Optimal disturbance rejection control for singularly perturbed composite systems with time‐delay

The optimal control problem for a class of singularly perturbed time‐delay composite systems affected by external disturbances is investigated. The system is decomposed into a fast linear subsystem and a slow time‐delay subsystem with disturbances. For the slow subsystem, the feedforward compensation technique is proposed to reject the disturbances, and the successive approximation approach (SAA) is applied to decompose it into decoupled subsystems and solve the two‐point boundary value (TPBV) problem. By combining with the optimal control law of the fast subsystem, the feedforward and feedback composite control (FFCC) law of the original composite system is obtained. The FFCC law consists of analytic state feedback and feedforward terms and a compensation term which is the limit of the adjoint vector sequence. The compensation term can be obtained from an iteration formula of adjoint vectors. Simulation results are employed to test the validity of the proposed design algorithm. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

A Wiener-type recurrent neural network and its control strategy for nonlinear dynamic applications

This paper presents a Wiener-type recurrent neural network with a systematic identification algorithm and a control strategy for the identification and control of unknown dynamic nonlinear systems. The proposed Wiener-type recurrent network resembles the conventional Wiener model that consists of a dynamic linear subsystem cascaded with a static nonlinear subsystem. The novelties of our network include: (1) the two subsystems are integrated into a single network whose output is expressed by a nonlinear transformation of a linear state-space equation; (2) the characteristics of the trained network can be analyzed by its associated state-space equation using the well-developed theory of linear systems; and (3) the size of the network structure is determined by the number of state variables (or the system order) of the unknown systems to be identified. To effectively identify a given unknown system from its input–output data, we have developed a systematic identification algorithm that consists of an order determination procedure, a parameterization procedure, and an online learning procedure. The false nearest neighbors algorithm was adopted to acquire a minimal embedding dimension from the input–output data as the system order, and then the eigensystem realization algorithm (ERA) was used to initialize a best-fit state-space representation according to the acquired system order. To improve the overall identification performance, we have derived an online parameter learning algorithm based on an ordered derivatives and momentum terms. Subsequently, a simple feedback linear controller was designed to control the unknown dynamic nonlinear systems without much complexity. Computer simulations and comparisons with some existing recurrent networks have conducted to confirm the effectiveness and superiority of the proposed Wiener-type network, identification algorithm and control strategy.     

Pseudo-decentralized adaptive stabilization of large-scale feedforward nonlinear systems

In this paper, we propose a pseudo-decentralized adaptive control scheme for a class of large-scale feedforward nonlinear systems with unknown nonlinear effects within subsystems and unknown nonlinear interactions among subsystems. The local controller of each subsystem takes a nested saturation feedback, using the state of its own subsystem, and the saturation levels are tuned online in a switching manner via a set of switching logics, which requires some binary flag communication among subsystems. Global asymptotic regulation of the closed-loop states is achieved.     

An algorithm for non-linear space—Time nuclear reactor control

A novel multivariable control algorithm for non-linear space-time nuclear reactor dynamics is proposed in this paper. The multivariable control algorithm is based on a mathematical model of the nuclear reactor which includes: a single energy group of neutrons, delayed neutron precursors, iodine, xenon and thermal-hydraulic feedback. The multivariable control algorithm is composed of non-linear time-varying feedforward and feedback control signals, a reference model of the nuclear reactor and a dynamic observer. The non-linear proportional plus integral feedback controller forces the nuclear reactor to follow the response of the reference model. The dynamic observer estimates the unmeasurable state variables. The feedforward and feedback control signals are determined in a novel approach by specifying the form of the closed-loop response of the neutron density variables. By virtue of the multivariable control algorithm the closed-loop differential equations are linear and time-varying. A linear stability analysis for base-load and load-cycle operation indicates that the closed-loop system is stable provided that the thermal-hydraulic subsystem is inherently stable. The simulated dynamic response indicates that the multivariable control algorithm provides excellent response characteristics.     

地铁综合监控系统中的数据转发研究

地铁综合监控系统是一个集多功能、多专业子系统的集成互联系统,它需要与相关子系统进行数据交换转发或下发控制命令.文中介绍并分析包括各车站子系统与车站综合监控系统数据的转发,综合监控系统内车站与控制中心数据的转发,控制中心向控制中心的各子系统下发控制命令及向TCC转发数据在内的多种数据转发的模型及流程,阐述了一整套综合监控系统数据交换方案,为城市轨道交通综合监控系统提供了技术参考及创新.     

Stabilization by forwarding design for switched feedforward systems with unstable modes

The problem of global stabilization is investigated for a class of switched nonlinear feedforward systems in this paper where the solvability of the stabilization problem for individual subsystem is not assumed. Some sufficient condition for the stabilization problem to be solvable is derived for the first time by exploiting the multiple Lyapunov functions method and the forwarding technique. Also, we design a switching law and construct bounded state feedback controllers of subsystems explicitly by a recursive design algorithm to achieve global asymptotic stability. The provided technique permits removal of a common restriction in which all subsystems in switched nonlinear feedforward systems are globally asymptotically stable. Finally, a numerical example is provided to demonstrate the feasibility of the theoretical result. Copyright © 2017 John Wiley & Sons, Ltd.     

Variance results for identification of cascade systems

The objective of this contribution is to analyze statistical properties of estimated models of cascade systems. Models of such systems are important in for example cascade control applications. The aim is to present and analyze some fundamental limitations in the quality of an identified model of a cascade system under the condition that the true subsystems have certain common dynamics. The model quality is analyzed by studying the asymptotic (large data) covariance matrix of the Prediction Error Method parameter estimate. The analysis will focus on cascade systems with three subsystems. The main result is that if the true transfer functions of the first and second subsystem are identical, the output signal information from the second and third subsystems will not affect the asymptotic variance of the estimated model of the first subsystem. This result implies that for a cascade system with two subsystems, where the dynamics of the first subsystem is a factor of the dynamics of the second one, the output signal information from the second subsystem will not improve the asymptotic quality of the estimate of the first subsystem. The results are illustrated by some simple FIR examples.     

Tracking control of electro-hydraulic servo multi-closed-chain mechanisms with the use of an approximate nonlinear internal model

This paper studied the trajectory-tracking problem of a hydraulic servo multi-closed-chain mechanism. The nonaffine nonlinear characteristic of the electro-hydraulic actuator and its time-varying uncertainty load resulting from the multi-closed-chain mechanism was taken into consideration in the proposed novel nonlinear control algorithm, that is, the approximate internal model control (AIMC) integrated with a position feedback control in cascade control design. This algorithm improves the trajectory-tracking performance of the hydraulic servomechanism (HSM). To reduce the difficulty in directly utilizing the AIMC for the HSM position trajectory, the complex electro-hydraulic mechanical system was divided into two subsystems: nonaffine nonlinear, and linear. The AIMC controller was designed for the nonaffine nonlinear subsystem to realize velocity trajectory tracking control, whereas a position feedback control was derived for the linear subsystem. The position trajectory tracking control was achieved by congruently combining the AIMC, and the position feedback control based on a recursive design idea. In addition, a complete state-space mathematical model for the HSM was developed and illustrated through simulations and experiments. Based on the proposed approach and the AIMC, the desired position and velocity trajectory tracking was examined on a hydraulic forging manipulator. The stability of the proposed method was analytically derived. Results of the simulations and experiments performed with the hydraulic manipulator demonstrated the effectiveness of the proposed approach.     

Pole placement approach to coherent passive reservoir engineering for storing quantum information

Reservoir engineering is the term used in quantum control and information technologies to describe manipulating the environment within which an open quantum system operates. Reservoir engineering is essential in applications where storing quantum information is required. From the control theory perspective, a quantum system is capable of storing quantum information if it possesses a so-called decoherence free subsystem (DFS). This paper explores pole placement techniques to facilitate synthesis of decoherence free subsystems via coherent quantum feedback control. We discuss limitations of the conventional `open loop'' approach and propose a constructive feedback design methodology for decoherence free subsystem engineering. It captures a quite general dynamic coherent feedback structure which allows systems with decoherence free modes to be synthesized from components which do not have such modes.     

多速率传感器不完全测量数据自适应融合算法

针对不完全测量的多速率传感器系统,建立了自适应的分级融合算法。该算法构建了传感器子系统故障检测方法,检测子系统故障,确认不完全测量,避免单点故障污染整个系统;建立子系统的切换控制逻辑,引入映射矩阵,构成新的信息分享系数,自适应地选择子系统参与中心融合;对于不完全测量信息的传感器子系统,利用子滤波预测值代替估计值进行融合;全局状态估计利用各子系统的当前与前一时刻的估计值,提高了融合性能。对建立的算法进行仿真,验证了该算法的有效性。     

Weakly coupled event triggered output feedback system in wireless networked control systems

This paper examines output feedback control of wireless networked control systems where there are separate links between the sensor-to-controller and controller-to-actuator. The proposed triggering events only rely on local information so that the transmissions from the sensor and controller subsystems are not necessarily synchronized. This represents an advance over recent work in event-triggered output feedback control where transmission from the controller subsystem was tightly coupled to the receipt of event-triggered sensor data. The paper presents an upper bound on the optimal cost attained by the closed-loop system. Simulation results demonstrate that transmissions between sensors and controller subsystems are not tightly synchronized. These results are also consistent with derived upper bounds on overall system cost.     

Decentralized control for two time-scale discrete-time systems

Output feedback design of discrete-time decentralized systems with slow and fast modes is considered. Conditions for the complete separation of slow and fast subsystems are given. The slow and fast subsystem outputs, which are obtained by applying the slow and fast subcontrollers to the corresponding subsystems, will be shown to approximate those of the original system. Also, the composite control, when being applied to the original system, will place the eigenvalues sufficiently close to the desired locations.     

严格反馈互联系统分散式backstepping自适应迭代学习控制

基于Lyapunov分析方法,针对具有严格反馈形式的非线性互联系统,本文设计了一种分散式backstepping自适应迭代学习控制器.子系统之间的互联项为所有子系统输出项线性有界,为每个子系统设计的控制器仅采用该子系统的信息,不需要子系统之间相互传递信息.在控制器中,引入在时间轴和迭代轴上同时更新的自适应参数,以补偿子系统之间的互联项影响.通过采用本文给出的控制器,可使得每个子系统的输出跟踪相应的参考模型输出,仿真结果验证了本文算法的有效性.     

Symbolic control of linear systems based on symbolic subsystems

This paper describes an approach to the control of continuous systems through the use of symbolic models describing the system behavior only at a finite number of points in the state space. These symbolic models can be seen as abstract representations of the continuous dynamics enabling the use of algorithmic controller design methods. We identify a class of linear control systems for which the loss of information incurred by working with symbolic subsystems can be compensated by feedback. We also show how to transform symbolic controllers designed for a symbolic subsystem into controllers for the original system. The resulting controllers combine symbolic controller dynamics with continuous feedback control laws and can thus be seen as hybrid systems. Furthermore, if the symbolic controller already accounts for software/hardware requirements, the hybrid controller is guaranteed to enforce the desired specifications by construction thereby reducing the need for formal verification.     

事件触发双模分布式预测控制

本文针对有界扰动作用下的线性离散大系统,提出了事件触发双模分布式预测控制设计方法.利用输入状态稳定性(input-to-state stability,ISS)理论建立了仅与子系统自身信息相关的事件触发条件.只有子系统满足相应的事件触发条件,才进行状态信息的传输和分布式预测控制优化问题的求解,并与邻域子系统交互最优解作用下的关联信息.当子系统进入不变集时,采用状态反馈控制律进行镇定,并与进入不变集的邻域子系统不再交互信息.分析了算法的递推可行性和系统的闭环稳定性,给出了扰动的上界.最后,通过车辆控制系统对算法进行仿真验证,结果表明,本文提出的方法能够有效降低优化问题的求解次数和关联信息的交互次数,节约计算资源和通信资源.     

Optimal control of ε-coupled and singularly perturbed distributed-parameter systems

Many distributed-parameter systems consist of interconnected subsystems involving fast and slow physical phenomena or reducing to a number of independent subsystems when a scalar parameter ε is zero. The purpose of this paper is to treat the control of such systems by invoking the ε-coupling and singular perturbation approaches developed by Kokotovic and his co-workers for lumped-parameter large-scale systems. In the case of ε- coupled distributed-parameter systems it is shown that the optimal state feedback matrix can be approximated by a Volterra-MacLaurin series with coefficients determined by solving two lower-order decoupled Riccati and linear equations. By using an mth-order approximation of the optimal feedback matrix, one obtains a (2m+1)th order approximation of the optimal performance function. In the singular perturbation approach the result is that for an O(ε²) suboptimal control one must solve two decoupled Riccati equations, one for the fast and one for the slow subsystem, and then construct appropriately the composite control law. By using only the Riccati equation for the slow subsystem, one obtains an O(ε) suboptimal control. The singular perturbation technique is then used to treat interconnected distributed-parameter systems involving may strongly coupled slow subsystems and weakly coupled fast subsystems.     

Robust decentralized eigenvalues clustering for multi-time-scale systems with perturbations

The formulation of the robust decentralized eigenvalues clustering with the design technique of the decentralized robust state feedback controller for multi-time-scale systems is facilitated. The multi-time-scale system, consisting of a slow subsystem and some fast subsystems with perturbations, is considered. From the eigenvalues locations of the multi-time-scale system, it is known that the locations of the fast subsystems are far from the slow subsystem ones. Furthermore, presented here are some sufficient conditions of robust eigenvalue clustering in specified regions for the uncertain slow subsystem and the uncertain fast subsystems with the parameter perturbations. An algorithm for the design of a decentralized robust state feedback controller for multitime-scale systems is then presented. It not only ensures that the eigenvalues of these subsystems are located in the desired regions, but also makes sure that all the eigenvalues of the whole system are in our desired regions. An illustrative example is adopted, and the result is satisfactory.     

Distributed receding horizon control for multi-vehicle formation stabilization

We consider the control of interacting subsystems whose dynamics and constraints are decoupled, but whose state vectors are coupled non-separably in a single cost function of a finite horizon optimal control problem. For a given cost structure, we generate distributed optimal control problems for each subsystem and establish that a distributed receding horizon control implementation is stabilizing to a neighborhood of the objective state. The implementation requires synchronous updates and the exchange of the most recent optimal control trajectory between coupled subsystems prior to each update. The key requirements for stability are that each subsystem not deviate too far from the previous open-loop state trajectory, and that the receding horizon updates happen sufficiently fast. The venue of multi-vehicle formation stabilization is used to demonstrate the distributed implementation.     

Identification of non-linear systems by recursive kernel regression estimates

Identification of non-linear, dynamical systems described by the Hammerstein model are discussed. Such a system consists of a multi-input single-output nonlinear, memoryless subsystem followed by a dynamic, linear subsystem. Outputs of both subsystems are corrupted by random noise. The parameters of the linear subsystem are identified by a correlation technique. The main contribution lies in estimating the non-linear, memoryless subsystem. The identification algorithm is based on the recursive kernel regression estimate. No restrictions are imposed on the functional form of the non-linearity as well on its continuity. We prove global convergence of the algorithm regardless of the distribution of the random input and for outputs with bounded moment of order greater than 2. The rate of convergence is obtained for the Lipschitz non-linearities and all input distributions.     

An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control

For nonlinear switched discrete-time systems with input constraints, this paper presents an open-closed-loop iterative learning control (ILC) approach, which includes a feedforward ILC part and a feedback control part. Under a given switching rule, the mathematical induction is used to prove the convergence of ILC tracking error in each subsystem. It is demonstrated that the convergence of ILC tracking error is dependent on the feedforward control gain, but the feedback control can speed up the convergence process of ILC by a suitable selection of feedback control gain. A switched freeway traffic system is used to illustrate the effectiveness of the proposed ILC law.