (LPCM) driven by a newly designed hybrid resonant inverter. First, the motor configuration and the driving circuit of an LPCM are introduced, and its hypothetical dynamic model is described briefly. The hybrid resonant drive system has the merits of high voltage gain from a parallel resonant current source and the invariant output characteristic from a two-inductance two-capacitance (LLCC) resonant driving circuit. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an AGC system is therefore investigated based on the principle of computed torque control and grey method to achieve high-precision position control under wide operation range. In this control system, a grey uncertainty predictor is utilized to estimate the lumped uncertainty on line to relax the requirement of the unknown uncertainty in the design of a computed torque position controller. In addition, the effectiveness of the proposed drive and control system is verified by numerical simulations and experimental results in the presence of uncertainties.

Supervisory control for linear piezoelectric ceramic motor drive using genetic algorithm

This paper presents a supervisory genetic algorithm (SGA) control system for a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance resonant driving circuit. First, the motor configuration and driving circuit of an LPCM are introduced, and its hypothetical dynamic model is described briefly. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an SGA control system is therefore investigated to achieve high-precision position control. The proposed SGA control system is composed of two parts. One is a GA control that is utilized to search an optimum control effort online via gradient descent training process, and the other is a supervisory control to stabilize the system states around a predefined bound region. Compared with conventional GA control systems, the proposed control scheme possesses the salient advantages of simple structure, fewer executing time, and good self-organizing properties. The effectiveness of the proposed driving circuit and control system is verified with numerical simulations and hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional position-control system.

Adaptive Hybrid Control for Linear Piezoelectric Ceramic Motor Drive Using Diagonal Recurrent CMAC Network

This paper presents an adaptive hybrid control system using a diagonal recurrent cerebellar-model-articulation-computer (DRCMAC) network to control a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance (LLCC) resonant inverter. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive hybrid control system is therefore designed based on a hypothetical dynamic model to achieve high-precision position control. The architecture of DRCMAC network is a modified model of a cerebellar-model-articulation-computer (CMAC) network to attain a small number of receptive-fields. The novel idea of this study is that it employs the concept of diagonal recurrent neural network (DRNN) in order to capture the system dynamics and convert the static CMAC into a dynamic one. This adaptive hybrid control system is composed of two parts. One is a DRCMAC network controller that is used to mimic a conventional computed torque control law due to unknown system dynamics, and the other is a compensated controller with bound estimation algorithm that is utilized to recover the residual approximation error for guaranteeing the stable characteristic. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional (IP) position control system.

Intelligent Motion Control for Linear Piezoelectric Ceramic Motor Drive

Since the dynamic characteristics of a linear piezoelectric ceramic motor (LPCM) are highly nonlinear and time varying, it is difficult to design a suitable motor drive and position controller that realizes accurate position control at all time. This study investigates a double-inductance double-capacitance (LLCC) resonant driving circuit and a sliding-mode fuzzy-neural-network control (SMFNNC) system for the motion control of an LPCM. First, the motor structure and LLCC driving circuit of an LPCM are introduced. The LLCC resonant inverter is designed to operate at an optimal switching frequency such that the output voltage will not be influenced by the variation of quality factor. Moreover, a SMFNNC system is designed to achieve favorable tracking performance without precise dynamic models being controlled. All adaptive learning algorithms in the SMFNNC system are derived in the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed driving circuit and control system is verified by experimental results.

Adaptive CMAC-based supervisory control for uncertain nonlinear systems.

An adaptive cerebellar-model-articulation-controller (CMAC)-based supervisory control system is developed for uncertain nonlinear systems. This adaptive CMAC-based supervisory control system consists of an adaptive CMAC and a supervisory controller. In the adaptive CMAC, a CMAC is used to mimic an ideal control law and a compensated controller is designed to recover the residual of the approximation error. The supervisory controller is appended to the adaptive CMAC to force the system states within a predefined constraint set. In this design, if the adaptive CMAC can maintain the system states within the constraint set, the supervisory controller will be idle. Otherwise, the supervisory controller starts working to pull the states back to the constraint set. In addition, the adaptive laws of the control system are derived in the sense of Lyapunov function, so that the stability of the system can be guaranteed. Furthermore, to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. Finally, the proposed control system is applied to control a robotic manipulator, a chaotic circuit and a linear piezoelectric ceramic motor (LPCM). Simulation and experimental results demonstrate the effectiveness of the proposed control scheme for uncertain nonlinear systems.

Implementation of LLCC-Resonant Driving Circuit and Adaptive CMAC Neural Network Control for Linear Piezoelectric Ceramic Motor

In this paper, an adaptive cerebellar-model articulation computer (CMAC) neural network (NN) control system is developed for a linear piezoelectric ceramic motor (LPCM) that is driven by an LLCC-resonant inverter. The motor structure and LLCC-resonant driving circuit of an LPCM are introduced initially. The LLCC-resonant driving circuit is designed to operate at an optimal switching frequency such that the output voltage will not be influenced by the variation of quality factor. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive CMAC NN control system is designed without mathematical dynamic model to control the position of the moving table of the LPCM drive system to achieve high-precision position control with robustness. In the proposed control scheme, the dynamic backpropagation algorithm is adopted to train the CMAC NN online. Moreover, to guarantee the convergence of output tracking error for periodic commands tracking, analytical methods based on a discrete-type Lyapunov function are utilized to determine the optimal learning-rate parameters of the CMAC NN. The effectiveness of the proposed driving circuit and control system is verified by experimental results in the presence of uncertainties, and the advantages of the proposed control system are indicated in comparison with a traditional integral-proportional position control system. Accurate tracking response and superior dynamic performance can be obtained due to the powerful online learning capability of the CMAC NN with optimal learning-rate parameters.

Robust CMAC neural network control for LLCC resonant driving linear piezoelectric ceramic motor

The authors present a robust cerebellar-model-articulation-computer (CMAC) neural network control system for a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance (LLCC) resonant inverter. The motor structure and the LLCC resonant driving circuit of a LPCM are introduced first. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time-varying, a robust CMAC neural network control system is therefore designed based on a hypothetical dynamic model to achieve high-precision position control. The LLCC resonant driving circuit is first designed to operate at an optimal switching frequency so that the output voltage will not be affected by the variation of quality factor. Next, the stability of a robust CMAC neural network control system can be ensured without any strict constraint and without much previous knowledge required. It can also be widely applied to other controlling problems. The effectiveness of the proposed driving circuit and control system is verified by the results taken from some experiments in this study under the occurrence of uncertainties. Furthermore, the advantages of the proposed control scheme are indicated in comparison with a traditional proportional integral position control system.