Inversion-based feedforward and reference signal design for fractional constrained control systems

In this paper we propose a solution for the synthesis of a feedforward control action for a fractional control system. In particular, an input–output inversion based methodology is devised in order to determine the open-loop signal that provides a predefined process variable transition from a steady-state value to another. The transition time is then minimized subject to constraints on the process and control variables and their derivatives. Simulation results show the effectiveness of the technique.     

应用模型预测控制的混合动力汽车模式切换动态协调控制

利用传统协调控制策略或模型预测控制(MPC)方法能够解决离合器模式切换的平顺性,但其改善效果不显著,且缺乏深入的细化研究.因此,为了改进混合动力汽车有离合器结合的模式切换过程中的平顺性,本文基于MPC制定有离合器模式切换过程的动态协调控制策略.在对混合动力系统有离合器模式切换模型进行简化的基础上,开展MPC在模式切换动态协调控制过程的原理描述,以减小有离合器模式间切换的冲击度进行基于MPC动态协调控制策略设计,并对不同权重下的冲击度进行了详细的对比.通过实验验证,其结果表明采用MPC的模式切换协调控制最大冲击度从26.3 m/s^3下降至9.26 m/s^3,降低了64.8%,明显的抑制了模式切换过程中的冲击度,有效的改善了模式切换的平顺性.     

基于控制周期计算的锌液净化除铜过程优化控制

净化除铜过程是锌直接浸出冶炼工艺中硫酸锌溶液净化的第1个步骤,其效果直接影响着后续工序的正常进行以及最终锌产品的质量.本文针对除铜过程中控制周期与锌粉添加量不确定性,造成出口铜离子浓度波动大及锌粉浪费等问题,研究基于控制周期的除铜过程锌粉添加优化控制方法.首先通过分析反应器中氧化还原电位(oxidation-reduction-potential,ORP)与锌粉的时序变化关系计算除铜过程反应响应时间,然后分析响应时间的统计特性,从而确定锌粉控制周期.在确定控制周期的基础上,采用基于控制周期的固定节点控制参数化方法,从而将最优控制求解问题转化为非线性规划问题.最后,采用状态转移算法对该非线性规划问题进行求解.采集工业现场数据进行实验证明了该优化控制方法的有效性,为类似的净化过程的优化控制提供了新思路.     

飞机座舱压力专家模糊预控方法仿真研究

针对新型战机高空高速俯冲及俯冲转平飞情况下带来的座舱压力控制难题,提出一种飞机座舱压力专家模糊PID预控方法。在飞机高速俯冲时,基于压力调节系统时间延迟及飞机高度变化率改进常规模糊PID控制策略并提高座舱压力调整速度;在飞机状态转换时,利用专家控制器根据知识库及状态转换时间预测调整模糊PID控制策略,并引入重置机制以改善调整性能。经过知识库的动态学习,得出飞机状态转换时,采用模糊PID控制、模糊预控、重置PID控制参数的专家控制策略具有最佳的控制效果的结论。通过仿真实验验证了该方法的有效性。     

仿生假手抓握力控制策略

为了使仿生假手完成各种精细作业,提出一种抓握力控制策略.在自由空间和约束空间中分别使用基于位置的阻抗控制和力跟踪阻抗控制.在过渡过程中使用模糊观测器切换控制模式.两种控制模式采用同一个基于位置的阻抗控制器,在约束空间向阻抗控制器中引入参考力,以满足约束空间的抓握力控制要求.这种方法可以使关节在自由空间和约束空间中分别实现良好的轨迹跟踪和力矩跟踪,在过渡过程中实现控制模式的可靠切换和系统的稳定过渡.提出一种自适应滑模摩擦力补偿方法,利用终端滑模思想设计了滑模函数,使得系统跟踪误差在有限时间内收敛,避免了传统线性滑模面状态跟踪误差无法在有限时间内收敛至0的问题.根据指数形式摩擦力的特点,利用终端滑模控制思想获得包含摩擦力参数估计的滑模控制律,并基于李亚普诺夫稳定性定理推导了估计参数的在线自适应律.对该抓握力控制策略在HIT假手上进行了抓取实验,实验结果证明了控制策略的有效性.     

A comparative study of nonlinear optimization and Taguchi methods applied to the intelligent control of manufacturing processes

current investigation focused on neural-network-based control of manufacturing processes utilizing an optimization scheme. In an earlier study, Demirci and Coulter introduced the utilization of neural networks for the intelligent control of molding processes. In that study, a forward model neural network, employed with a search strategy based on the factorial design of experiments method, was shown to successfully control the flow progression during injection molding processes. Recently, Demirciet al. showed that the search mechanism based on the factorial design of experiments method can be intolerable in time during on-line control of manufacturing processes, and suggested an inverse model neural network. This inverse model neural network was shown to be beneficial as it totally eliminated time-consuming parameter searches, but it required a harder mapping than the forward model neural network and thus its performance was inferior. In the present study, the authors investigated two different optimization methods that were utilized in making the search method of the forward control scheme more efficient. The first method was Taguchi's method of parameter design, and the second method was a nonlinear optimization method known as Nelder and Mead's downhill simplex method. These two methods were separately utilized in creating an efficient search method to be used with the forward model neural network. The performance of the resulting two control methods was compared with each other as well as with that of the forward control scheme utilizing a search strategy based on the factorial design of experiments method. Although the applications in this study were on molding processes, the method can be applied to any manufacturing process for which a process model and anin-situ sensing scheme exists.     

ILC strategy for progress improvement of economic performance in industrial model predictive control systems

A novel approach to progress improvement of the economic performance in model predictive control (MPC) systems is developed. The conventional LQG based economic performance design provides an estimation which cannot be done by the controller while the proposed approach can develop the design performance achievable by the controller. Its optimal performance is achieved by solving economic performance design (EPD) problem and optimizing the MPC performance iteratively in contrast to the original EPD which has nonlinear LQG curve relationship. Based on the current operating data from MPC, EPD is transformed into a linear programming problem. With the iterative learning control (ILC) strategy, EPD is solved at each trial to update the tuning parameter and the designed condition; then MPC is conducted in the condition guided by EPD. The ILC strategy is proposed to adjust the tuning parameter based on the sensitivity analysis. The convergence of EPD by the proposed ILC has also been proved. The strategy can be applied to industry processes to keep enhancing the performance and to obtain the achievable optimal EPD. The performance of the proposed method is illustrated via an SISO numerical system as well as an MIMO industry process.     

基于等离子流动控制的翼型减阻技术

为研究基于等离子流动控制的减阻技术,基于Langtry-Menter转捩模型提出边界层转捩数值模拟技术.该技术可有效结合转捩模型与湍流模型,用标准模型验证其精确性,为采用等离子流动控制抑制边界层分离和转捩研究提供数值模拟平台.采用基于现象学模型的等离子流动控制数值模拟技术,对流动分离以及边界层转捩抑制进行数值模拟,为基...     

Nonlinear one-step-ahead control using neural networks: Control strategy and stability design

A nonlinear one-step-ahead control strategy based on a neural network model is proposed for nonlinear SISO processes. The neural network used for controller design is a feedforward network with external recurrent terms. The training of the neural network model is implemented by using a recursive least-squares (RLS)-based algorithm. Considering the case of the nonlinear processes with time delay, the extension of the mentioned neural control scheme to d-step-ahead predictive neural control is proposed to compensate the influence of the time-delay. Then the stability analysis of the neural-network-based one-step-ahead control system is presented based on Lyapunov theory. From the stability investigation, the stability condition for the neural control system is obtained. The method is illustrated with some simulated examples, including the control of a continuous stirred tank reactor (CSTR).     

Two-sensor control in active vibration isolation using hard mounts

To isolate precision machines from floor vibrations, active vibration isolators are often applied. In this paper, a two-sensor control strategy, based on acceleration feedback and force feedback, is proposed for an active vibration isolator using a single-axis active hard mount. The hard mount provides a stiff support while an active control system is used to get the desired isolation performance. In our previous work, we showed that a sensor fusion control strategy for active hard mounts can be used to realize three performance objectives simultaneously: providing isolation from floor vibrations, achieving a low sensitivity for direct disturbance forces, and adding damping to internal modes of the supported precision machine. In the present work, an enhanced control strategy is presented, referred to as two-sensor control. We will show that two-sensor control outperforms sensor fusion, because it has more possibilities for loop-shaping and has better stability properties. The two-sensor control strategy is successfully validated on an experimental setup.     

Event‐triggered finite‐time reliable control for nonhomogeneous Markovian jump systems

This article investigates the event‐triggered finite‐time reliable control problem for a class of Markovian jump systems with time‐varying transition probabilities, time‐varying actuator faults, and time‐varying delays. First, a Luenberger observer is constructed to estimate the unmeasured system state. Second, by applying an event‐triggered strategy from observer to controller, the frequency of transmission is reduced. Third, based on linear matrix inequality technique and stochastic finite‐time analysis, event‐triggered observer‐based controllers are designed and sufficient conditions are given, which ensure the finite‐time boundedness of the closed‐loop system in an H_∞ sense. Finally, an example is utilized to show the effectiveness of the proposed controller design approach.     

Neural network control of multivariable processes with a fast optimisation algorithm

A radial basis function (RBF) neural network model based predictive control scheme is developed for multivariable nonlinear systems in this paper. A fast convergence algorithm is proposed and employed in multidimensional optimisation in the control scheme to reduce the computing time and save required computer memory. The scheme is applied to a simulated two-input two-output nonlinear process for set-point tracking control. Simulation results demonstrate the effectiveness of the control strategy and the fast learning algorithm for multivariable non-linear processes. Comparison of the performance with PID control is included.     

Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach

Most industrial processes exhibit inherent nonlinear characteristics. Hence, classical control strategies which use linearized models are not effective in achieving optimal control. In this paper an Artificial Neural Network (ANN) based reinforcement learning (RL) strategy is proposed for controlling a nonlinear interacting liquid level system. This ANN-RL control strategy takes advantage of the generalization, noise immunity and function approximation capabilities of the ANN and optimal decision making capabilities of the RL approach. Two different ANN-RL approaches for solving a generic nonlinear control problem are proposed and their performances are evaluated by applying them to two benchmark nonlinear liquid level control problems. Comparison of the ANN-RL approach is also made to a discretized state space based pure RL control strategy. Performance comparison on the benchmark nonlinear liquid level control problems indicate that the ANN-RL approach results in better control as evidenced by less oscillations, disturbance rejection and overshoot.     

State and mode feedback control strategy for discrete‐time Markovian jump linear systems with time‐varying controllable mode transition probability matrix

In this article, the state and mode feedback control strategy is investigated for the discrete‐time Markovian jump linear system (MJLS) with time‐varying controllable mode transition probability matrix (MTPM). This strategy, consisting of a state feedback controller and a mode feedback controller, is proposed to ensure MJLS's stability and meanwhile improve system performance. First, a mode‐dependent state feedback controller is designed to stabilize the MJLS based on the time‐invariant part of the MTPM such that it can still keep valid even if the MTPM is adjusted by the mode feedback control. Second, a generalized quadratic stabilization cost is put forward for evaluating MJLS's performance, which contains system state, state feedback controller, and mode feedback controller. To reduce the stabilization cost, a mode feedback controller is introduced to adjust each mode's occurrence probability by changing the time‐varying controllable part of MTPM. The calculation of such mode feedback controller is given based on a value‐iteration algorithm with its convergence proof. Compared with traditional state feedback control strategy, this state and mode feedback control strategy offers a new perspective for the control problem of general nonhomogeneous MJLSs. Numerical examples are provided to illustrate the validity of the proposed strategy.     

Linearizing feedforward–feedback control of pH processes based on the Wiener model

This paper examines the control of pH processes based on the Wiener model construct (a dynamic linear element representing the mixing dynamics of the process in series with a static nonlinearity representing the titration curve). Conditions under which the pH process behaves like an exact Wiener system are examined. Linearization by output transformation using both the true inverse of the titration curve and an estimate of the inverse is employed to make the pH process appear linear enabling the application of a linear feedback (PI) controller. Although many others have utilized an identified nonlinearity for linearizing feedback control of pH processes, much less work has been done on using the nonlinearity for linearizing feedforward control. Here, a simple linearizing feedforward controller is proposed based on a current estimate of the inverse titration curve. Simulated closed-loop results demonstrate the superiority of the linearizing feedforward–feedback strategy versus linearizing feedback only, when the inverse titration curve is accurately estimated.     

Nonlinear system identification and control of chemical processes using fast orthogonal search

The use of the fast orthogonal search (FOS) method is presented for model estimation and control of nonlinear chemical processes. FOS provides a nonlinear approximation used in an inner-loop that allows for simpler linear control methods to be used as an outer-loop controller. It is a straightforward, simple-to-use method for linearization of systems based on orthogonal system identification. The control concept is derived from the method of inverse dynamic control (IDC). The novel combination of this with the FOS method of system identification results in a very efficient and effective method of control. The method is demonstrated and tested on two nonlinear chemical process control simulations and the results are shown to compare very favourably to published results on the same problems.     

Model predictive functional control for processes with unstable poles

In this paper the coprime‐factorized model predictive functional control for single‐input single‐output processes with an arbitrary number of unstable poles is presented. The predictive functional control algorithm gives a framework for designing the control for a wide range of processes. The main idea in the case of unstable poles is based on the prediction of the process output based on the coprime‐factorized process model. The robust stability of the proposed control algorithm is also discussed, using the small‐gain theorem, which provides a sufficient condition for stability. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

多变量有约束过程的协调预测控制策略

针对多变量约束过程，提出了基于关联分析的协调预测控制策略，从而对系统控制性能和操作变量理想工作区起到了很好的协调作用，减少了在线控制计算工作量。     

改进的动态矩阵控制算法在发酵罐温度控制中的应用

啤酒发酵是一类复杂的生化反映过程,其温度控制具有大时滞特性。由于机制复杂、环境多变,温度对象难以建立精确的数学模型,常规控制方式难以胜任此类系统的控制,并且当存在不可预测的干扰时,控制效果更难保障。针对这一问题,以啤酒发酵罐的温度为控制对象,将动态矩阵控制(DMC)引入该温度控制,基于DMC一步控制的思想,引入时间最优控制对DMC控制量进行改进,形成快速响应的预测控制算法。应用结果表明该算法有效提高了系统对干扰的抑制能力,具有较好的应用价值。     

The optimal robust control policy for uncertain semi-Markov control processes

The optimization problems of Markov control processes (MCPs) with exact knowledge of system parameters, in the form of transition probabilities or infinitesimal transition rates, can be solved by using the concept of Markov performance potential which plays an important role in the sensitivity analysis of MCPs. In this paper, by using an equivalent infinitesimal generator, we first introduce a definition of discounted Poisson equations for semi-Markov control processes (SMCPs), which is similar to that for MCPs, and the performance potentials of SMCPs are defined as solution of the equation. Some related optimization techniques based on performance potentials for MCPs may be extended to the optimization of SMCPs if the system parameters are known with certainty. Unfortunately, exact values of the distributions of the sojourn times at some states or the transition probabilities of the embedded Markov chain for a large-scale SMCP are generally difficult or impossible to obtain, which leads to the uncertainty of the semi-Markov kernel, and thereby to the uncertainty of equivalent infinitesimal transition rates. Similar to the optimization of uncertain MCPs, a potential-based policy iteration method is proposed in this work to search for the optimal robust control policy for SMCPs with uncertain infinitesimal transition rates that are represented as compact sets. In addition, convergence of the algorithm is discussed.