Zero-phase odd-harmonic repetitive controller for a single-phase PWM inverter

In this paper, a zero-phase odd-harmonic repetitive control scheme is proposed for pulse-width modulation inverters. The proposed repetitive controller combines an odd-harmonic periodic generator with a noncasual zero-phase compensation filter. It occupies less data memory than a conventional repetitive controller does. Moreover, it offers faster convergence of the tracking error, and yields very low total harmonics distortion (THD) and low tracking error. Analysis and design of the proposed system are presented. Experimental results with the proposed repetitive controller are presented to validate the approach. The phenomena of even-harmonic residues in the proposed control system is discussed and experimentally demonstrated.     

Discrete-time model predictive contouring control for biaxial feed drive systems and experimental verification

In this paper we present a novel algorithm to model predictive contouring control for biaxial feed drive systems. model predictive control (MPC) refers to a class of model-based controllers that uses an explicit process model and tracking error dynamics to predict the future behavior of a plant, making it effective for machine tool feed drive systems that must achieve high-precision motion and are severely affected by friction, cutting force and changes in the workpiece mass. To improve contouring performance, we propose a new performance index in which error components orthogonal to the desired contour curve are given more importance than tracking errors with respect to each feed drive axis. Controller parameters are calculated in real time by solving an optimization problem. The parameters depend on the instantaneous slope of the reference trajectory and thus vary with time for curved reference trajectories, resulting in a time-varying controller. Weighting factors for the error components in orthogonal and tangential directions are used to adjust the error importance in each direction. In addition, to consider the required feed drive energy, the control inputs in both directions are included in the performance index. The effectiveness of the proposed control approach is demonstrated with an experimental biaxial feed drive system for circular and non-circular trajectories. The proposed contouring controller allows the feed drive to follow smooth curves and reduces contouring error.     

Performance evaluation of a MPPT controller with model predictive control for a photovoltaic system

ABSTRACT

Efficiency has been a major factor in the growth of photovoltaic (PV) systems. Different control techniques have been explored to extract maximum power from PV systems under varying environmental conditions. This paper evaluates the performance of a new improved control technique known as model predictive control (MPC) in power extraction from PV systems. Exploiting the ability of MPC to predict future state of controlled variables, MPC has been implemented for tacking of maximum power point (MPP) of a PV system. Application of MPC for maximum power point tracking (MPPT) has been found to result into faster tracking of MPP under continuously varying atmospheric conditions providing an efficient system. It helps in reducing unwanted oscillations with an increase in tracking speed. A detailed step by step process of designing a model predictive controller has been discussed. Here, MPC has been applied in conjunction with conventional perturb and observe (P&O) method for controlling the dc-dc boost converter switching, harvesting maximum power from a PV array. The results of MPC controller has been compared with two widely used conventional methods of MPPT, viz. incremental conductance method and P&O method. The MPC controller scheme has been designed, implemented and tested in MATLAB/Simulink environment and has also been experimentally validated using a laboratory prototype of a PV system.     

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.     

Zero tracking error controller for three-phase CVCF PWM inverter

A zero tracking error control scheme for three-phase CVCF PWM inverters is proposed. The proposed scheme uses repetitive controller (RC) to force output line voltages to track a sinusoidal reference signal with zero error. Minimised voltage distortion and a fast response are obtained. The validity of the proposed scheme has been verified by simulations     

基于模型预测控制的大口径快摆镜随动系统

为了满足地基大口径望远镜精密稳像系统的需求,对大口径快摆镜(FSM)的控制方法进行了研究。为了解决三促动器FSM的运动解耦为系统辨识带来的困难,通过解析法和系统辨识法相结合建立了FSM的传递函数模型。依据该模型,设计了PID控制器与模型预测控制器(MPC),采用仿真和实验两种方式比较了两种控制器的效果。仿真结果表明,在受到阶跃扰动后,MPC控制器的恢复速度是PID控制器的45倍。在50 Hz正弦信号下,由于FSM的大惯量特点,PID控制器有严重的时滞,而MPC控制器能以1.224×10^-6″的误差稳定跟随。在噪声抑制方面,对实时加入10%幅值噪声的随机信号,MPC控制器的噪声抑制效果是PID控制器的13.3倍。实验结果表明,MPC控制器能以0.430″的误差稳定跟随50 Hz正弦信号,其跟踪精度是PID控制器的3.212倍,采用MPC控制器的快摆镜能满足快摆镜高带宽和高精度的需求。     

Digital repetitive learning controller for three-phase CVCF PWMinverter

In this paper, a plug-in digital repetitive leaning control scheme is proposed for three-phase constant-voltage constant-frequency (CVCF) pulsewidth modulation inverters to achieve high-quality sinusoidal output voltages. In the proposed control scheme, the repetitive controller (RC) is plugged into the stable one-sampling-ahead-preview-controlled three-phase CVCF inverter system using only two voltage sensors. The RC is designed to eliminate periodic disturbance and/or track periodic reference signal with zero tracking error, The design theory of plug-in repetitive learning controller is described systematically and the stability analysis or overall system is discussed. The merits of the controlled systems include features of minimized total harmonic distortion, robustness to parameter uncertainties, fast response, and fewer sensors. Simulation and experimental results are provided to illustrate the effectiveness of the proposed scheme     

MPC-based yaw stability control in in-wheel-motored EV via active front steering and motor torque distribution

This paper focuses on yaw stability control of in-wheel-motored electric vehicle (EV), and a model predictive controller is designed based on holistic control structure via active front steering and motor torque distribution. By designing a suitable reference model, the controller stabilizes a vehicle along the desired states while rejecting skid and fulfilling its physical constraints, so this is described as a constrained tracking problem. To solve this, the holistic control scheme is built to simplify the hierarchical structure of the controller and directly optimize the control inputs of system. Based on holistic control structure and MPC method, an objective function with constraints is designed over a receding horizon to meet the control requirements. Finally, the proposed nonlinear model predictive controller is evaluated on eight degrees of freedom (8DOF) EV model offline simulation platform. Simulation results of different road maneuver on slippery surfaces show the benefits of the control methodology used.     

Neural-network-based maximum-power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller

The photovoltaic (PV) generator exhibits a nonlinear V-I characteristic and its maximum power (MP) point varies with solar insolation. In this paper, a feedforward MP-point tracking scheme is developed for the coupled-inductor interleaved-boost-converter-fed PV system using a fuzzy controller. The proposed converter has lower switch current stress and improved efficiency over the noncoupled converter system. For a given solar insolation, the tracking algorithm changes the duty ratio of the converter such that the solar cell array voltage equals the voltage corresponding to the MP point. This is done by the feedforward loop, which generates an error signal by comparing the instantaneous array voltage and reference voltage corresponding to the MP point. Depending on the error and change of error signals, the fuzzy controller generates a control signal for the pulsewidth-modulation generator which in turn adjusts the duty ratio of the converter. The reference voltage corresponding to the MP point for the feedforward loop is obtained by an offline trained neural network. Experimental data are used for offline training of the neural network, which employs a backpropagation algorithm. The proposed peak power tracking effectiveness is demonstrated through simulation and experimental results. Tracking performance of the proposed controller is also compared with the conventional proportional-plus-integral-controller-based system. These studies reveal that the fuzzy controller results in better tracking performance.     

A neural-network-based controller for a single-link flexiblemanipulator using the inverse dynamics approach

This paper presents an intelligent-based control strategy for tip position tracking control of a single-link flexible manipulator. Motivated by the well-known inverse dynamics control strategy for rigid-link manipulators, two feedforward neural networks (NNs) are proposed to learn the nonlinearities of the flexible arm associated with the inverse dynamics controller. The redefined output approach is used by feeding back this output to guarantee the minimum phase behavior of the resulting closed-loop system. No a priori knowledge about the nonlinearities of the system is needed and the payload mass is also assumed to be unknown. The network weights are adjusted using a modified online error backpropagation algorithm that is based on the propagation of output tracking error, derivative of that error and the tip deflection of the manipulator. The real-time controller is implemented on an experimental test bed. The results achieved by the proposed NN-based controller are compared experimentally with conventional proportional-plus-derivative-type and standard inverse dynamics controls to substantiate and verify the advantages of our proposed scheme and its promising potential in identification and control of nonlinear systems     

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     

Coordinated motion control and event-based obstacle-crossing for four wheel-leg independent motor-driven robotic system

This work investigates the coordinated motion control and obstacle-crossing problem for a four wheel-leg independent motor-driven robotic system via a model predictive control (MPC) approach based on an event-triggering mechanism. The modeling of a wheel-leg robotic control system with a dynamic supporting polygon is organized. The system dynamic model is 3 degrees of freedom ignoring the pitch, roll, and vertical motions. The single wheel dynamic is analyzed considering the characteristics of the motor-driven and the Burckhardt nonlinear tire model. As a result, an over-actuated predictive model is proposed with the motor torques as inputs and the system states as outputs. As the supporting polygon is only adjusted at certain conditions, an event-based triggering mechanism is designed to save hardware resources and energy. The MPC controller is evaluated on a virtual prototype as well as a physical prototype. The simulation results guide the parameter tuning for the controller implementation in the physical prototype. The experimental results on these two prototypes verify the efficiency of the proposed approach.     

Optimal two-vector combination-based model predictive current control with compensation for PMSM drives

The duty-ratio-based model predictive control (D-MPC) is rapidly researched for permanent-magnet synchronous machine (PMSM) drives. Existing D-MPC methods produce large current ripple and distortion. To solve this issue and promote the system performance, an optimal two-vector combination MPC (OTC-MPC) is proposed for current control in this paper. The collection of the combination is firstly produced for the proposed OTC-MPC by combining the two vectors and corresponding duty-ratio, and then the optimal combination is selected among all feasible two-vector combinations, thus, the output vectors and duty-ratio are simultaneously optimised. The optimising process is simplified so that the proposed OTC-MPC can be easily implemented. Moreover, a simplified repetitive control with feed-forward compensation method is added to eliminate the predictive current errors of MPC, and also to improve the system robustness against external disturbances. Theoretical analysis, simulation and experimental results demonstrate that the proposed OTC-MPC effectively reduces current ripple and distortion while retaining fast dynamic response compared with the conventional D-MPC.     

A DSP-based single-phase AC power source

This paper presents the development of a single-phase AC power source, which is capable of generating high-quality sinusoidal waveforms with adjustable amplitudes and frequencies over a wide range. Moreover, various types of arbitrary waveforms can also be generated. The system consists essentially of a well-controlled single-phase pulsewidth modulated inverter. To perform tight closed-loop control of the inverter, a digital controller based on the generalized predictive control approach has been developed. The controller gains are determined by minimizing a cost function that reduces both the tracking error and the control signals. To evaluate the proposed approach, a digital-signal-processor-based experimental prototype has been constructed. Experimental results under various loading conditions have demonstrated that the system performs well     

Precise position control of shape memory alloy actuator using inverse hysteresis model and model reference adaptive control system

Position control of Shape Memory Alloy (SMA) actuators has been a challenging topic during the last years due to their nonlinearities in the governing physical equations as well as their hysteresis behaviors. Using the inverse of phenomenological hysteresis model in order to compensate the input–output hysteresis behavior of these actuators shows the effectiveness of this approach. In this paper, in order to control the tip deflection of a large deformation flexible beam actuated by an SMA actuator wire, a feedforward–feedback controller is proposed. The feedforward part of the proposed control system, maps the beam deflection into SMA temperature, is based on the inverse of the generalized Prandtl–Ishlinskii model. An adaptive model reference temperature control system is cascaded to the inverse hysteresis model in order to estimate the SMA electrical current for tracking the reference signal. In addition, a closed-loop proportional–integral controller with position feedback is added to the feedforward controller to increase the accuracy as well as eliminate the steady state error in position control process. Experimental results indicate that the proposed controller has great accuracy in tracking some square wave signals. It is also experimentally shown that the suggested controller has precise tracking performance in presence of environmental disturbances.     

Adaptive repetitive control to track variable periodic signals with fixed sampling rate

Recent research has shown that the repetitive control is very efficient in tracking periodic signals, where it is required that an integer number of samples in each period. However, in some industrial applications where the signal period varies but other requirements force a fixed sample rate, the number of samples per period may be a non-integer. To address this problem, this paper presents a new adaptive repetitive control, which deals with the non-integer samples per period due to the fixed sampling rate. The proposed adaptive repetitive control consists of two portions, the repetitive controller and nominal controller, where the former uses a fictitious sampler operating at a variable sample rate maintained at multiple times of the signal frequency, while the latter uses a fixed sampling rate. Interpolations are utilized to generate the fictitious samples required for the repetitive learning. The nearly perfect tracking was achieved for non-integer samples per period, when a simple linear interpolation is used. The error due to the interpolation is quantified, which is negligible to the residual tracking error. The comparison of the proposed and the existing schemes shows the significant improvement on the tracking performance. The experimental results on the control of a servomotor demonstrate the effectiveness of the proposed schemes.     

A novel AQM algorithm based on feedforward model predictive control

Developing feedforward model predictive controller as an active queue management (AQM) scheme is studied in this paper. MPC is an advanced control strategy for AQM. However, the conventional MPC is usually an implementable form of feedback MPC. In this paper, a feedforward and feedback optimal control law is presented. It is a clean, easily implementable, version of model predictive control that incorporates feedforward. Firstly, we use the nominal fluid model to design the feedforward control input so that the output tracks the given queue length with small error. Furthermore, in order to achieve robust performance and to reject the (unmeasured) disturbance, the feedback component is designed. In particular, a disturbance observer is incorporated into the prediction output in standard feedback MPC. This framework can significantly improve performance in the presence of measurement noise and certain types of model uncertainty. Finally, the simulation results show the effectiveness of FF‐AQM algorithm.     

Constrained Model Predictive Control of the Drive System With Mechanical Elasticity

In this paper, the application of model predictive control (MPC) for high-performance speed control and torsional vibration suppression in the drive system with flexible coupling is demonstrated. The control methodology presented in this paper relies on incorporating the drive's safety and physical limitations directly into the control problem formulation so that future constraint violations are anticipated and prevented. In order to reduce the computational complexity, the standard MPC controller is replaced by its explicit form. The resulting explicit controller achieves the same level of performance as the conventional MPC, but requires only a fraction of the real-time computational machinery, thus leading to fast and reliable implementation. The simulation results are confirmed by laboratory experiments.      

Robust PD-type iterative learning control for discrete systems with multiple time-delays subjected to polytopic uncertainty and restricted frequency-domain

This paper proposes the PD-type iterative learning control (ILC) for multiple time-delays systems with polytopic parameter uncertainty. Based on repetitive process framework, the system under study is equivalently converted into a class of uncertain repetitive processes with multiple time-delays. This approach accounts for effective inclusion of both time and trial domain objectives and hence some requirements on transient dynamics and trial-to-trial error convergence are incorporated for robust design procedures. Additionally, this approach can easily avoid the need for computation with very large dimensioned matrices as it is required for the lifting approach. Also, the proposed controller is designed with the generalized Kalman-Yakubovich-Popov lemma to ensure the monotonic trial-to-trial error convergence in finite frequency domain. This allows us to reduce the conservatism inherent to entire frequency range approaches since the reference signal spectrum reside in a known frequency range. Moreover, the sufficient conditions for the convergence of the resulting scheme are expressed by linear matrix inequalities and hence they are amenable to effective algorithmic solution. Finally, numerical simulations of different scenarios are presented to illustrate the effectiveness of the proposed method. In particular, to highlight the potential interest in PD-type ILC the robust tracking performance is compared with the results for P and D types of ILC.

     

Two-parameter robust repetitive control with application to a novel dual-stage actuator for noncircular Machining

This work presents a robust repetitive controller design for a novel dual-stage actuator system. The dual-stage actuator, which consists of an electrohydraulic actuator for 25-mm-gross motion and a piezoelectric actuator for 40-/spl mu/m fine motion, is designed for noncircular machining application. The controller is designed through a sequence of two single-input-single-output (SISO) designs by exploiting the triangular structure of the two by two actuator system dynamics. The tracking error from the first stage electrohydraulic actuator is used as reference for the second stage piezoelectric actuator. In this master-slave control arrangement, the overall sensitivity function is the product of two sensitivity functions from each actuator's servo loop. Thus, performance is improved at the frequencies where the sensitivity values are already well less than one. In the real-time control implementation, the effects of finite word length are analyzed and addressed via controller order reduction and realization. In an experiment of tracking an automotive cam profile at the rate of 10 cycles per second (600 rpm), the proposed dual-stage servo system generated tracking error of 4-/spl mu/m peak-to-valley and 0.80-/spl mu/m root-mean-square (RMS) value, showing a substantial improvement over the 16 micron peak-to-valley and 2.64-/spl mu/m RMS errors generated by the electrohydraulic servo system alone.