智能控制：从学习控制到平行控制

20世纪60年代,学习控制开启了人类探究复杂系统控制的新途径,基于人工智能技术的智能控制随之兴起.本文以智能控制为主线,阐述其由学习控制向平行控制发展的历程.本文首先介绍学习控制的基本思想,描述了智能机器的架构设计与运行机理.随着信息科技的进步,基于数据的计算智能方法随之出现.对此,本文进一步简述了基于计算智能的学习控制方法,并以自适应动态规划方法为切入点分析非线性动态系统自学习优化问题的求解过程.最后,针对工程复杂性与社会复杂性互相耦合的复杂系统控制问题,阐述了基于平行控制的学习与优化方法求解思路,分析其在求解复杂系统优化控制问题方面的优势.智能控制思想经历了学习控制、计算智能控制到平行控制的演化过程,可以看出平行控制是实现复杂系统知识自动化的有效方法.     

Stochastic optimal control of networked control systems with control packet dropouts

This paper considers the stochastic optimal control problem for networked control systems(NCSs)with control packet dropouts.The proportional plus up to the third-order derivative(PD3)compensation strategy is adopted to compensate for control packet dropouts at the actuator by using the past control packets stored in the buffer.Based on the strategy,a new NCS structure model with packet dropouts is provided,where the packet dropout is assumed to obey the Bernoulli random binary distribution.In terms of the given model,the stochastic optimal control law is proposed. Numerical examples illustrate the effectiveness of the results.     

Supervisory control of switching control systems

In this paper, the problem of designing a switching policy for an adaptive switching control system is formulated as a problem of supervisory control of a discrete-event system (DES). Two important problems in switching control are then addressed using the DES formulation and the theory of supervisory control under partial observation. First, it is verified whether for a given set of controllers, a switching policy satisfying a given set of constraints on the transitions among controllers exists. If so, then a minimally restrictive switching policy is designed. Next, an iterative algorithm is introduced for finding a minimal set of controllers for which a switching policy satisfying the switching constraints exists. It is shown that in the supervisory control problem considered in this paper, limitations on event observation are the factors that essentially restrict supervisory control. In other words, once observation limitations are respected, limitations on control will be automatically satisfied. This result is used to simplify the proposed iterative algorithm for finding minimal controller sets.     

From robust control to adaptive control

The paper emphasizes the interaction between robust control, identification in closed loops and adaptive control. Robust control and recent algorithms developed for plant model identification in closed loops have led to new designs of adaptive control systems. Their performances are further enhanced by the use of multiple-model adaptive control, based on switching and tuning. These developments are illustrated by their application to the control of a flexible transmission system.     

Hyperbolic optimal control and fuzzy control

In this paper, we consider a new approach to fuzzy control which entails the formulation of a novel state-space representation and a new form of optimal control problem. Basically, in this new formulation, linear functions in the conventional state-space representation and cost functional are replaced by hyperbolic functions. We give a solution for this new, infinite-time, optimal control problem, which we call hyperbolic optimal control. Furthermore, we show that the resulting optimal controller is in fact a Mamdani-type fuzzy controller with Gaussian membership functions and center of gravity defuzzification. These results enable us to investigate analytically important issues, such as stability and robustness, pertaining to fuzzy controllers as well as add a powerful theoretical framework to the field of fuzzy control     

Survey on iterative learning control,repetitive control,and run-to-run control

In this paper, three control methods—iterative learning control (ILC), repetitive control (RC), and run-to-run control (R2R)—are studied and compared. Some mathematical transformations allow ILC, RC, and R2R to be described in a uniform framework that highlights their similarities. These methods, which play an important role in controlling repetitive processes and run-based processes, are collectively referred to as learning-type control in this paper. According to the classification adopted in this paper, learning-type control has two classes—direct form and indirect form. The main ideas and designing procedures for these two patterns are introduced, separately. Approximately 400 papers related to learning-type control are categorized. Statistical analysis of the resulting data reveals some promising fields for learning-type control. Finally, a flowchart based on the unique features of the different methods is presented as a guideline for choosing an appropriate learning-type control for different problems.     

Generalized control systems,boundary control systems,and delayed control systems

We investigate the relationships between the three classes of systems mentioned in the title: we show that systems with delays in control are a special instance of boundary control systems, and a boundary control system produces a generalized control system when projected onto its (unstable) eigenspaces. We use this observation to investigate the action of feedback on the dynamical behavior of systems with boundary controls. In particular, the well-known fact that spectral controllability is necessary and sufficient for a system with delays in control to be stabilizable is derived from a general rather than from anad hoc method. This paper was written according to the programs of the GNAFA-CNR group, with the financial support of the Italian “Ministero della Pubblica Istruzione.”     

Embedded model control: reconciling modern control theory and error-based control design

The paper addresses the problem of reconciling the modern control paradigm developed by R. Kalman in the sixties of the past century, and the centenary error based design of the proportional, integrative and derivative (PID) controllers. This is done with the help of the error loop whose stability is proved to be necessary and sufficient for the close loop plant stability. The error loop is built by cascading the uncertain plant to model discrepancies (causal, parametric, initial state, neglected dynamics), which are driven by the design model output and by arbitrary bounded signals, with the control unit transfer functions. The embedded model control takes advantage of the error loop and its equations to design appropriate algorithms of the modern control theory (state predictor, control law, reference generator), which guarantee the error loop stability and performance. A simulated multivariate case study shows modeling and control design steps and the coherence of the predicted and simulated performance.     

事件驱动的网络化系统最优控制

针对随机事件驱动的网络化控制系统, 研究其中的有限时域和无限时域内最优控制器的设计问题. 首先, 根据执行器介质访问机制将网络化控制系统建模为具有多个状态的马尔科夫跳变系统; 然后, 基于动态规划和马尔科夫跳变线性系统理论设计满足二次型性能指标的最优控制序列, 通过求解耦合黎卡提方程的镇定解, 给出最优控制律的计算方法, 使得网络化控制系统均方指数稳定; 最后, 通过仿真实验表明了所提出方法的有效性.

     

Constrained control algorithm for non-linear control problems

The general non-linear control model presented by Kendrick (1981) is extended to cases where there are both unconstrained and constrained controls. The resulting algorithm is based on the unconstrained control algorithm, so it can be implemented without difficulty in the existing software. Moreover, the algorithm does not require much additional computation, which makes the algorithm rather efficient. An application to a complicated, but practical, economic problem concerning exchange-rate policy illustrates the relevance of the extension of the control model.     

Sampled-data control of networked linear control systems

In this paper, the problem of synthesis and analysis for the networked control systems (NCSs) with time-driven digital controllers and event-driven holders is considered. The NCS is modelled as a sampled-data system with time-delay in its discrete-time subsystem. This model is able to capture many network-induced features, for example, time-delay and packet dropout. Moreover, the model allows different combinations of the time-driven or event-driven mode of the devices, including the samplers, the controllers and the holders. By transforming time-delay in the discrete-time subsystem into its continuous-time subsystem of the sampled-data system, we have also obtained a less conservative time-delay dependent stability result for the NCSs, using a new Lyapunov function and a relaxed condition. Some limitations of the existing literatures on network-induced time-delay and sampling period are removed in the proposed framework. Furthermore, a sampled-data control design procedure is developed for the NCSs. Linear matrix inequality approach has been employed to solve the stability and control design problems. Finally, numerical examples are included to demonstrate the effectiveness of the proposed stability result and the potential of the proposed techniques.     

网络控制系统在线时延估计控制

从网络控制系统的实际应用出发，在不附加网络同步时钟和对时廷特征的高线假设下，运用通讯技术中的网络协议对网络时廷进行在线估计；在此基础上，运用最优控制为网络控制系统设计控制器。通过基于CAN总线的实验系统所得结果验证了上述设计的有效性。     

变采样网络控制系统的鲁棒控制

对于线性时不变控制对象,在控制器和控制对象都采用时间-事件驱动时系统就变成便采样网络控制系统,当网络时延不确定时,在小于或者等于一个变采样周期时,基于动态输出反馈对变采样网络控制系统进行建模,使用李雅普诺夫方法和线性矩阵不等式研究了系统的鲁棒稳定性,并设计了鲁棒控制器,最后给出实例证明在鲁棒控制器的控制下系统稳定。     

发电机组汽门系统的多模型自学习控制

针对发电机组的非线性、大范围运行等实际问题,研究了用于汽门系统的多模型自学习控制（MMSC）,首先根据各种工况下的样本数据归纳出模糊控制规则;然后由模糊聚类算法将多种工况约简为典型工况,得到相应的子模型模糊控制器（FLC）．以子模型FLC输出的加权集成作为MMSC的控制输出,而加权系数取决干子模型匹配度．在子模型FLC学习优化中,由支持向量机离线逼近模糊规则曲面,再由梯度下降算法在线自学习．仿真实验验证了所设计控制器的优良性能．     

Resilient control for wireless networked control systems

This paper proposes an approach to quantify the concept of resiliency in terms of Quality of Control (QoC) of a control system. Based on this concept, an intelligent resilient control algorithm (RCA) is presented for wireless networked control systems (WNCS) to maintain operational normalcy in face of wireless interference incidents, such as Radio Frequency (RF) jamming and signal blocking. The proposed algorithm closes the control loop with wireless sensors feasible by significantly increasing control system’s tolerance to data packet loss and delay caused by wireless interference. The proposed algorithm, along with other well developed wireless technologies, has the potential to enable implementing wireless sensors widely in the next generation of industrial automation and control systems.     

Parameter validity control in process control systems

Possibility of identifying the (explicit and implicit) invalidity in sensors’ indications within the process control systems (automatic control systems for industrial processes) is considered, on the basis of statistical data on industrial processes provided by the control system; the data is represented in the form of simulation model with interval validity estimates of the parameters. In the designed model, validity identification includes verifying all the functional relations specified for the selected industrial process. The issue of implementing the suggested model is studied subject to process control systems in the energy sector.     

Multi-phase current control using finite-state model-predictive control

The use of finite-state model-predictive controllers for current control of multi-phase machines is investigated. The basic setup is comprised a predictive model and an exhaustive optimizer that minimizes a predefined cost function for the next sampling period. The output of the predictive controller is a vector of gating signals to be applied to a voltage source inverter. The inverter can accommodate just a finite number of configurations and hence the name of finite-state. The use of predictive controllers, already proposed for three-phase drives, is applied here to multi-phase drives. Some implementation issues are discussed along, including the choice of the cost function, the switching frequencies applied to the inverter and the computation time needed for optimization. Simulation and experimental results are provided illustrating various aspects of the control scheme using an asymmetrical dual three-phase AC motor drive as a test bed.     

Guaranteed cost control for networked control systems

The guaranteed cost control problem for networked control systems (NCSs) is addressed under conmmnication constraints and varying sampling rate. First of all, a simple inFormation-scheduling scheme is presented to describe the scheduling approach of system signals in NCSs. Then, based on such a scheme and given sampling method, the design procedure in dynarmic output feedback manner is also derived which renders the closed loop system to be asymptotically stable and guarantees an upper bound of the LQ pefformance cost function.     

Minimizing control variation in nonlinear optimal control

In any real system, changing the control signal from one value to another will usually cause wear and tear on the system’s actuators. Thus, when designing a control law, it is important to consider not just predicted system performance, but also the cost associated with changing the control action. This latter cost is almost always ignored in the optimal control literature. In this paper, we consider a class of optimal control problems in which the variation of the control signal is explicitly penalized in the cost function. We develop an effective computational method, based on the control parameterization approach and a novel transformation procedure, for solving this class of optimal control problems. We then apply our method to three example problems in fisheries, train control, and chemical engineering.