Optimal adaptive scheduling and control of beer membrane filtration

An adaptive optimal scheduling and controller design is presented that attempts to improve the performance of beer membrane filtration over the ones currently obtained by operators. The research was performed as part of a large European research project called EU Cafe with the aim to investigate the potential of advanced modelling and control to improve the production and quality of food. Significant improvements are demonstrated in this paper through simulation experiments. Optimal scheduling and control comprises a mixed integer non-linear programming problem (MINLP). By making some suitable assumptions that are approximately satisfied in practice, we manage to significantly simplify the problem by turning it into an ordinary non-linear programming problem (NLP) for which solution methods are readily available. The adaptive part of our scheduler and controller performs model parameter adaptations. These are also obtained by solving associated NLP problems. During cleaning stages in between membrane filtrations enough time is available to solve the NLP problems. This allows for real-time implementation.     

An efficient heuristic for adaptive production scheduling and control in one-of-a-kind production

Even though research in flow shop production scheduling has been carried out for many decades, there is still a gap between research and application—especially in manufacturing paradigms such as one-of-a-kind production (OKP) that intensely challenges real time adaptive production scheduling and control. Indeed, many of the most popular heuristics continue to use Johnson's algorithm (1954) as their core. This paper presents a state space (SS) heuristic, integrated with a closed-loop feedback control structure, to achieve adaptive production scheduling and control in OKP. Our SS heuristic, because of its simplicity and computational efficiency, has the potential to become a core heuristic. Through a series of case studies, including an industrial implementation in OKP, our SS-based production scheduling and control system demonstrates significant potential to improve production efficiency.     

Towards applied nonlinear adaptive control

A novel approach for the solution of nonlinear adaptive control problems is proposed. This approach does not rely on structural assumptions on the system to be controlled nor on linear parameterization. The approach is illustrated by means of several applications, including wing rock elimination, an adaptive visual servoing problem, the control of a power converter, and a flight control problem. Simulations and experimental results highlight the validity of the proposed methodology.     

Theory and applications of adaptive control—A survey

Progress in theory and applications of adaptive control is reviewed. Different approaches are discussed with particular emphasis on model reference adaptive systems and self-tuning regulators. Techniques for analysing adaptive systems are discussed. This includes stability and convergence analysis. It is shown that adaptive control laws can also be obtained from stochastic control theory. Issues of importance for applications are covered. This includes parameterization, tuning, and tracking, as well as different ways of using adaptive control. An overview of applications is given. This includes feasibility studies as well as products based on adaptive techniques.     

Robust adaptive repetitive learning control for a class of time-varying nonlinear systems with unknown control direction

A robust adaptive repetitive learning control method is proposed for a class of time-varying nonlinear systems. Nussbaum-gain method is incorporated into the control design to counteract the lack of a priori knowledge of the control direction which determines the motion direction of the system under any input. It is shown that the system state could converge to the desired trajectory asymptotically along the iteration axis through repetitive learning. Simulation is carried out to show the validity of the proposed control method.     

Gain scheduling consensus of multi-agent systems subject to actuator saturation

ABSTRACT

This paper presents a gain scheduling approach for achieving the consensus tracking of multi-agent systems with actuator saturation. We first construct a series of nesting ellipsoid invariant sets associated with consensus errors. When the consensus errors stay between the two ellipsoid invariant sets, the feedback gains keep constant, but when the consensus errors enter into the smaller ellipsoid invariant set, the feedback gains abruptly become larger. By combining this gain scheduling technique and the parametric Lyapunov equations, we, respectively, design state and output feedback gain scheduling protocols. Their main advantage, in comparison with the fixed case, is that the convergence rate of consensus tracking can be enhanced by scheduling the gain parameters. Numerical simulations verify the effectiveness of theoretical analysis.     

Robust adaptive fuzzy VSS control for a class of uncertain nonlinear systems using small gain design

In this paper, a novel robust adaptive fuzzy variable structure control (RAFVSC) scheme is proposed for a class of uncertain nonlinear systems. The uncertain nonlinear system and gain functions originating from modeling errors and external disturbances are all unstructured (or non-repeatable), state-dependent and completely unknown. The Takagi–Sugeno type fuzzy logic systems are used to approximate uncertain functions in the systems and the RAFVSC is designed by use of the input-to-state stability (ISS) approach and small gain theorem. In the algorithm, there are three advantages which are that the asymptotic stability of adaptive control in the presence of unstructured uncertainties can be guaranteed, the possible controller singularity problem in some of existing adaptive control schemes using feedback linearization techniques can be removed and the adaptive mechanism with minimal learning parameterizations can be achieved. The performance and effectiveness of the proposed methods are discussed and illustrated with two simulation examples.     

Human-machine interaction force control: using a model-referenced adaptive impedance device to control an index finger exoskeleton

Exoskeleton robots and their control methods have been extensively developed to aid post-stroke rehabilitation. Most of the existing methods using linear controllers are designed for position control and are not suitable for human-machine interaction （HMI） force control, as the interaction system between the human body and exoskeleton is uncertain and nonlinear. We present an approach for HMI force control via model reference adaptive impedance control （MRAIC） to solve this problem in case of index finger exoskeleton control. First, a dynamic HMI model, which is based on a position control inner loop, is for- mulated. Second, the theoretical MRAC framework is implemented in the control system. Then, the adaptive controllers are designed according to the Lyapunov stability theory. To verify the performance of the proposed method, we compare it with a proportional-integral-derivative （PID） method in the time domain with real experiments and in the frequency domain with simu- lations. The results illustrate the effectiveness and robustness of the proposed method in solving the nonlinear HMI force control problem in hand exoskeleton.     

Lyapunov-based adaptive control of MIMO systems

The design of Model-Reference Adaptive Control for MIMO linear systems has not yet achieved, in spite of significant efforts, the completeness and simplicity of its SISO counterpart. One of the main obstacles has been the generalization of the SISO assumption that the sign of the high-frequency gain (HFG) is known. Here we overcome this obstacle and present a more complete MIMO analog to the well known Lyapunov-based SISO design which is significantly less restrictive than the existing analogs. Our algorithm makes use of a new control parametrization derived from a factorization of the HFG matrix K_p=SDU, where S is symmetric positive definite, D is diagonal, and U is unity upper triangular. Only the signs of the entries of D or, equivalently, the signs of the leading principal minors of K_p, are assumed to be known.     

Robust adaptive fault-tolerant control for a class of uncertain nonlinear systems with multiple time delays

This study is concerned with the problem of robust adaptive fuzzy fault-tolerant control for a class of uncertain nonlinear systems with mismatching parameter uncertainties, external disturbances, multiple state time delays perturbations and actuator failures, which include loss of effectiveness, outage and stuck modes. A novel direct adaptive fuzzy tracking control scheme is developed to achieve the fault-tolerant control objective. First, by introducing a positive nonlinear control gain function, the effects of state time delays and actuator failures are effectively compensated. Then, a suitable fuzzy logic system (FLS), which is used to approximate the corresponding nonlinear function, is constructed to eliminate the influences on mismatched parameter uncertainty and external disturbance. Moreover, it is shown that all the closed-loop system signals are uniformly bounded and that the tracking error converges to a small neighborhood of the origin via Lyapunov–Krasovskii stability analysis. Finally, the proposed adaptive fuzzy fault-tolerant tracking design approach is illustrated on a two stage chemical reactor system with delayed recycle streams.     

间歇精馏过程的模糊逻辑与增益自调整PID混合控制

针对间歇精馏过程的强非线性和非平稳时变特性,结合模糊逻辑控制和增益自调整PID控制的优点,提出了一种模糊逻辑和增益自调整PID混合控制的先进控制策略,详细推导了其控制算法,设计了相应的控制器,并在EuroBEEB工控机上用实时BASIC语言编程实现,对一套甲醇/水二元间歇精馏塔的塔顶浓度进行了推断控制实验,获得了比单独采用模糊逻辑控制时更好的控制结果。这说明,模糊逻辑和增益自调整PID混合控制是强非线性和非平稳时变过程的一种有效控制策略。     

Robust adaptive control of discrete-time systems using persistent excitation

An important result in the robust adaptive control of continuous-time systems, using the persistent excitation of the reference input, was recently given by Narendra and Annaswamy (1986, IEEE Trans. Aut. Control, AC-31, 306–315). According to this result, the global boundedness of all the signals in the adaptive system can be assured if the degree of persistent excitation of the reference input is larger than an appropriate bound on the external disturbance. The main theorem in Narendra and Annaswamy (1986) is proved for a class of plants characterized by the property that the reference model used in the adaptive controller could be chosen to be strictly positive real, a condition which involves constraints on the relative degree of the plant. This paper presents a generalization of the above result to plants of arbitrary relative degree. Together with the work reported in the earlier paper, it demonstrates that the boundedness of all the signals in an adaptive system in the presence of bounded disturbances and arbitrary initial conditions can be assured by increasing the degree of persistent excitation of the reference input.     

生产调度优化问题文化进化算法研究

许多生产调度优化问题属于NP-hard问题,其求解通常采用智能启发式算法。基于文化算法及文化进化思想设计的文化进化算法,通过上层文化空间的经验知识指导下层个体进化搜索的方向及步长,通过模拟人类社会文化进化的机制实现文化空间的进化与更新,最后将算法应用到置换Flow shop问题的求解,用Matlab编程仿真测试,结果表明此算法解决生产调度优化问题是可行的,而且其全局搜索性能优于一种改进的GA算法。     

矩阵分解的多变量模型参考自适应控制

研究了基于高频增益矩阵分解的多变量模型参考自适应控制 .为此首先基于矩阵分解构造出新的参数化方程 ,选取必然等价自适应控制律 .然后 ,引入规范化信号和类Lyapunov函数 ,获得了规范化自适应律 .并进一步给出了虚拟规范化信号的有用性质 .最后 ,严格地给出了闭环系统的稳定性和收敛性分析     

基于PSO算法的WNN大包线增益调参控制律设计

提出了一种基于粒子群优化算法的小波神经网络大包线调参控制律设计方法.该方法用小波函数代替了Sigmoid函数作为激活函数.由于结合了小波变换良好的高频域时间精度、低频域频率精度的性质和神经网络的自学习功能,因而具有较强逼近非线性函数的能力.为了克服局部极小值问题并进一步提高对非线性函数逼近能力,利用粒子群优化算法对小波神经网络进行参数训练,并利用该网络实现了大包线增益调参.飞行仿真结果表明,所设计的小波神经网络增益调参控制器具有优良的控制性能,不仅能够保证平衡状态下的控制效果,而且在未训练的平衡状态下依然具有良好的控制性能,并且在存在20%的建模误差时,最大超调量仅为6 m,仅是使用常规增益调参方法的18%.     

无线传感器网络自适应性拓扑控制的研究

自适应拓扑控制方法用到多跳两层无线传感器网络(WSNs),在每个簇中用两类传感器,有效且低开销的传感器节点N感知环境现象信息,并传输它们的信息到汇聚节点S,所有Ss协同工作去除随机信息并传输数据到基站BS。因为覆盖范围依赖于它的汇聚节点的工作情况,而汇聚节点的能耗在网络的生命期中是关键性因素。这个方法主要是从节点路由能量匹配角度出发,设计可控制数据流路由路径,用于尽可能有效地保持网络能量,并不是仅仅考虑路径的最优选择,而是考虑能效的最优方式选择路由,从而增加整个网络的生命期。     

基于总线的开放式控制系统调度策略

提出“基于改进EDF的模糊反馈调度策略”,并应用于基于总线的开放式控制系统。设计了一个适用于此类总线控制架构的任务调度方案,由任务截止期、任务重要性以及传输误差和响应时间的综合影响来动态调节任务优先级,用以解决由引入总线而导致的响应速度慢、总线阻塞时的丢包问题。通过对机器人的控制实验验证了所提出调度策略的可行性。实验结果表明,加入调度模块后控制系统的响应时间和丢包率都有显著改善,系统的实时性得到了提高。     

Non-linear dynamics in adaptive control: Chaotic and periodic stabilization

In this paper the non-linear features of adaptive control algorithms are highlighted. Using a simple “linear” discrete time example, based on classical design, it is demonstrated that in the presence of undermodelling errors, non-linear phenomena in the feedback gain such as limit cycles and even chaos arise. Despite these complicated dynamics, robust stabilization of the plant can still occur. For the class of undermodelling errors considered, the set of plants stabilizable by this adaptive controller is completely characterized.     

机器人多胞变增益输出反馈H∞控制

针对n类关节的刚性机器人,提出一种设计包含极点配置的多胞变增益输出反馈H_∞控制器的新方法.利用平衡族附近的线性化,机器人系统可化为一关于平衡族的连续线性变参数系统,通过引入滤波器得到易于设计变增益控制器的增广对象,并将其凸分解为多胞表示,基于二次D 稳定和二次H_∞性能概念,利用多胞特性将整个控制器设计转化为对胞体顶点控制器的设计,然后利用LMI方法,对多胞的各顶点分别设计满足H_∞∞性能和动态特性的输出反馈控制器,最后综合顶点控制器得到具有同     

Learning-based multi-pass adaptive scheduling for a dynamic manufacturing cell environment

Because the essential attributes are uncertain in a dynamic manufacturing cell environment, to select a near-optimal subset of manufacturing attributes to enhance the generalization ability of knowledge bases remains a critical, unresolved issue for classical artificial neural network-based (ANN-based) multi-pass adaptive scheduling (MPAS). To resolve this problem, this study develops a hybrid genetic /artificial neural network (GA/ANN) approach for ANN-based MPAS systems. The hybrid GA/ANN approach is used to evolve an optimal subset of system attributes from a large set of candidate manufacturing system attributes and, simultaneously, to determine configuration and learning parameters of the ANN according to various performance measures. In the GA/ANN-based MPAS approach, for a given feature subset and the corresponding topology and learning parameters of an ANN decoded by a GA, an ANN was applied to evaluate the fitness in the GA process and to generate the MPAS knowledge base used for adaptive scheduling control mechanisms. The results demonstrate that the proposed GA/ANN-based MPAS approach has, according to various performance criteria, a better system performance over a long period of time than those obtained with classical machine learning-based MPAS approaches and the heuristic individual dispatching rules.