Robust control performance of time-varying human controller models

Previous models for human operator control performance have exhibited stochastic characteristics. Even with a repetitive task and a highly motivated and trained subject, there is clear variability of output (control actions). This variability, termed remnant, has been previously modeled as observation and motor noise associated with the operator. Recently, a new approach was taken: remnant was modeled as a time-varying operator characteristic associated with changes in the relative weighting of various performance measures, together with lack of understanding of the dynamics of the plant being controlled. The present work extends this concept and shows that the performance of time-varying human controller models is very robust with respect to changes in the time variability of the operator model. The results are particularly heartening in view of the inherent difficulties associated with modeling human control performance.     

Nonlinear predictive controller based on S-PARAFAC Volterra models applied to a communicating two-tank system

This paper proposes a new predictive controller approach for nonlinear process based on a reduced complexity homogeneous, quadratic discrete-time Volterra model called quadratic S-PARAFAC Volterra model. The proposed model is yielded by using the symmetry property of the Volterra kernels and their tensor decomposition using the PARAFAC technique that provides a parametric reduction compared to the conventional Volterra model. This property allows synthesising a new nonlinear-model-based predictive control (NMBPC). We develop the general form of a new predictor, and therefore, we propose an optimisation algorithm formulated as a quadratic programming under linear and nonlinear constraints. The performances of the proposed quadratic S-PARAFAC Volterra model and the developed NMBPC algorithm are illustrated on a numerical simulation and validated on a benchmark as a continuous stirred-tank reactor system. Moreover, the efficiency of the proposed quadratic S-PARAFAC Volterra model and the NMBPC approach are validated on an experimental communicating two-tank system.     

基于分段线性化Hammerstein模型的自校正控制器设计

许多实际系统可以表示为不连续非线性块状结构模型,其不连续非线性部分常采用符号函数参数化,该处理方法适用于递推参数辨识,但自适应控制器的设计较为困难。鉴于此,针对一类含有不连续非线性环节的Hammerstein模型,采用一系列线性分段函数参数化不连续非线性环节,提出自校正控制方法。根据线性分段函数的逆函数特性,求解自适应控制律。理论分析证明了闭环系统的稳定性,仿真结果验证了所提出方法的有效性。     

基于神经网络和多模型的非线性离散自适应控制

针对一类非线性离散时间单变量系统,提出了基于多模型切换策略的非线性自适应控制方法.首先将被控系统划分为多个工作区间,然后在每个工作区间内建立1个线性自适应控制器和1个非线性神经网络自适应控制器.线性控制器可以保证系统的稳定性,神经网络非线性控制器可以有效的改善系统的暂态性能,采用有效的切换策略可以在保证系统稳定的情况下很好的改善系统的性能.仿真结果验证了所提出方法的有效性.     

一种基于遗传算法的非线性PID控制器

基于PID控制器各增益参数与偏差信号之间呈现非线性关系,拟合各参数的非线性函数可分别对控制器的P／I／D各部分实施单独调节的思想,提出根据控制与误差之间的调节规律,给定一组增益参数的非线性函数,并采用遗传算法来优化和构造此非线性PID调节器．典型系统的仿真结果表明,该控制器可在一定程度上兼顾系统的动态和静态性能．     

基于性能诊断的预测控制器参数整定策略

针对多变量模型预测控制系统在长期运行中出现设定值变化、模型失配、扰动特性变化后控制器参数不再匹配的问题,提出一种在得到性能诊断结果后基于改进粒子群算法的控制器参数整定方法.通过分析最优控制律与三项系统性能的关系,构造出对应的目标优化函数,对粒子群算法迭代过程中粒子的位置和惯性因子做出改进,弥补该算法易于陷入局部最优以及...     

一类时变系统的多模型切换动态调节控制算法

当被控对象参数所在的不确定区域未知时,多模型切换调节等控制算法虽然最终可以使系统达到期望特性,但随着不确定区域的增大,系统需要增加大量的模型才能满足快速性等方面上的要求.因此,为了实现一类时变系统在只增加少量模型时便能够满足期望控制性能的目标,提出了多模型切换动态调节控制算法.此算法在不确定区域内分配若干上层模型,在双切换机制的监督下,利用动态分配方法,在最优上层模型所在的子区域分配出若干下层模型,在与自适应模型的协作中,设计最优控制器,保证了系统的暂态和稳态响应.在数值仿真与故障卫星系统的仿真研究中,结果表明了多模型切换动态调节算法在暂态性能方面优于多模型切换调节算法,并验证了此算法的有效性.     

基于最大-最小蚂蚁系统的PID参数整定

PID参数是影响PID控制器控制效果的重要参数。本文提出一种基于最大-最小蚂蚁系统(MMAS)进行PID参数整定的新型算法MPID,并给出了MPID算法的具体实现步骤。实验仿真表明,MPID算法与基于遗传算法、基本蚁群算法的PID整定方法相比,优化效果有明显改善,说明了该算法的可行性和优越性。     

PID-type fuzzy logic controller tuning based on particle swarm optimization

In this paper, a new PID-type fuzzy logic controller (FLC) tuning strategy is proposed using a particle swarm optimization (PSO) approach. In order to improve further the performance and robustness properties of the proposed PID-fuzzy approach, two self-tuning mechanisms are introduced. The scaling factors tuning problem of these PID-type FLC structures is formulated and systematically resolved, using a proposed constrained PSO algorithm. The case of an electrical DC drive benchmark is investigated, within a developed real-time framework, to illustrate the efficiency and superiority of the proposed PSO-based fuzzy control approaches. Simulation and experimental results show the advantages of the designed PSO-tuned PID-type FLC structures in terms of efficiency and robustness.     

Robust PID controller tuning based on the heuristic Kalman algorithm

This paper presents a simple but effective tuning strategy for robust PID controllers satisfying multiple performance criteria. Finding such a controller is known to be computationally intractable via the conventional techniques. This is mainly due to the non-convexity of the resulting control problem which is of the fixed order/structure type. To solve this kind of control problem easily and directly, without using any complicated mathematical manipulations and without using too many “user defined” parameters, we utilize the heuristic Kalman algorithm (HKA) for the resolution of the underlying constrained non-convex optimization problem. The resulting tuning strategy is applicable both to stable and unstable systems, without any limitation concerning the order of the process to be controlled. Various numerical studies are conducted to demonstrate the validity of the proposed tuning procedure. Comparisons with previously published works are also given.     

A new PID controller tuning method based on multiple integrations

The magnitude optimum (MO) technique provides a non-oscillatory closed-loop response for a large class of process models. However, this technique is based on a transfer function model that requires precise process identification and extensive computations. In the present paper, it is shown that a close relation exists between multiple integrations of the process step response and the MO criterion. Due to this relation, the MO criterion can be more simply achieved. Some practical guidelines for performing multiple integrations and for re-tuning controller parameters are also given.     

Robust PID controller tuning based on the constrained particle swarm optimization

This paper proposes a novel tuning strategy for robust proportional-integral-derivative (PID) controllers based on the augmented Lagrangian particle swarm optimization (ALPSO). First, the problem of PID controller tuning satisfying multiple H_∞ performance criteria is considered, which is known to suffer from computational intractability and conservatism when any existing method is adopted. In order to give some remedy to such a design problem without using any complicated manipulations, the ALPSO based robust gain tuning scheme for PID controllers is introduced. It does not need any conservative assumption unlike the conventional methods, and often enables us to find the desired PID gains just by solving the constrained optimization problem in a straightforward way. However, it is difficult to guarantee its effectiveness in a theoretical way, because PSO is essentially a stochastic approach. Therefore, it is evaluated by several simulation examples, which demonstrate that the proposed approach works well to obtain PID controller parameters satisfying the multiple H_∞ performance criteria.     

Adaptive stabilization using a nonlinear time-varying controller

Byrnes et al. (1986) showed that there is no smooth, finite-dimensional, nonlinear time-invariant (NLTI) controller which asymptotically stabilizes every finite-dimensional, stabilizable and detectable, linear time-invariant (LTI) plant (with a fixed number of inputs and outputs). Here we construct a finite-dimensional nonlinear time-varying (NLTV) controller which does exactly that; we treat both the discrete-time and continuous-time cases. With p equal to one in the discrete-time case and the number of plant outputs in the continuous-time case, we first show that for every stabilizable and detectable plant, there exists a p-dimensional linear time-varying (LTV) compensator which provides exponential stabilization; we then construct a (p+1)-dimensional NLTV controller which asymptotically stabilizes every admissible plant by switching between a countable number of such LTV compensators     

基于D分解的分数阶PID控制器的图形化参数整定

针对二自由度分数阶PID控制器设计的参数多,结构复杂等复杂问题,提出了一种基于D分解法和主导极点配置的新型参数整定方法。其基本原理首先基于动态响应指标约束进行主导极点配置,在确保闭环系统的响应特性良好的条件下确定系统超调量和调节时间,由此经过转换得到未知参数之间的函数关系。其次,使用D分解法,将未知参数在不影响的控制性能的条件下由多减少,再由相关参数取得系统性能稳定的参数域中优化,最后以差分进化算法为导向,以两种方法取得的相关约束条件为指标取得最优控制器参数,在确保所选极点的优势下使所设计的控制器达到理想的控制性能。最终,将所设计的控制器通过应用在整数阶和分数阶被控对象上,使用仿真验证新方法的鲁棒性和快速性,同时也表现了新方法的有效性和实用性。     

Comparative study on linearized robot models

For representing the input—output behaviour of a robot manipulator by a linear time-invariant model, four direct linearization schemes are: (i) state linearization, (ii) linearization based on an identification method, (iii) linearization based on neglecting velocity-dependent and gravity terms and (iv) linearization based on neglecting the velocity-dependent term only (rate linearization). In order to make an appropriate choice of linear model for the development of real-time control, these schemes are extensively studied in this paper. It is shown that the rate linearization method leads to a satisfactory tradeoff between computation, accuracy, and stability. In the case of high velocity motions, a combination of state linearization and rate linearization is proposed.     

基于模糊性能指标的广义预测控制器参数调整

针对广义预测控制算法在控制时域中求得的M个控制量，利用模糊模拟技术对系统的约束进行检验，不断修正目标函数中控制量的加权系数，充分利用系统预测控制量的信息，增强系统的鲁棒性，并满足系统的约束。     

Series PID controller tuning based on the SIMC rule and signal filtering

The SIMC (Simple control) rule, proposed by Skogestad, is ineffective for a class of processes with oscillatory dynamics and processes defined by transfer functions obtained as a result of ideal decoupling of multiple-input multiple-output systems. For this class of stable processes it is proposed to apply a higher-order filtering to the open-loop process step response and to approximate the filtered step response with stable SOPDT models. These models are used to obtain a high performance/robustness tradeoff by the ideal series PID controllers, tuned by the SIMC rule, with the higher-order filter in the feedback loop. Parallel PID controllers, with higher-order noise filters, tuned by applying exact process frequency response and optimization under constraints on the robustness and sensitivity to measurement noise, are used to demonstrate merits of the proposed simple design and tuning of the series PID controller. Experimental results on a mechanical laboratory plant are presented in Appendix.     

非线性零阶接近有界多模型神经网络自适应控制器

针对一类单变量非线性离散时间系统,提出一种零阶接近有界的多模型神经网络自适应控制器。该控制器包含一个非线性鲁棒自适应控制器和一个非线性神经网络自适应控制器。当系统非线性项放宽到零阶接近有界时,这两个控制器分别用于保证系统的稳定性和提高系统的性能,系统的控制输入由切换机构在两个控制器之间进行切换产生。最后给出了稳定性和收敛性证明,并通过仿真实验验证了该控制器的有效性。     

基于内模法的PID控制器自整定算法

为解决传统工业控制中比例-积分-微分(PID)控制器参数整定的问题,提出了一种基于内模法(IMC)以及系统辨识的控制器参数确定算法。该方法首先利用被控过程在开环阶跃信号激励下,输入与暂态输出的对应关系,将被控对象辨识为一阶加滞后(FOPDT)或二阶加时滞(SOPDT)的模型;再利用IMC算法确定控制器的参数。对于在内模法中引入的滤波器参数λ的确定问题,提出通过引入γ和σ两个参数,并与输出误差的平方建立关系来确定λ 的方法。仿真显示,对于输出误差绝对值之和(IAE)这个指标,该种算法与传统基于IMC的PID控制算法相比,在无输入扰动时可提高20%左右,在有输入扰动时可提高10%左右。仿真结果表明:在用单位阶跃信号激励系统时,提出的整定方法在保证了系统鲁棒性的前提下,提高了系统的瞬态响应速度,并有效抑制了系统输出的超调。     

基于幅值裕度和相位裕度的PID参数最优整定方法

给出一种基于幅值裕度和相位裕度的PID参数最优整定方法.首先,基于改进的D–分割法确定满足幅值裕度和相位裕度要求的控制参数稳定域,然后根据最大灵敏度函数、超调和调节时间定义控制器设计的目标函数,在所得到的控制参数稳定域中计算出一组最优的控制参数值.仿真结果表明,该整定方法能够保证闭环系统具有强鲁棒性、良好的跟踪性能和抗干扰性能.它不仅适用于稳定时滞对象,而且还适用不稳定时滞对象.