网络化多智能体主从式预测编队控制

为解决通讯延时对编队控制造成的不利影响,研究网络化多智能体在通讯延时情形下的主从式预测编队控制问题,提出一种主从式预测编队控制架构.在该架构中,所有智能体都基于延时状态预测自身当前时刻状态,用于主动补偿反馈通道延时.主智能体将自身未来预测状态发送给各从智能体,从而主动补偿主从智能体间的通讯延时.仿真结果验证了所提出主从式预测编队控制架构的可行性和灵活性.     

一种联合收获机脱粒滚筒转速的鲁棒预测控制

联合收获机的轴流式脱粒滚筒转速系统具有强非线性、不确定、滞后、大干扰等特点,以收获机的行走速度和喂入量为控制量,滚筒转速为被控量,建立闭环控制系统,设计时变的鲁棒稳定控制器.在频域内基于比例微分校正技术补偿无级变速传动的滞后环节影响;基于近似模型预测的连续预测控制律保证了大范围工作的控制系统性能;同时,设计收敛的非线性...     

四旋翼无人飞行器的轨迹跟踪与滑模事件驱动控制

四旋翼飞行器作为一个典型的欠驱动的系统,具有强耦合、非线性等特性.针对飞行器外部干扰、和通信资源受限条件下的轨迹跟踪控制问题,进行滑模事件驱动控制方法的研究.首先,分析动力学特性,通过时间尺度分解方法将系统解耦成位置子系统和姿态子系统.其次,将位置子系统转化为严格反馈形式,设计反步滑模控制器,实现位置轨迹稳定跟踪;针对姿态子系统存在时变有界扰动及通信受限,设计滑模事件驱动控制律,在抑制干扰的同时实现对虚拟姿态跟踪指令的跟踪.根据Lyapunov分析方法证明了所设计控制器的稳定性,并通过理论分析证明闭环控制系统不会出现Zeno现象.最后,仿真结果验证了滑模事件驱动控制律在存在外部扰动和通信受限时四旋翼无人飞行器轨迹跟踪的鲁棒性.     

一种预测控制方法及其仿真

针对现有预测控制方法的最优控制律的计算较为复杂的问题,提出了一种新的预测控制算法.与现有预测控制算法相比,该算法结构简单,计算量小,鲁棒性强.对算法在一个采样周期内控制律的求取进行了较详细说明.通过对线性和非线性系统的仿真,说明了该算法对线性系统能达到很好的预测控制效果,同时对时变、非线性系统也具有较好的适应性,并具有较强的抗干扰能力.对于快时变系统,该算法需要根据实际时变特性对相关系数加以调整,来达到较满意的效果.     

基于群决策考虑属性效用一致性的DEA他评交叉效率公共权重排序法

针对一类主从式异构线性网络化多智能体系统,考虑每个智能体的反馈通道和前向通道中存在随机网络诱导时延和数据包丢失问题,采用预测控制方法,提出一种基于观测器的网络化多智能体协同输出跟踪控制方案.在该方案中,主智能体在每一时刻基于自身滞后输出和系统参考信号,计算一组控制预测序列和输出预测序列,前者用以主动补偿主智能体控制回路中的随机网络诱导时延和数据包丢失,后者被发往从智能体;从智能体在每一时刻基于主智能体发送过来的输出预测序列和自身滞后输出,计算一组控制预测序列,用以主动补偿从智能体控制回路中的随机网络诱导时延和数据包丢失;随后推导闭环网络化多智能体控制系统的稳定性,并通过实验验证该方案的有效性和可行性.     

具有多传输通道系统的网络化预测控制

本文研究了一类具有多传输通道网络化系统的控制问题,基于网络化预测控制方法,提出了一种改进型的分布式预测补偿方式,从而更有效地利用反馈数据来提高控制系统的性能.对闭环网络化预测控制系统进行分析,得到其稳定性条件,特别地,在模型精确已知和多传输通道的时延为定常的情况下,该条件将会退化为本地控制的闭环系统稳定性条件.上述结论的好处是网络化预测控制系统中状态观测器和控制器的设计可以参考本地控制.通过球杆系统算例验证本文所提方法的正确性和有效性.     

非线性系统的蚁群优化预测PID控制

针对非线性、时变及大惯性系统的控制问题,提出了一种基于蚁群算法的预测PID控制算法。该算法以神经网络作为预测模型,将预测控制和PID控制相结合,并用蚁群算法在线优化控制器参数,其中以常规的Ziegler-N ichols方法整定的控制器参数为基础,选取蚁群优化变量的动态搜索区间。该算法考虑了控制能量受限情况下,非线性系统的预测控制问题。计算机仿真结果表明,该非线性控制方案具有较好的鲁棒性,相对传统PID控制策略还表现出了良好的动态性能,能够满足对再热汽温对象的控制要求。     

自动染料配浆控制算法研究

在配浆控制过程中,是一个多干扰时变的系统,且有很强的非线性,存在一定的滞后,常规的自动控制算法不能满足快速,精确的调整目标.根据调浆的过程,提出了一套基于对参考轨迹寻优的ESO-GPC算法,通过扩张状态观测器(ESO)对非线性系统的动态补偿线性化,使广义预测控制的计算量大大降低,对干扰提前进行补偿,提高了系统的响应速度和抗干扰能力.基于神经网络对GPC(广义预测控制)的参考轨迹在线寻优调整,进一步提高系统的鲁棒性,优化控制轨迹.     

神经网络广义预测控制在锅炉燃烧系统中的应用

针对锅炉燃烧系统的非线性、大延迟、时变、干扰频繁等特点,以煤粉浓度为中间被调量,将神经网络、广义预测控制、串级控制相结合,设计了基于神经网络模型的广义预测串级控制系统.该控制方法克服了单纯PID控制对大惯性大延迟对象调节品质差、抗干扰性弱的缺点,神经网络预测器有效地补偿了传统预测控制基于线性模型的局限性.将该控制算法用于燃烧系统中主汽压力对象的控制,仿真结果表明该方法具有较强的跟踪性能和抗干扰能力及良好的动静态性能指标.     

一类非线性时滞过程的传感器主动容错控制

通过结合非线性过程的一般模型控制(GMC)、强跟踪预测器(STP)和强跟踪滤波器(STF),本文提出了一类具有输入时滞非线性时变过程的传感器主动容错控制方法.基于强跟踪预测器对未来状态的预测,传统的一般模型控制被扩展到一类具有输入时滞的非线性过程.然后采用强跟踪滤波器估计过程状态及传感器偏差,传感器偏差估计用于驱动一个故障检测逻辑.当某一传感器故障被检测出来时,STF的状态估计值将用于重构过程输出(代替真实输出),此重构输出被STP用于继续进行状态预测,从而确保系统性能.最后,三容水箱系统仿真结果证明该方法的有效性.     

Coordination of Networked Nonlinear Multi-Agents Using a High-Order Fully Actuated Predictive Control Strategy

This paper is concerned with the coordinative control problem of networked nonlinear multi-agents (NNM) with communication delays. A high-order fully actuated (HOFA) model is introduced to describe the nonlinear multi-agents. Based on this model, a HOFA predictive coordination method is proposed to compensate for the communication delays actively and achieve simultaneous stability and consensus. This method largely simplifies the design of networked nonlinear multi-agents and makes the control performance be same for networked nonlinear multi-agents with and without communication delays. The analysis on the closed-loop systems derives the simultaneous stability and consensus criteria of networked nonlinear multi-agents using the HOFA predictive coordination method. With the presented way of designing HOFA predictive coordination controllers, a simulated example demonstrates the advantages of the proposed method.      

A two-tier control architecture for nonlinear process systems with continuous/asynchronous feedback

In this work, we introduce a two-tier control architecture for nonlinear process systems with both continuous and asynchronous sensing and actuation. This class of systems arises naturally in the context of process control systems based on hybrid communication networks (i.e. point-to-point wired links integrated with networked wired or wireless communication) and utilising multiple heterogeneous measurements (e.g. temperature and concentration). Assuming that there exists a lower-tier control system which relies on point-to-point communication and continuous measurements to stabilise the closed-loop system, we propose to use Lyapunov-based model predictive control to design an upper-tier networked control system to profit from both the continuous and the asynchronous measurements as well as from additional networked control actuators. The proposed two-tier control system architecture preserves the stability properties of the lower-tier controller while improving the closed-loop performance. The theoretical results are demonstrated using two different chemical process examples.     

On the model-based approach to nonlinear networked control systems

The problem of model-based stabilization of a nonlinear system based on its approximate discrete-time model is addressed, under the assumption that both the feedforward and the feedback paths are subject to network-induced constraints. These constraints include irregularity of the transfer intervals, time-varying communication delays, and the possibility of packet losses. A communication protocol that copes with these constraints is proposed. A “Stability+performance recovery” result for the nonlinear model-based networked control system (NCS) is formulated and proven.Simulation results presented confirm that the proposed method improves the maximum allowable transfer interval.     

An output formation consensus control for leader–follower networked multi-agent systems under communication constraints and switching topologies

In this paper, the formation consensus problem for a class of leader–follower networked multi-agent systems under communication constraints and switching topologies is investigated. A networked predictive control scheme is proposed to achieve stability and output formation consensus with the switching topology, capable of compensating for data loss and time delays in the network. By equating the whole closed-loop networked multi-agent system with the proposed control scheme to the corresponding switched system, the sufficient and necessary condition of output formation consensus and stability for agents is given. Finally, using three-degree-of-freedom air-bearing spacecraft simulators as the control objects, the proposed scheme is demonstrated to be able to actively compensate for the communication constraints through numerical simulations, and it is also verified to have a good control performance by further realizing the formation task of the simulators through practical experiments.     

Tracking Control of Multi-Agent Systems Using a Networked Predictive PID Tracking Scheme

With the rapid development of network technology and control technology, a networked multi-agent control system is a key direction of modern industrial control systems, such as industrial Internet systems. This paper studies the tracking control problem of networked multi-agent systems with communication constraints, where each agent has no information on the dynamics of other agents except their outputs. A networked predictive proportional integral derivative(PPID) tracking scheme is proposed t...     

网络化预测控制系统的设计,分析与实时实现

采用预测控制策略, 讨论网络化控制系统的设计, 分析与实时实现. 对网络化控制系统的特点进行详细分析, 表明网络化控制系统与传统的控制系统有很大的不同. 为了达到期望的闭环网络化控制系统的性能, 引入网络化预测控制方案, 并对网络化预测控制系统的设计、稳定性分析和实时实现进行深入研究. 本文采用仿真和实际实验, 展示网络化预测控制方案可以弥补随机网络通信延迟和数据丢失、达到期望的控制性能、并具有良好的闭环系统稳定性.     

Economic model predictive control of nonlinear singularly perturbed systems

We focus on the development of a Lyapunov-based economic model predictive control (LEMPC) method for nonlinear singularly perturbed systems in standard form arising naturally in the modeling of two-time-scale chemical processes. A composite control structure is proposed in which, a “fast” Lyapunov-based model predictive controller (LMPC) using a quadratic cost function which penalizes the deviation of the fast states from their equilibrium slow manifold and the corresponding manipulated inputs, is used to stabilize the fast dynamics while a two-mode “slow” LEMPC design is used on the slow subsystem that addresses economic considerations as well as desired closed-loop stability properties by utilizing an economic (typically non-quadratic) cost function in its formulation and possibly dictating a time-varying process operation. Through a multirate measurement sampling scheme, fast sampling of the fast state variables is used in the fast LMPC while slow-sampling of the slow state variables is used in the slow LEMPC. Appropriate stabilizability assumptions are made and suitable constraints are imposed on the proposed control scheme to guarantee the closed-loop stability and singular perturbation theory is used to analyze the closed-loop system. The proposed control method is demonstrated through a nonlinear chemical process example.     

Event-triggered controller design of nonlinear discrete-time networked control systems in T-S fuzzy model

This article is concerned with event-triggered fuzzy control design for a class of discrete-time nonlinear networked control systems (NCSs) with time-varying communication delays. Firstly, a more general mixed event-triggering scheme (ETS) is proposed. Secondly, considering the effects of the ETS and communication delays, based on the T-S fuzzy model scheme and time delay system approach, the original nonlinear NCSs is reformulated as a new event-triggered networked T-S fuzzy systems with interval time-varying delays. Sufficient conditions for uniform ultimately bound (UUB) stability are established in terms of linear matrix inequalities (LMIs). In particular, the quantitative relation between the boundness of the stability region and the triggering parameters are studied in detail. Thirdly, a relative ETS is also provided, which can be seen as a special case of the above proposed mixed ETS. As a difference from the preceding results, sufficient conditions on the existence of desired fuzzy controller are derived to ensure the asymptotic stability of the closed-loop system with reduced communication frequency between sensors and controllers. Moreover, a co-design algorithm for simultaneously determining the gain matrices of the fuzzy controller and the triggering parameters is developed. Finally, two illustrative examples are presented to demonstrate the advantage of the proposed ETS and the effectiveness of the controller design method.     

Control of a group of systems whose communication channels are assigned by a semi-Markov process

This technical note is concerned with the problem of medium access constraint for a group of networked systems. The scheduling of each subsystem is defined by a stochastic protocol, which can be modelled by a semi-Makov chain with a time-varying transition probability matrix. The resulting closed-loop nonlinear systems are a semi-Markovian jump system with delay. Sufficient conditions for exponential mean-square stability of the resulting closed-loop systems are derived via a Lyapunov–Krasovskii method. Based on the stability criterion, the controller gain of each subsystem is designed. A simulation example is used to demonstrate the effectiveness of proposed method.