Precision Motion Control of a Linear Permanent Magnet Synchronous Machine Based on Linear Optical-Ruler Sensor and Hall Sensor

Abstract

The linear optical-ruler sensor with 1 μm precision mounted in the linear permanent magnet synchronous machine (LPMSM) is used for measuring the mover position of LPMSM in order to enhance the precision of a measured mover position. Due to nonlinear friction and uncertainty effects, linear controllers are very hard to achieve good mover positioning of LPMSM. The proposed adaptive amended Elman neural network backstepping (AAENNB) control system is adopted for controlling the LPMSM drive system to bring about the mover positioning precision of LPMSM. Firstly, a backstepping scheme is posed for controlling the tracing motion of the LPMSM drive system. The proposed backstepping control system, which is applied in the mover position of the LPMSM drive system, possesses better dynamic control performance and robustness to uncertainties for the tracing trajectories. Because of the LPMSM with nonlinear and time-varying dynamic characteristics, an adaptive amended Elman neural network uncertainty observer (AAENNUO) is posed to estimate the required lumped uncertainty. According to the Lyapunov stability theorem, on-line parameter training methodology of the amended Elman neural network (AENN) can be derived by use of adaptive law. The error estimated law is proposed to compensate for the observed error induced by the AENN with adaptive law. Furthermore, to help improve convergence and to obtain better learning performance, the mended particle swarm optimization (PSO) algorithm is utilized for adjusting the varied learning rate of the weights in the AENN. At last, these experimental results, which show better performance, are verified by the proposed control system.

Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive

“…The d - q axis model of the linear motion single axis robot machine by use of a synchronous rotating reference frame can be described as follows [3]:vqs=R1siqs+Lqsdiqs/dt+ωesfalse(Ldsids+λpmsfalse) vds=R1sids+Ldsdids/dt−ωesLqsiqs where vds , vqs are the d - axis and q - axis voltages; ids , iqs are the d -axis and q - axis currents; R1s is the phase winding resistance; Lds , Lqs…”

“…A linear motion single axis robot machine that can achieve rapid rates of acceleration by use of electromagnetic force has few features which are of merit [1,2,3], such as being simple fabric, having no adverse reaction, little friction, elated velocity, elated pushed force, and elated precision in a long-distance location and so on. A linear motion single axis robot machine consists of some of magnets that create constant magnetic fields, and some windings that create the traveling magnetic fields.…”

“…A linear motion single axis robot machine consists of some of magnets that create constant magnetic fields, and some windings that create the traveling magnetic fields. A number of applications of the linear motion single axis robot machine include checking the camera moving unit, ink jet printer, chip mounter, checking the device, a high-speed screw-tightening unit, a high-speed loading/unloading robot, and material handling systems [1,2,3].…”

“…One of the control methods for the large state feedback linearizable systems include the backstepping techniques [3,4,5]. The design of tracking and adjustment strategies can provide a systematic skeleton.…”

Indirect Force Control of a Cable-Driven Parallel Robot: Tension Estimation using Artificial Neural Network trained by Force Sensor Measurements

“…Liu et al used an NN-based friction compensation method for approximating the residual values during free motion [25]. Several other friction compensation techniques using ANNs could be found in the previous research [26,27,28]. Nevertheless, to the best of the authors’ knowledge, our study is the first ANN application to use the cable force control of a CDPR by using an ANN-based CDPR end-effector’s cable tension estimator for estimating the cable–pulley friction and compensating for cable uncertainties.…”