约束时变不确定离散系统的输出反馈预测控制综合

研究多包描述系统的离线型输出反馈预测控制．已有一方法首先综合状态反馈预测控制，满足输入/ 状态约束；而在设计观测器时，不再考虑输入/ 状态约束．本文则首先给出观测器，并给出一组不等式条件使得真实状态、观测状态和观测误差都保持在同一个椭圆内部，以便采用线性矩阵不等式处理输入/ 状态约束．基此，本文离线计算一椭圆序列，每个椭圆对应一控制律和一观测器，而在线的实时控制律和观测器则从该序列中选择，使得闭环系统具有稳定性保证．仿真例子说明了本文方法的有效性．     

An off-line robust MPC algorithm for uncertain polytopic discrete-time systems using polyhedral invariant sets

In this paper, an off-line synthesis approach to robust model predictive control (MPC) using polyhedral invariant sets is presented. Most of the computational burdens are moved off-line by computing a sequence of state feedback control laws corresponding to a sequence of polyhedral invariant sets. At each sampling time, the smallest polyhedral invariant set that the currently measured state can be embedded is determined. The corresponding state feedback control law is then implemented to the process. The controller design is illustrated with two examples. Comparisons between the proposed algorithm and an ellipsoidal off-line robust MPC algorithm have been undertaken. The proposed algorithm yields a substantial expansion of the stabilizable region. Therefore, it can achieve less conservative result as compared to an ellipsoidal off-line robust MPC algorithm.     

Robust output feedback model predictive control using off-line linear matrix inequalities

A fundamental question about model predictive control (MPC) is its robustness to model uncertainty. In this paper, we present a robust constrained output feedback MPC algorithm that can stabilize plants with both polytopic uncertainty and norm-bound uncertainty. The design procedure involves off-line design of a robust constrained state feedback MPC law and a state estimator using linear matrix inequalities (LMIs). Since we employ an off-line approach for the controller design which gives a sequence of explicit control laws, we are able to analyze the robust stabilizability of the combined control laws and estimator, and by adjusting the design parameters, guarantee robust stability of the closed-loop system in the presence of constraints. The algorithm is illustrated with two examples.     

An improved robust model predictive control approach to systems with linear fractional transformation perturbations

In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control (MPC) approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.     

A specified‐time control framework for control‐affine systems and rigid bodies: A time‐rescaling approach

This paper addresses the specified‐time control problem for control‐affine systems and rigid bodies, wherein the specified‐time duration can be designed in advance according to the task requirements. By using the time‐rescaling approach, a novel framework to solve the specified‐time control problem is proposed, and the original systems are converted to the transformation systems based on which the specified‐time control laws for both control‐affine systems and rigid bodies are studied. Compared with the existing approaches, our proposed specified‐time control laws can be derived from the known stabilization control laws. To our best knowledge, it is the first time that transformation system–based specified‐time control framework for control‐affine system and rigid body dynamics is proposed. To further improve the convergence performance of specified‐time control, a finite‐time attitude synchronization control law for rigid bodies on rotation matrices is proposed, and thereby, the finite‐time–based specified‐time control law is designed eventually. In the end, numerical simulations and SimMechanics experiments are provided to illustrate effectiveness of the theoretical results.     

离线鲁棒预测控制器综合方法的改进方案

对离线鲁棒预测控制器综合方法进行改进.离线鲁棒预测控制算法离线确定一个控制律序列,对应一组吸引域;在线根据当前状态位于哪个吸引域内部(或哪两个吸引域之间)选择相应的一个控制律(或相邻的两个控制律的线性插值).引入参数Lyapunov函数得到多包椭圆型吸引域和多包二次稳定的离线控制律,改进了控制器的可行性和最优性.改进的控制器保持了稳定性以及控制律关于系统状态的连续性.     

时变不确定系统的变时域离线鲁棒预测控制

给出多包描述约束系统的鲁棒调节器的一种新方法.现有的离线方案离线构造一系列的状态反馈控制律,其中每一个控制律是通过将无穷时域的控制输入固定为唯一的状态反馈控制律而得到的.而本文在优化较大椭圆内的控制律时,使下个时刻的状态进入临近的更小的椭圆———在较小的椭圆内部,相应的控制律序列作为局部控制器.因此,本文新方法相当于给出了变时域的预测控制器,可给出更优的控制作用.仿真例子说明了新方法的有效性.     

Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review

In Computer Numerical Control (CNC) machining, determining optimum or appropriate cutting parameters can minimize machining errors such as tool breakage, tool deflection and tool wear, thus yielding a high productivity or minimum cost. There have been a number of attempts to determine the machining parameters through off-line adjustment or on-line adaptive control. These attempts use many different kinds of techniques: CAD-based approaches, Operations Research approaches, and Artificial Intelligence (AI) approaches. After describing an overview of these approaches, we will focus on reviewing AI-based techniques for providing a better understanding of these techniques in machining control. AI-based methods fall into three categories: knowledge-based expert systems approach, neural networks approach and probabilistic inference approach. In particular, recent research interests mainly tend to develop on-line or real-time expert systems for adapting machining parameters. The use of AI techniques would be valuable for the purpose.     

Designing Decentralized Controllers for Distributed-Air-Jet MEMS-Based Micromanipulators by Reinforcement Learning

Distributed-air-jet MEMS-based systems have been proposed to manipulate small parts with high velocities and without any friction problems. The control of such distributed systems is very challenging and usual approaches for contact arrayed system don’t produce satisfactory results. In this paper, we investigate reinforcement learning control approaches in order to position and convey an object. Reinforcement learning is a popular approach to find controllers that are tailored exactly to the system without any prior model. We show how to apply reinforcement learning in a decentralized perspective and in order to address the global-local trade-off. The simulation results demonstrate that the reinforcement learning method is a promising way to design control laws for such distributed systems.     

Model predictive control for constrained uncertain piecewise linear systems

For constrained piecewise linear (PWL) systems, the possible existing model uncertainty will bring the difficulties to the design approaches of model predictive control (MPC) based on mixed integer programming (MIP). This paper combines the robust method and hybrid method to design the MPC for PWL systems with structured uncertainty. For the proposed approach, as the system model is known at current time, a free control move is optimized to be the current control input. Meanwhile, the MPC controller uses a sequence of feedback control laws as the future control actions, where each feedback control law in the sequence corresponds to each partitions and the arbitrary switching technique is adopted to tackle all the possible switching. Furthermore, to reduce the online computational burden of MPC, the segmented design procedure is suggested by utilizing the characteristics of the proposed approach. Then, an offline design algorithm is proposed, and the reserved degree of freedom can be online used to optimize the control input with lower computational burden.     

The Ellipsoidal Invariant Set of Fractional Order Systems Subject to Actuator Saturation: The Convex Combination Form

The domain of attraction of a class of fractional order systems subject to saturating actuators is investigated in this paper. We show the domain of attraction is the convex hull of a set of ellipsoids. In this paper, the Lyapunov direct approach and fractional order inequality are applied to estimating the domain of attraction for fractional order systems subject to actuator saturation. We demonstrate that the convex hull of ellipsoids can be made invariant for saturating actuators if each ellipsoid with a bounded control of the saturating actuators is invariant. The estimation on the contractively invariant ellipsoid and construction of the continuous feedback law are derived in terms of linear matrix inequalities (LMIs). Two numerical examples illustrate the effectiveness of the developed method.      

Hammerstein-Wiener nonlinear model based predictive control for relative QoS performance and resource management of software systems

Runtime management of Quality of Service (QoS) performance and resource provisioning is a vital issue in shared resource software environments. A useful performance management technique for such software systems is the relative guarantee feedback control scheme. The existing approaches for this class of control systems are mainly based on off-line linear or on-line model identification and control techniques, which tend to have performance issues in the presence of nonlinearities induced by this scheme. Instead of using such modeling techniques, this paper proposes a new approach for QoS performance management and resource provisioning by using an off-line identification of Hammerstein and Wiener nonlinear block structural model. Using the characteristic structure of the nonlinear model, a predictive feedback controller based on a gain schedule technique is incorporated in the design to achieve the performance objectives. The proposed approach is validated using experiments based on a prototype, demonstrating superior runtime QoS performance management and resource provisioning in a complex software system.     

An event-driven high level model for the specification of laws in open multi-agent systems

The agent development paradigm poses many challenges to software engineering researchers, particularly when the systems are distributed and open. They have little or no control over the actions that agents can perform. Laws are restrictions imposed by a control mechanism to deal with uncertainty and to promote open system dependability. In this paper, we present a high level event-driven conceptual model of laws. XMLaw is an alternative approach to specifying laws in open multi-agent systems that presents high level abstractions and a flexible underlying event-based model. Thus XMLaw allows for flexible composition of the elements from its conceptual model and is flexible enough to accept new elements.     

动态输出反馈鲁棒模型预测控制离线算法

研究具有多包不确定性和有界噪声系统的动态输出反馈鲁棒模型预测控制(Robust model predictive control, RMPC)的离线方法. 先前的在线方法中, 在估计状态和估计误差集合已知的情况下, 在每一采样时刻通过近似最优算法求解控制器参数. 本文采用先前的方法计算离线控制器参数和吸引域. 首先, 选定一系列估计状态, 其中,每个估计状态对应同样一组嵌套的估计误差集合. 然后,针对每一估计状态和每一估计误差集合的组合,离线计算唯一的控制器参数和对应的吸引域. 这些控制器参数和对应的吸引域存储在表中. 如果离线确定的吸引域包含实时的扩展状态, 则该离线控制器参数是实时可行的. 在线时, 根据实时估计状态和选取实时估计误差集合, 在表中搜索包含实时扩展状态且优化性能指标最小的吸引域所对应的控制器参数. 通过连续搅拌釜式反应器控制系统验证了该方法的有效性.     

A STEP-compliant Industrial Robot Data Model for robot off-line programming systems

Recently, various robot off-line programming systems have promoted their own robot data models, resulting in a plethora of robot representation methods and unchangeable data files among CAx and robot off-line programming systems. The current paper represents a STEP-compliant Industrial Robot Data Model (IRDM) for data exchange between CAx systems and robot off-line programming systems. Using this novel representation method, most resources involved in a robot manufacturing system can be represented. The geometric and mathematic aspects of industrial robots have been defined in IRDM, so that the robot off-line programming system could have abundant information to represent robots’ kinematic and dynamic behaviors. In order to validate the proposed models and approaches, a prototype robot off-line programming system with 3D virtual environment is presented. The functionalities of IRDM not only have significant meaning for providing a unified data platform for robot simulation systems, but also have the potential capability to represent robot language using the object-oriented concept.     

Scheduling of a limited communication channel for optimal control

In this paper a method for optimal off-line scheduling of a limited resource used for control purposes is presented. For various reasons, real-time communication channels are prone to have limited bandwidth. To overcome this obstacle, the rate of actions must be chosen accordingly at design time, both with respect to the limitation of the resource and to control performance. A resulting off-line schedule implements the rate of actions as a repetitive sequence of communication instants. Periodic control theory is used to define a cost functional for LQ-control, that measures the performance of a sampled-data system in relation to a desired continuous time performance. In contrast to uniform sampling, the communication sequence is here allowed to be time-varying. This approach results in a complex combinatorial optimization problem, whose solution gives the optimal off-line schedule, i.e., the sequence in which the actions should take place. The optimization problem is solved by a neighborhood search method where a heuristic method is used to generate initial guesses close to the optimum. The optimal schedule is typically such that the sampling is non-uniform, but the resulting LQ-control law is time-varying and takes this non-uniform sampling into account.     

Time-synchronised hardware-in-the-loop simulation — Applied to sheet-metal press line optimisation

This paper presents an off-line process parameter tuning method, focussing on complex control functions for automated manufacturing systems. The proposed method is based on a combination of simulation-based optimisation and time-synchronised hardware-in-the-loop simulation. Real industrial control systems, e.g., PLCs, are included in the simulation, executing the unchanged control code from the manufacturing plant. This approach allows all plant control functions to be included, even time-critical feedback loops and vendor secret blocks such as motion control. Consequently, the method suggested manages the problems identified in previously presented simulation-based approaches. Furthermore, a case study of an automotive sheet-metal press line has been performed to verify the proposed method, with successful results.     

Decentralized Sliding Mode Control for Multi‐Input Complex Interconnected Systems Subject to non‐smooth Nonlinearities

Many modern control systems become gradually more complicated and, consequently, the approach to control design approaches is both difficulty and complex. Moreover, if such a complex interconnected system is subjected to non‐smooth nonlinearities in the actuator, then unexpected difficulties, degradation or, even worse, instability will arise in the system performance. Therefore, a new decentralized sliding mode control (DSMC) approach for such a class of complex interconnected systems subjected to non‐smooth (deadzone) nonlinearities is proposed in this paper. Based on sliding mode control (SMC) theory, the proposed DSMC laws guarantee the global reaching condition of the sliding mode in uncertain complex interconnected systems with deadzone nonlinearities, that is, they can ensure that the sliding mode is reached in finite time and with prescribed transient behavior. In the sliding mode, the investigated uncertain complex interconnected system with deadzone nonlinearities in the actuator still are insensitive to system uncertainties and external disturbances. The proposed DSMC laws can work effectively for uncertain complex interconnected systems either with or without deadzone nonlinearities in the actuator. However, this cannot be guaranteed by the traditional DSMC design for systems without input deadzone nonlinearities. Furthermore, the sliding motion can be controlled to converge within a specified exponential speed. Finally, two illustrative examples with a series of computer simulations are presented to demonstrate the effectiveness of the proposed DSMC laws.     

A parametric Lyapunov equation approach to low gain feedback design for discrete-time systems

Low gain feedback, a parameterized family of stabilizing state feedback gains whose magnitudes approach zero as the parameter decreases to zero, has found several applications in constrained control systems, robust control and nonlinear control. In the continuous-time setting, there are currently three ways of constructing low gain feedback laws: the eigenstructure assignment approach, the parametric ARE based approach and the parametric Lyapunov equation based approach. The eigenstructure assignment approach leads to feedback gains explicitly parameterized in the low gain parameter. The parametric ARE based approach results in a Lyapunov function along with the feedback gain, but requires the solution of an ARE for each value of the parameter. The parametric Lyapunov equation based approach possesses the advantages of the first two approaches and results in both an explicitly parameterized feedback gains and a Lyapunov function. The first two approaches have been extended to discrete-time setting. This paper develops the parametric Lyapunov equation based approach to low gain feedback design for discrete-time systems.