A Repetitive Model Predictive Control Approach for Precision Tracking of a Linear Motion System

In this paper, a new model predictive control (MPC) approach suitable for high precision linear motion drive operating with repetitive tracking tasks is presented. For the proposed predictive controller, the feedforward controller of the conventional MPC has been modified to provide zero-phase learning property. This is achieved by augmenting the reference trajectory with a phase-compensated term that is updated with the historical tracking error. The proposed approach attempts to combine the merits of both the conventional MPC and repetitive control schemes. Experimental results have demonstrated that the system effectively reduces the tracking error from the periodic disturbance caused by the friction. Its performance under varying reference conditions and different loadings shows that the system is robust.      

Contouring control of biaxial systems based on polar coordinates

A contouring controller for biaxial systems that integrates the effects of feedback, feedforward, and cross-coupled control is proposed in this study. Conventional approaches to contouring control suffer from the complicated contour-error model and from lack of a systematic way for controller design. The integrated controller is based on polar coordinates under which a relatively simple contour-error model can be obtained. Taking the simple contour error as a state variable, the contouring-control problem is transformed into a stabilization problem. The feedback-linearization technique incorporated with linear feedback or robust control (such as sliding-mode control) can then yield the integrated controller. The proposed method is verified both numerically and experimentally and is compared with the conventional approach. It is found that the proposed controller is better for high speed and/or noncircular contouring. In addition, it can be applied to either linear plants or nonlinear plants (like linear motors).     

Cross-coupling position command shaping control in a multi-axis motion system

A new structure of a cross-coupling position command shaping controller (CPCSC) for precise tracking in multi-axis motion control is proposed in this paper. This controller feedforwards the cross-coupling terms, based on the geometrical relationship between the tracking and contouring errors, to compensate for the contouring error in real-time. Compared with the conventional multi-axis cross-coupling control (CCC) system, this new structure has the advantage that its compensators in CCC have a simpler design process than conventional ones, as does its stability analysis. The proposed controller is evaluated and compared experimentally with a traditional uncoupled and a conventional CCC controller on a multi-axis positioning system controlled by microcomputer. The experimental results show that the new structure remarkably reduces contour error. In addition, this new controller can be implemented easily on most current systems by reprogramming the reference position command subroutine.     

A neural network-based tracking control system

An application of the backpropagation neural network to the tracking control of industrial drive systems is presented. The merits of the approach lie in the simplicity of the scheme and its practicality for real-time control. Feedback error trajectories, rather than desired and/or actual trajectories, are employed as inputs to the neural network tracking controller. It can follow any arbitrarily prescribed trajectory even when the desired trajectory is changed to that not used in the training. Simulation was performed to demonstrate the feasibility and effectiveness of the proposed scheme     

Fuzzy two-degrees-of-freedom speed controller for motor drives

A fuzzy two-degrees-of-freedom (2-DOF) controller and its application to the speed control of an induction motor drive are presented in this paper. The proposed controller is composed of two fuzzy controllers to obtain good tracking and regulating responses. Unlike the conventional fuzzy controller, the error between the outputs of a reference model and the controlled drive is used to drive the proposed fuzzy controller. The drive rotor speed response can closely follow the trajectory produced by the reference model, and good load speed regulating response can also be obtained simultaneously owing to the possession of two-degrees-of-freedom in structure. Moreover, these performances are rather insensitive to the operating condition changes. The dynamic signal analysis as well as the construction of fuzzy control algorithms are described in detail. Some simulated and measured results are provided to demonstrate the effectiveness of the proposed fuzzy controller     

Iterative learning contouring control: Theory and application to biaxial systems

This paper is concerned with the iterative learning control (ILC) for the contouring control of multi-axis motion system. A novel ILC algorithm, which is called iterative learning contouring control (ILCC), is proposed and verified with biaxial motion systems. In motion control, conventional ILC requires that the number of control objectives is the same as the number of command inputs, which is called square property. This allows for the formulation of transfer function or matrix inversion. The unique feature of contouring control is its non-square property. For contouring control, conventional ILC circumvents the non-square property by adopting the configuration of cross-coupled control (CCC). In other words, the estimated contour error is decomposed into components of individual axis for compensation by ILC and thus the contour error is reduced indirectly. The proposed ILCC can deal with the non-square property and can directly reduce the contour error by adopting the equivalent contour error as the control objective of the ILC. The equivalent contour error represents an accurate contour error model and hence better contouring accuracy can be achieved. The ILCC algorithm is implemented on two platforms, an XY table and a commercial CNC machine tool. Experiments on the contouring control of a circular path with different speeds and a B-spline path are conducted. It is found that the contour error can be reduced about 88% after several learning iterations. The experimental results confirm the effectiveness of the proposed method and show the excellent contouring performance.     

New robust adaptive control algorithm for high-performance ACdrives

This paper presents a new robust structure for a model reference adaptive control (MRAC) controller for field-oriented-controlled (FOC) drives which requires no prior knowledge of the drive parameters and is guaranteed to provide global asymptotic stability of the closed-loop system. This structure simplifies the design and implementation of the adaptive controller requiring less effort to synthesis than a standard MRAC system. Discussion on theoretical aspects, such as selection of a reference model, stability analysis proof, gain adaptive process, steady-state error elimination, and robustness to unmodeled dynamics are included. The paper describes many practical aspects of the implementation, such as adaptive gain analysis, adaptive rate selection, the gain variation limits, gain windup prevention measure, and initial values. The new robust adaptive controller has been successfully implemented on an FOC drive and experiment results for dynamic tracking, sudden loading and unloading, and gains adaptation under different operation conditions are presented to support the robustness of the proposed controller     

Model reference adaptive speed control for induction motor driveusing neural networks

A model reference adaptive speed control scheme using neural networks is presented. The robust observer-based model reference tracking control technique is used to establish the training patterns. Then, the trained neural networks are used as an adaptive speed controller to robustly track a reference model for an induction motor drive     

FPGA-Based Adaptive Backstepping Sliding-Mode Control for Linear Induction Motor Drive

A field-programmable gate array (FPGA)-based adaptive backstepping sliding-mode controller is proposed to control the mover position of a linear induction motor (LIM) drive to compensate for the uncertainties including the friction force. First, the dynamic model of an indirect field-oriented LIM drive is derived. Next, a backstepping sliding-mode approach is designed to compensate the uncertainties occurring in the motion control system. Moreover, the uncertainties are lumped and the upper bound of the lumped uncertainty is necessary in the design of the backstepping sliding-mode controller. However, the upper bound of the lumped uncertainty is difficult to obtain in advance of practical applications. Therefore, an adaptive law is derived to adapt the value of the lumped uncertainty in real time, and an adaptive backstepping sliding-mode control law is the result. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results. With the adaptive backstepping sliding-mode controller, the mover position of the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.     

Dynamics and contouring control of a 3-DoF parallel kinematics machine

In machining applications, instead of tracking error (the difference between the actual position and the desired position), contouring error (the minimum distance from the actual position to the desired trajectory) characterizes product quality. In this paper, we propose a generalized moving task coordinate frames based contouring control for parallel kinematics machines, whose dynamics is in general coupled and strongly nonlinear. The Orthopod, a 3 degree-of-freedom purely translational parallel kinematics machine, is introduced as a control plant. The Lagrange-D’Alembert formulation is used to model the system dynamics. The developed dynamic model in Cartesian space is transformed and parametrized by tangential error, normal error, and binormal error in moving task coordinate frames. The contouring error is then approximated by the normal error and the binormal error, which is the projection of tracking error to the normal plane at the desired position. By employing the structural properties of the transformed dynamics, a special feedback linearization, the computed torque control is applied. It leads to a stabilization problem for a second-order linear time-invariant system. Coulumb plus viscous friction model is used to compensate friction effects. Friction parameters are identified by least-squares approach. For comparison purpose, the tracking error based computed torque control is also carried out. Experiments demonstrate that the proposed control scheme not only leads to improved contouring accuracy, but also produces smaller and smoother control input torques, which may contribute to smaller vibration.     

位置和艏向角约束的非对角欠驱动USV轨迹跟踪

仅配备有纵向推进力和转船力矩装置的无人水面艇是典型的欠驱动系统,不能通过定常光滑反馈控制律镇定到平衡状态。本文针对一类惯性矩阵和阻尼矩阵非对角的欠驱动无人水面艇,设计了基于附加控制器和反步法的光滑时变跟踪控制律,在保证跟踪误差暂态性能的前提下,实现了曲线和直线情形下的轨迹跟踪。首先,通过状态变换将非对角模型转化为对角形式,并运用反馈线性化理论简化控制输入。其次,通过设计虚拟控制函数来镇定误差运动学方程,并通过引入障碍Lyapunov函数(BLF)来保证跟踪误差满足规定的性能。然后,通过在误差镇定函数中引入虚拟控制量解决了系统的欠驱动问题,稳定性分析表明本文控制策略能够保证闭环系统中的所有状态是一致最终有界的。最后,Matlab/Simulink仿真结果表明了该控制器的有效性。     

Path-tracking velocity control for robot manipulators with actuator constraints

An algorithm for high-performance path tracking for robot manipulators in the presence of model uncertainties and actuator constraints is presented. The path to be tracked is assumed given, and the nominal trajectories are computed using, for example, well-known algorithms for time-optimal path tracking. For online path tracking, the nominal, feedforward trajectories are combined with feedback in a control architecture with a secondary controller, such that robustness to uncertainties in model or environment is achieved. The control law is based on existing path-velocity control (PVC), or so called online time scaling, but in addition to speed adaptation along the tangent of the path, the algorithm also comprises an explicit formulation and approach, with several attractive properties, for handling the deviations along the transversal directions of the path. For achieving fast convergence along the normal and binormal directions of the path in 3D motion, the strategy proposed has inherent exponential convergence properties. The result is a complete architecture for path-tracking velocity control (PTVC). The method is evaluated in extensive simulations with manipulators of different complexity, and PTVC exhibits superior performance compared to PVC.     

Development of Robust Current 2-DOF Controllers for a Permanent Magnet Synchronous Motor Drive With Reaction Wheel Load

This paper develops robust 2-DOF current and torque control schemes for a permanent magnet synchronous motor (PMSM) drive with satellite reaction wheel load. A DSP-based experimental PMSM-driven reaction wheel system is established, and the key motor parameters are estimated for realizing the proposed control schemes. In the proposed current control schemes, the traditional 2-DOF controller is augmented with an internal model feedback resonant controller or a robust tracking error cancellation controller (RECC). Comparative performance and error analyses of these two proposed control schemes are given. Accordingly, an improved robust 2-DOF current control scheme combining the resonant controller and the RECC is further proposed. The resonant controller enhances the transient and steady-state tracking of the sinusoidal current, simultaneously rejecting the back electromotive force. A similar robust tracking control for the observed torque can be designed, which exhibits quick transient response. Effectiveness of the proposed controls and the driving performance of the whole reaction wheel are evaluated experimentally.      

A VSS speed controller with model reference response for inductionmotor drive

This paper is mainly concerned with the development of a variable-structure system (VSS) controller with model reference speed response for an induction motor drive. An indirect-field-oriented (IFO) induction motor drive is first implemented, and its dynamic model at a nominal operating condition is estimated from measured data. Then, a two-degrees-of-freedom linear model-following controller (2DOFLMFC) is designed to meet the prescribed tracking and load regulation speed responses at the nominal case. As the variations of system parameters and operating condition occur, the prescribed control specifications may not be satisfied further. To improve this, a VSS controller is developed to generate a compensation control signal to reduce the control performance degradation. The proposed VSS controller is easy to implement, since only the output variable is sensed. The existence condition of sliding-mode control is derived, and the chattering suppression during the static period is also considered. Good model-following tracking and load regulation speed responses are obtained by the designed VSS controller. Effectiveness of the proposed controller and the performance of the resulting drive system are confirmed by some simulation and measured results     

Robust model predictive control and observer for direct driveapplications

In this paper, robust position control of a direct drive using a state space model predictive control (MPC) algorithm is presented. The proposed controller consists of a state feedback regulator and a feedforward controller. Their gains are obtained by minimizing a cost function that is a sum of the position tracking errors and the control cost over some user defined time horizons. The effects of the controller parameters on the dynamic performance and the robustness of the direct drive are investigated. To provide good estimates of the state variables in the presence of load disturbance, a new observer based on the receding horizon concept is also formulated. Experimental results are presented to demonstrate the effectiveness of the approach     

Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms

A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance     

Estimation of the contouring error vector for the cross-coupledcontrol design

In biaxial motion systems, applying the cross-coupled control (CCC) significantly improves contouring accuracy for linear and circular contours. As geometrical and parametric curves become more popular in modern manufacturing, machining processes with multiaxis motion systems are required, however, the available biaxial CCC cannot be directly applied to arbitrary contours with multiaxis machining systems. In this paper, we propose a novel approach for arbitrary contours by estimating the contouring error vector to efficiently determine the variable gains for CCC. Experimental results for a biaxial motion system indicate that the proposed approach efficiently yields variable gains similar to those in traditional CCC. Furthermore, results on a three-axis CNC machining center show that the present approach significantly improves motion accuracy in multiaxis motion systems     

Model reference adaptive predictive control for a variable-frequency oil-cooling machine

This paper develops methodologies and techniques for the design, analysis, and implementation of a model reference adaptive predictive temperature controller for a variable-frequency oil-cooling machine, suited for cooling high-speed machine tools. The oil-cooling process is modeled experimentally as a first-order system model with a time delay and its system parameters are identified using the recursive least-square method. Based on this model, a model reference adaptive predictive controller is proposed for achieving set-point tracking and robustness. A real-time model reference adaptive predictive control algorithm is then presented and implemented utilizing a stand-alone digital signal processor TMS320F243 from Texas Instruments Incorporated. The experimental results show that the proposed control method is proven capable of giving satisfactory performance under set-point changes, fixed loads, and load changes.     

Evolutionary algorithm based feedforward control for contouring of a biaxial piezo-actuated stage

The main objective of this paper is to establish a precision contouring control of a biaxial piezoelectric-actuated stage. The positioning accuracy of the piezoelectric-actuated stage is limited due to the hysteretic nonlinearity of the piezoelectric actuators (PEA). To compensate this hysteresis problem, a feedforward controller based on an evolution algorithm is proposed. The dynamics of the hysteresis is formulated by the Bouc–Wen model and the evolutionary algorithms (EAs) are studied to identify the optimal parameters of the Bouc–Wen model. To verify the consistency, two micro-contouring tasks are implemented by the proposed feedforward controller with the feedback of linear optical scales in DSP based real-time control architecture.     

Drive-Mode Control for Vibrational MEMS Gyroscopes

This paper presents a novel design methodology and hardware implementation for the drive-mode control of vibrational micro-electro-mechanical systems gyroscopes. Assuming that the sense mode (axis) of the gyroscope is operating under open loop, the drive-mode controller compensates an undesirable mechanical spring-coupling term between the two vibrating modes, attenuates the effect of mechanical-thermal noise, and most importantly, forces the output of the drive mode to oscillate along a desired trajectory. The stability and robustness of the control system are successfully justified through frequency-domain analysis. The tracking error between the real output and the reference signal for the drive mode is proved to be converging with the increase of the bandwidth of the controller. The controller is first simulated and then implemented using field-programmable analog array circuits on a vibrational piezoelectric beam gyroscope. The simulation and experimental results verified the effectiveness of the controller.