Control of ball mill grinding circuit using model predictive control scheme

Ball mill grinding circuits are essentially multivariable systems with high interaction among process variables. Traditionally grinding circuits are controlled by detuned multi-loop PI controllers that minimize the effect of interaction among the control loops. Detuned controllers generally become sluggish and a close control of the circuit is not possible. Model Predictive Controllers (MPC) can handle such highly interacting multivariable systems efficiently due to its coordinated approach. Moreover, MPC schemes can handle input and output constraints more explicitly and operation of the circuits close to their optimum operating conditions is possible. Control studies on a laboratory ball mill grinding circuit are carried out by simulation with detuned multi-loop PI controllers, unconstrained and constrained model predictive controllers and their performances are compared.     

Fractional order and BICO disturbance observers for a run-of-mine ore milling circuit

Grinding mill circuits are hard to control due to poor plant models, large external disturbances, uncertainties from internal couplings, and process variables that are difficult to measure. This paper proposes a novel fractional order disturbance observer (FO-DOB) for a run-of-mine (ROM) ore milling circuit. A fractional order low pass filter (Q-filer) is used in the DOB to offer an additional degree of freedom in tuning for set-point tracking performance and disturbance rejection performance. Another disturbance observer is introduced in which a Bode ideal cut-off (BICO) filter is used for the Q-filter. A full non-linear plant model is used for evaluation of the performance gained over the ubiquitous PI controller. The simulation results show that the FO-DOB and BICO-DOB schemes are useful additional tools for ROM ore milling circuit control implementations.     

MEMS近红外光谱仪微镜驱动控制系统研发

针对微机电系统（MEMS）近红外光谱仪中MEMS微镜驱动系统的耦合与复杂扰动问题,提出了一种基于扰动观测器（DOB）与模型预测控制（MPC）的复合控制结构。通过分析MEMS微镜的驱动工作原理,建立MEMS微镜偏转角与驱动电压的传递函数模型,设计了MPC以消除系统耦合,通过分析系统扰动模型,设计了DOB实现对系统内部与外部扰动的集中监测。仿真研究与实验测试结果表明：基于DOB MPC复合结构的MEMS微镜驱动控制系统,既可以有效抑制系统的外部扰动,又可以抑制由模型失配和变量耦合导致的内部扰动。     

A disturbance observer enhanced composite cascade control with experimental studies

The presence of strong disturbances usually causes great performance degradation of industrial process control systems. A disturbance observer (DOB) enhanced composite cascade control consisting of model predictive control (MPC), proportional-integral-derivative (PID) control, and DOB is proposed in this paper. DOB is employed here to estimate the severe disturbances and the estimated values are applied for feed-forward compensation, forming a composite control together with MPC. To evaluate the efficiency and validity of the proposed control structure, the simulation as well as experimental studies have been carried out for a level tank process which represents a typical first-order plus dead-time (FODT) industry process. Both the simulation and experimental results show that the proposed composite control method significantly improves the disturbance attenuation property of the MPC scheme in controlling such a typical industrial process.     

基于干扰观测器的不确定线性多变量系统控制

针对不确定线性多变量系统,提出一种基于干扰观测器(Disturbance observer,DOB)的保证系统内部稳定性的控制策略.以简单的结构和少量的计算代价实现鲁棒的控制效果,补偿外界干扰以及内部模型误差.通过引入一个闭环稳定性等价系统,辅助分析基于干扰观测器的多变量控制系统的内部稳定性,探讨在存在外部扰动及内部模型不确定性的情况下系统保证内部稳定的充分条件,指导外环控制器及内环干扰观测器中Q滤波器的设计.通过数值模型上的仿真验证了提出的控制方法的有效性.     

Active Vibration Control for a Flexible‐Link Manipulator with Input Constraint Based on a Disturbance Observer

This paper mainly focuses on designing an active vibration control for a flexible‐link manipulator in the presence of input constraint and unknown spatially infinite dimensional disturbances. The manipulator we studied can be taken as an Euler–Bernoulli beam, the dynamic model of which has the form of partial differential equations. As the existence of spatially infinite dimensional disturbances on the beam, we first design a disturbance observer to estimate infinite dimensional disturbances. The proposed disturbance observer is guaranteed exponentially stable. Then, taking input saturation into account, a novel disturbance‐observer‐based controller is developed to regulate the joint angular position and rapidly suppress vibrations on the beam, which is the main contribution of this study. The closed‐loop system is validated asymptotically stable by theoretical analysis. The effectiveness of the proposed scheme is demonstrated by numerical simulations.     

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

A dual closed‐loop tracking control is proposed for a wheeled mobile robot based on active disturbance rejection control (ADRC) and model predictive control (MPC). In the inner loop system, the ADRC scheme with an extended state observer (ESO) is proposed to estimate and compensate external disturbances. In the outer loop system, the MPC strategy is developed to generate a desired velocity for the inner loop dynamic system subject to a diamond‐shaped input constraint. Both effectiveness and stability analysis are given for the ESO and the dual closed‐loop system, respectively. Simulation results demonstrate the performances of the proposed control scheme.     

多变量解耦控制的工业过程运行层次控制方法

基于多变量解耦控制技术,提出了一种工业过程运行的层次控制方法,用于实现表征过程整体运行性能的工艺指标.底层回路控制系统采用多回路PI/PID控制技术进行设计,用于将关键工艺参数控制在给定的工作点.针对中被控过程和底层回路控制系统构成的文义对象,采用扩展的单位反馈解耦方法设计上层回路设定控制器,该回路设定控制器能够克服系...     

基于高阶观测器和干扰补偿控制的模型预测控制方法

针对状态不可测、外部干扰未知, 并且状态和输入受限的离散时间线性系统, 将高阶观测器、干扰补偿控制与标准模型预测控制(Model predictive control, MPC)相结合, 提出了一种新的MPC方法. 首先利用高阶观测器同步观测未知状态和干扰, 使得观测误差一致有界收敛;然后基于该干扰估计值设计新的干扰补偿控制方法, 并将该方法与基于状态估计的标准MPC相结合, 实现上述系统的优化控制. 所提出的MPC方法克服了利用现有MPC方法求解具有外部干扰和状态约束的优化控制问题时存在无可行解的局限, 能够保证系统状态在每一时刻都满足约束条件, 并且使系统的输出响应接近采用标准MPC方法控制线性标称系统时得到的输出响应. 最后, 将所提控制方法应用到船舶航向控制系统中, 仿真结果表明了所提方法的有效性和优越性.     

Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism

It is well known that the major cause of instability in industrial cement ball mills is the so-called plugging phenomenon. A novel neural network adaptive control scheme for cement milling circuits that is able to fully prevent the mill from plugging is presented. Estimates of the one-step-ahead errors in control signals are calculated through a neural predictive model and used for controller tuning. A robust on-line learning algorithm, based on sliding mode control (SMC) theory is applied to both: to the controller and to the model as well. The proposed approach allows handling of mismatches, uncertainties and parameter changes in the model of the mill. The simulation results from indicate that both the neural model and the controller inherit the major advantages of SMC, i.e. robustness. Furthermore, learning is achieved in a rapid manner.