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 model predictive current control strategy for three-phase voltage source PWM rectifiers

Traditional model predictive control (MPC) strategy is highly dependent on the model and has poor robustness. To solve the problems, this paper proposes a robust model predictive current control strategy based on a disturbance observer. According to the current predictive model of three-phase voltage source PWM rectifiers (VSR), voltage vectors were selected by minimizing current errors in a fixed time interval. The operating procedure of the MPC scheme and the cause of errors were analysed when errors existed in the model. A disturbance observer was employed to eliminate the disturbance generated by model parameters mismatch via feed-forward compensation, which strengthened the robustness of the control system. To solve the problem caused by filter delay in MPC control, an improved compensation algorithm for the observer was presented. Simulation and experimental results indicate that the proposed robust model predictive current control scheme presents a better dynamic response and has stronger robustness compared with the traditional MPC.     

Sparse-grid-based adaptive model predictive control of HL60 cellular differentiation

Quantitative methods such as model-based predictive control are known to facilitate the design of strategies to manipulate biological systems. This study develops a sparse-grid-based adaptive model predictive control (MPC) strategy to direct HL60 cellular differentiation. Sparse-grid sampling and interpolation support a computationally efficient adaptive MPC scheme in which multiple data-consistent regions of the model parameter space are identified and used to calculate a control compromise. The algorithm is evaluated in silico with structural model mismatch. Simulations demonstrate how the multiscenario control strategy more effectively manages the mismatch compared to a single scenario approach. Furthermore, the controller is evaluated in vitro to differentiate HL60 cells in both normal and perturbed environments. The controller-derived input sequence successfully achieves and sustains the specified target level of granulocytes when implemented in the laboratory. The results and analysis given here imply that adoption of this experiment planning technique to direct cell differentiation within more complex tissue engineered constructs will require the use of a reasonably accurate mathematical model and an extension of this algorithm to multiobjective controller design.     

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.     

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.     

2R柔性机械臂压电抑振的模糊控制实验研究

柔性机械臂在运动过程中不可避免产生弹性振动,对柔性机械臂的振动抑制成为重要研究课题.基于压电陶瓷的模糊控制抑振,利用电阻应变传感器检测机械臂振动信息,经动态应变仪采集到计算机系统,根据控制规律进行模糊运算,得到控制电压;再以该电压驱动压电陶瓷片,产生与振动方向相反控制力矩,抑制机械臂柔性变形.实验结果显示,压电抑振效果显著,模糊控制的鲁棒性较高.     

Real-time predictive control strategy for a plug-in hybrid electric powertrain

Model predictive control is a promising approach to exploit the potentials of modern concepts and to fulfill the automotive requirements. Since, it is able to handle constrained multi-input multi-output optimal control problems. However, when it comes to implementation, the MPC computational effort may cause a concern for real-time applications. To maintain the advantage of a predictive control approach and improve its implementation speed, we can solve the problem parametrically. In this paper, we design a power management strategy for a Toyota Prius plug-in hybrid powertrain (PHEV) using explicit model predictive control (eMPC) based on a new control-oriented model to improve the real-time implementation performance. By implementing the controller to a PHEV model through model and hardware-in-the-loop simulation, we get promising fuel economy as well as real-time simulation speed.     

Active queue management algorithm based on data-driven predictive control

Model predictive control (MPC) is a popular strategy for active queue management (AQM) that is able to incorporate physical and user defined constraints. However, the current MPC methods rely on explicit fluid model of TCP behavior with input time delay. In this paper, we propose a novel AQM algorithm based on data-driven predictive control, called Data-AQM. For Internet system with large delay, complex change and bad disturbance, data-driven predictive controller can be obtained directly based on the input–output data alone and does not require any explicit model of the system. According to the input–output data, the future queue length in data buffer, which is the basis of optimizing drop probability, is predicted. Furthermore, considering system constraints, the control requirement is converted to the optimal control objective, then the drop probability is obtained by solving the optimal problem online. Finally, the performances of Data-AQM are evaluated through a series of simulations.     

Embedded explicit model predictive vibration control

This article presents an embedded active vibration suppression system featuring real-time explicit model predictive control (EMPC) that is implemented on a microcontroller unit (MCU). The EMPC controller minimizes the tip deflection of an aluminum cantilever beam driven by piezoceramic actuators, gaining its feedback from direct position measurements. The output and input performance of the EMPC method is compared to an analogously tuned positive position feedback (PPF) controller. An extensive analysis is provided on the cycle timing and memory needs of the explicit predictive vibration control scheme. The results demonstrate that the EMPC controller may achieve the same vibration suppression results compared to PPF with less input effort, while inherently respecting process constraints. Furthermore, we show that EMPC task execution timing is comparable in the random access memory (RAM) and read only memory (ROM) alternatives, suggesting that numerous current microcontrollers are suitable for EMPC-based active vibration control, in case the prediction model is kept simple.     

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.      

Kalman filter and LQ based speed controller for torsional vibrationsuppression in a 2-mass motor drive system

In this paper, a high-performance speed control for torsional vibration suppression in a 2-mass motor drive system, like a rolling mill which has a long shaft and large loadside mass or a robot arm which has flexible coupling, was studied. The speed control method which has better control response than a typical one in command following, torsional vibration suppression, disturbance rejection, and robustness to parameter variation, was proposed. The performance of command following, torsional vibration suppression, and robustness to parameter variation was satisfied by using a Kalman filter and LQ based speed control with an integrator. Also, disturbance rejection performance was improved through load torque compensation. Through various experiments of a real 22 kW field oriented controlled AC motor drive system having 2-mass mechanical system, the characteristics of the proposed speed controller and typical PI speed controller were compared and analyzed     

Hybrid learning control schemes with input shaping of a flexible manipulator system

The paper describes a practical approach to investigate and develop a hybrid iterative learning control scheme with input shaping. An experimental flexible manipulator rig and corresponding simulation environment are used to demonstrate the effectiveness of the proposed control strategy. A collocated proportional-derivative (PD) controller utilizing hub-angle and hub-velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate iterative learning control with acceleration feedback and genetic algorithms (GAs) for optimization of the learning parameters and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. The system performance with the controllers is presented and analysed in the time and frequency domains. The performance of the hybrid learning control scheme with input shaping is assessed in terms of input tracking and level of vibration reduction. The effectiveness of the control schemes in handling various payloads is also studied.     

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。     

A New Cooperative Distributed MPC Method Based on Reduction and Classification

To decrease the overlarge calculation in-duced by the centralized processing, a new cooperative dis-tributed Model predictive control (MPC) method is pro-posed for large-scale systems with coupled dynamics. Re-duction and classification are investigated by defining the influence degree to reduce the whole system and then to classify the reduced system into several subsystem groups. These groups are mutually decoupled, while there is rel-ativity between these subsystems comprised in the same group. Centralized/cooperative and distributed MPC algo-rithms for each group are implemented to ensure the feasi-bility and the stability of the whole system. Meanwhile, for practical applications, the finite times interactive control strategy between different groups is adopted to compen-sate information loss brought by the reduced subsystem and realize the global cooperative distributed MPC. This algorithm significantly decreases the computational load, has better control performance. Simulations are given to illustrate the effectiveness of these developed algorithms.     

Model predictive control of bidirectional isolated DC–DC converter for energy conversion system

Model predictive control (MPC) is a powerful and emerging control algorithm in the field of power converters and energy conversion systems. This paper proposes a model predictive algorithm to control the power flow between the high-voltage and low-voltage DC buses of a bidirectional isolated full-bridge DC–DC converter. The predictive control algorithm utilises the discrete nature of the power converters and predicts the future nature of the system, which are compared with the references to calculate the cost function. The switching state that minimises the cost function is selected for firing the converter in the next sampling time period. The proposed MPC bidirectional DC–DC converter is simulated with MATLAB/Simulink and further verified with a 2.5 kW experimental configuration. Both the simulation and experimental results confirm that the proposed MPC algorithm of the DC–DC converter reduces reactive power by avoiding the phase shift between primary and secondary sides of the high-frequency transformer and allow power transfer with unity power factor. Finally, an efficiency comparison is performed between the MPC and dual-phase-shift-based pulse-width modulation controlled DC–DC converter which ensures the effectiveness of the MPC controller.     

Fault-tolerant control system of flexible arm for sensor fault by using reaction force observer

In recent years, control system reliability has received much attention with increase of situations where computer-controlled systems such as robot control systems are used. In order to improve reliability, control systems need to have abilities to detect a fault (fault detection) and to maintain the stability and the control performance (fault tolerance). In this paper, we address the vibration suppression control of a one-link flexible arm robot. Vibration suppression is realized by an additional feedback of a strain gauge sensor attached to the arm besides motor position. However, a sensor fault (e.g., disconnection) may degrade the control performance and make the control system unstable at its worst. In this paper, we propose a fault-tolerant control system for strain gauge sensor fault. The proposed control system estimates a strain gauge sensor signal based on the reaction force observer and detects the fault by monitoring the estimation error. After fault detection, the proposed control system exchanges the faulty sensor signal for the estimated one and switches to a fault-mode controller so as to maintain the stability and the control performance. We apply the proposed control system to the vibration suppression control system of a one-link flexible arm robot and confirm the effectiveness of the proposed control system by some experiments.     

Robust controller design for PTP motion of vertical XY positioning systems with a flexible beam

This paper presents a point-to-point (PTP) motion control method for accurate positioning and vibration suppression of a vertical XY positioning system with a flexible beam. The proposed method is composed of a feedforward and feedback controller. The input preshaping based on the analytic modeling and frequency equation of the system is proposed as a feedforward controller to produce the desired responses. The feedback controller based on a robust internal-loop compensator is designed to meet the specified performance and to stabilize the whole system in the presence of uncertainties and disturbances. By integrating the input preshaping controller and feedback controller, it is shown that the system is stable and the vibration of the flexible beam is suppressed. The proposed algorithm is demonstrated experimentally on an XY positioning system which consists of a base cart, elastic beam and moving mass.     

Vibration control of a single-link flexible manipulator using an array of fiber optic curvature sensors and PZT actuators

This paper presents a novel distributed sensing and actuation approach for actively suppressing vibrations within flexible link manipulators. Through vibration suppression, the method acts to regulate the shape of flexible links and, consequently, improves the performance of any independent trajectory controller being employed over the manipulator joints. To demonstrate the approach, a series of piezoceramic actuators (PZTs) are bonded to the surface of a single-link flexible manipulator. Slewing of the flexible link induces vibrations in the link that persist long after the hub stops rotating. The vibration suppression is achieved through a combined scheme of PD-based hub motion control and a PZT actuator controller that is a composite of linear and angular velocity feedback controllers. A Lyapunov approach is used to synthesize the composite controller, and a unique, commercially-available sensor, called ShapeTape^?, that provides the linear and angular velocity feedback. The sensor array is comprised of a series of fiber optic curvature sensors that are laminated on a long, thin ribbon tape which can be embedded into the flexible link and measures the bend and twist of the link’s centerline. Simulation and experimental results show the effectiveness of the proposed approach and the ability of the new sensor to provide the requisite feedback.     

采用预测控制进行纵向塞曼幅值热稳频的研究

产生纵向塞曼频率分裂的He—Ne激光器输出左、右旋圆偏振光,其光强差和激光频率呈稳定的调谐曲线。装置利用单片机采集幅值信号,采用预测控制理论进行数字调节,输出PWM(PulseWidth Modulation)信号补偿激光管的温度以实现热稳频控制。为实现对热稳频大滞后、非线性这一复杂系统的高质量控制,提出了预测控制方法,该方法克服了传统PID(Proportional IntegralDifferential)调节中存在的调整工作量大,控制后果不能及时反馈的不足,适用于各种热稳频过程。数字仿真和实验验证预测控制是稳定的,对模型误差的适应能力强。对10s采样周期,估算得系统频率稳定度达6.7×10~(-9)。     

压电膜在振动控制中的应用及有限元分析

介绍了压电现象的基本原理及其在柔性结构主动减振中的应用，回顾了以往有限元法用于柔性结构振动主动控制分析的主要研究工作，提出了今后需要解决的问题。