H-infinity performance optimization for networked control systems with limited communication channels

This paper studies the problems of H-infinity performance optimization and controller design for continuous-time NCSs with both sensor-to-controller and controller-to-actuator communication constraints （limited communication channels）. By taking the derivative character of network-induced delay into full consideration and defining new Lyapunov functions, linear matrix inequalities （LMIs）-based H-infinity performance optimization and controller design are presented for NCSs with limited communication channels. If there do not exist any constraints on the communication channels, the proposed design methods are also applicable. The merit of the proposed methods lies in their Jess conservativeness, which is achieved by avoiding the utilization of bounding inequalities for cross products of vectors. The simulation results illustrate the merit and effectiveness of the proposed H-infinity controller design for NCSs with limited communication channels.

Fault detection and optimization for networked control systems with uncertain time-varying delay

The observer-based robust fault detection filter design and optimization for networked control systems （NOSs） with uncer- tain time-varying delays are addressed. The NCSs with uncertain time-varying delays are modeled as parameter-uncertain systems by the matrix theory. Based on the model, an observer-based residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of the linear matrix inequality. Furthermore, a time domain opti- mization approach is proposed to improve the performance of the fault detection system. To prevent the false alarms, a new thresh- old function is established, and the solution of the optimization problem is given by using the singular value decomposition （SVD） of the matrix. A numerical example is provided to illustrate the effectiveness of the proposed approach.

Analysis and optimization of delays in networked control systems

The minimization problem of time delays in networked control system （NCS） is concered, which is a hot area of such research field. First, some analysis and comments on time-delayed NCS model listed in previous work are given. Then, time delay minimization problem based on average behavior of network queuing delay is presented. Under fixed routing scheme and certain optimization performance indexes, the delay minimization problem is translated into convex optimization problem. And the solution of the delay minimization problems is attained through optimized allocation of flow rates among network links.

A Control Scheme Based on Online Delay Evaluation for a Class of Networked Control Systems

A control scheme that integrates control technology with communication technology to solve the delay problem is introduced for a class of networked control systems: Networked Half-Link Systems (NHLS). Concretely, we use the master-slave clock synchronization technology to evaluate the delays online, and then the LQ optimal control based on delays is adopted to stabilize the controlled plant. During the clock synchronization process, the error of evaluated delays is inevitably induced from the clock synchronization error, which will deteriorate the system performances, and even make system unstable in certain cases. Hence, the discussions about the clock error, and the related control analysis and design are also developed. Specifically, we present the sufficient conditions of controller parameters that guarantee the system stability, and a controller design method based on the error of delays is addressed thereafter. The experiments based on a CANbus platform are fulfilled, and the experimental results verify the previous analytic results finally.

An Optimal Hybrid Learning Approach for Attack Detection in Linear Networked Control Systems

A novel learning-based attack detection and estimation scheme is proposed for linear networked control systems(NCS),wherein the attacks on the communication network in the feedback loop are expected to increase network induced delays and packet losses,thus changing the physical system dynamics.First,the network traffic flow is modeled as a linear system with uncertain state matrix and an optimal Q-learning based control scheme over finite-horizon is utilized to stabilize the flow.Next,an adaptive observer is proposed to generate the detection residual,which is subsequently used to determine the onset of an attack when it exceeds a predefined threshold,followed by an estimation scheme for the signal injected by the attacker.A stochastic linear system after incorporating network-induced random delays and packet losses is considered as the uncertain physical system dynamics.The attack detection scheme at the physical system uses the magnitude of the state vector to detect attacks both on the sensor and the actuator.The maximum tolerable delay that the physical system can tolerate due to networked induced delays and packet losses is also derived.Simulations have been performed to demonstrate the effectiveness of the proposed schemes.

STUDY ON THE OPTIMIZATION OF TRANSPORT CONTROL POLICY IN COMMUNICATION NETWORK

In communication networks with policy-based Transport Control on-Demand (TCoD) function,the transport control policies play a great impact on the network effectiveness. To evaluate and optimize the transport policies in communication network,a policy-based TCoD network model is given and a comprehensive evaluation index system of the network effectiveness is put forward from both network application and handling mechanism perspectives. A TCoD network prototype system based on Asynchronous Transfer Mode/Multi-Protocol Label Switching (ATM/MPLS) is introduced and some experiments are performed on it. The prototype system is evaluated and analyzed with the comprehensive evaluation index system. The results show that the index system can be used to judge whether the communication network can meet the application requirements or not,and can provide references for the optimization of the transport policies so as to improve the communication network effectiveness.

A global optimization algorithm based on multi-loop neural network control

This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller,in which the objective function that is used is the control plant of each sub-control system.To obtain the global optimization solution from a control plant that has many local minimum points,a transformation function is presented.On the one hand,this approach changes a complex objective function into a simple function under the condition of an unchanged globally optimal solution,to find the global optimization solution more easily by using a multi-loop control system.On the other hand,a special neural network(in which the node function can be simply positioned locally)that is composed of multiple transformation functions is used as the controller,which reduces the possibility of falling into local minimum points.At the same time,a filled function is presented as a control law;it can jump out of a local minimum point and move to another local minimum point that has a smaller value of the objective function.Finally,18 simulation examples are provided to show the effectiveness of the proposed method.

A Novel Approach to Design Robust Fault Detection Filter for Networked Control Systems

In this paper, an approach for designing robust fault detection filter (RFDF) of networked control systems (NCSs) with unknown inputs is studied. The design aims at implementing the optimal trade-off between robustness of unknown inputs (including the item produced by networked-induced delay) and sensitivity of fault. The key design issue is to introduce an optimal fault detection filter based on NCSs with the control law compensation as the reference residual model of NCSs and to formulate the RFDF design as a model-matching problem. By applying H∞ optimization technique, linear matrix inequality (LMI) approach is given to solve the model-matching problem. The validity of the proposed approach is shown by a numerical example.

Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.

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 onthe 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.

Observer-based Adaptive Optimal Control for Unknown Singularly Perturbed Nonlinear Systems With Input Constraints

This paper introduces an observer-based adaptive optimal control method for unknown singularly perturbed nonlinear systems with input constraints. First, a multi-Time scales dynamic neural network MTSDNN observer with a novel updating law derived from a properly designed Lyapunov function is proposed to estimate the system states. Then, an adaptive learning rule driven by the critic NN weight error is presented for the critic NN, which is used to approximate the optimal cost function. Finally, the optimal control action is calculated by online solving the Hamilton-Jacobi-Bellman HJB equation associated with the MTSDNN observer and critic NN. The stability of the overall closed-loop system consisting of the MTSDNN observer, the critic NN and the optimal control action is proved. The proposed observer-based optimal control approach has an essential advantage that the system dynamics are not needed for implementation, and only the measured input U+002F output data is needed. Moreover, the proposed optimal control design takes the input constraints into consideration and thus can overcome the restriction of actuator saturation. Simulation results are presented to confirm the validity of the investigated approach. © 2014 Chinese Association of Automation.

Optimal distributed resource allocation in a wireless sensor network for control systems

Wireless technology is applied increasingly in networked control systems. A new form of wireless network called wireless sensor network can bring control systems some advantages, such as flexibility and feasibility of network deployment at low costs, while it also raises some new challenges. First, the communication resources shared by all the control loops are limited. Second, the wireless and multi-hop character of sensor network makes the resources scheduling more difficult. Thus, how to effectively allocate the limited communication resources for those control loops is an important problem. In this paper, this problem is formulated as an optimal sampling frequency assignment problem, where the objective function is to maximize the utility of control systems, subject to channel capacity constraints. Then an iterative distributed algorithm based on local buffer information is proposed. Finally, the simulation results show that the proposed algorithm can effectively allocate the limited communication resource in a distributed way. It can achieve the optimal quality of the control system and adapt to the network load changes.

Optimal Neuro-Control Strategy for Nonlinear Systems With Asymmetric Input Constraints

In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints.Then,we develop a Hamilton-Jacobi-Bellman equation(HJBE),which arises in the discounted cost optimal control problem.To obtain the optimal neurocontroller,we utilize a critic neural network(CNN)to solve the HJBE under the framework of reinforcement learning.The CNN's weight vector is tuned via the gradient descent approach.Based on the Lyapunov method,we prove that uniform ultimate boundedness of the CNN's weight vector and the closed-loop system is guaranteed.Finally,we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

一种农业自动化灌溉系统的高效能设计研究

以进一步提升农业自动化灌溉系统的作业效率为研究目标,选取其核心结构组件布局作为研究对象,基于通信控制优化理念展开高效能设计研究。结合自动化灌溉系统的运行机理,从通信网络架构分层角度入手,建立灌溉系统参数计算处理模型。针对系统软件控制和硬件配置完成优化,选定灌溉作物进行灌溉试验,结果表明:此通信网络架构理念下的控制优化布局,可实现灌溉系统的响应准确率与决策准确率同步提升,系统运行稳定有效;整体系统的灌溉效率可达94.55%,满足智能化灌溉控制设计要求,具有很好的应用推广价值。研究结果对于灌溉系统向智能化精准化提升有较好的启示意义。

Neuro-Optimal Guidance Control for Lunar Soft Landing

Returning to moon has become a top topic recently. Many studies have shown that soft landing is a challenging problem in lunar exploration. The lunar soft landing in this paper begins from a 100 km circular lunar parking orbit. Once the landing area has been selected and it is time to deorbit for landing, a ΔV burn of 19.4 m/s is performed to establish a 100×15 km elliptical orbit. At perilune, the landing jets are ignited, and a propulsive landing is performed. A guidance and control scheme for lunar soft landing is proposed in the paper, which combines optimal theory with nonlinear neuro-control. Basically, an optimal nonlinear control law based on artificial neural network is presented, on the basis of the optimum trajectory from perilune to lunar surface in terms of Pontryagin's maximum principle according to the terminal boundary conditions and performance index. Therefore some optimal control laws can be carried out in the soft landing system due to the nonlinear mapping function of the neural network. The feasibility and validity of the control laws are verified in a simulation experiment.

Optimizing control of coal flotation by neuro-immune algorithm

Coal flotation is widely used to separate commercially valuable coal from the fine ore slurry, and is an industrial process with nonlinear, multivariable, time-varying and long time-delay characteristics. The online detection of ash content of products as the operation performance evaluation in the flotation system is extraordinarily difficult because of the low solid content and numerous micro-bubbles in the slurry. Moreover, it is time-consuming by manual analysis. Consequently, the optimal separation is not usually maintained. A novel technique, called the neuro-immune algorithm (NIA) inspired by the biological nervous and immune systems, is presented in this paper for predicting the ash content of clean coal and performing the optimizing control to the coal flotation system. The proposed algorithm integrates the deeply-studied artificial neural network (ANN) and the developing artificial immune system (AIS). A two-layer back-propagation network was constructed offline based on the historical process data under the best system situation, using five parameters: the flow and the density of raw slurry, the input flows of water, the kerosene and the GF oil, as the inputs and the ash content of clean coal as the output. The immune cell of AIS is made up of six parameters above as the antigen. The cytokine based clone selection algorithm is used to produce the relative antibody. The detailed computation procedures about the hybrid neuro-immune algorithm are minutely discussed. The ash content of clean coal was predicted by NIA using the practical process data s: (308.6 174.7 146.1 43.6 4.0 9.4), and the absolute difference between the actual and computed ash content values was 0.0967%. The optimizing control on NIA was simulated considering two different situations where the ash content of clean coal was controlled downward from 10.00% or upward from 9.20% predicted by ANN to the target value 9.50%. The results indicate that the target ash content and the value of controlling parameters are obtained after several control cycles.