模糊控制器的结构化分析及系统化设计方法

对于模糊控制器的输入变量,采用一种新型的不均匀、全交迭、三角形的隶属度函数,推导了两输入（e,△e)－输出（△u）的典型模糊控制器输出的解析表达式,并对最常用的输入变量各取5个模糊变量的情况进行分析。在此基础上提出一种模糊控制系统的系统化设计方法,可根据已有的PI／PD控制器参数设计相应的模糊控制器参数。仿真实验说明了该方法的有效性。     

Distributed event-triggered optimal bipartite consensus control for multiagent systems with input delay via reinforcement learning method

In this article, an optimal bipartite consensus control (OBCC) scheme is proposed for heterogeneous multiagent systems (MASs) with input delay by reinforcement learning (RL) algorithm. A directed signed graph is established to construct MASs with competitive and cooperative relationships, and model reduction method is developed to tackle input delay problem. Then, based on the Hamilton–Jacobi–Bellman (HJB) equation, policy iteration method is utilized to design the bipartite consensus controller, which consists of value function and optimal controller. Further, a distributed event-triggered function is proposed to increase control efficiency, which only requires information from its own agent and neighboring agents. Based on the input-to-state stability (ISS) function and Lyapunov function, sufficient conditions for the stability of MASs can be derived. Apart from that, RL algorithm is employed to solve the event-triggered OBCC problem in MASs, where critic neural networks (NNs) and actor NNs estimate value function and control policy, respectively. Finally, simulation results are given to validate the feasibility and efficiency of the proposed algorithm.     

Optimal digital controller and observer design for multiple time-delay transfer function matrices with multiple input–output time delays

In this article, we address the optimal digital design methodology for multiple time-delay transfer function matrices with multiple input–output time delays. In our approach, the multiple time-delay analogue transfer function matrix with multiple input–output time delays is minimally realised using a continuous-time state-space model. For deriving an explicit form of the optimal digital controller, the realised continuous-time multiple input–output time-delay system is discretised, and an extended high-order discrete-time state-space model is constructed for discrete-time LQR design. To derive a low-order optimal digital observer for the multiple input–output time-delay system, the multiple time-delay state obtained from the multiple time-delay outputs is discretised. Then, the well-known duality concept is employed to design an optimal digital observer using the low-order discretised multiple input time-delay system together with the newly discretised multiple time-delay state. The proposed approach is restricted to multiple time-delay systems where multiple time delays arise only in the input and output, and not in the state.     

Decentralised output-feedback LQG control with one-step communication delay

This paper studies the output-feedback LQG control of a two-player system with one-step communication delay. A novel information pattern is considered and a new controller structure is introduced. The proposed controller consists of two parts: the first part is based on estimated system state; the second part is based on current local measurement output. The form of the optimal controller is established using the method of independence decomposition. A necessary condition is established to construct the optimal controller gains. Two iterative algorithms are used to find the optimal gains numerically. Finally, the effectiveness of the theoretical results is illustrated through a numerical example.     

分域设计迭代学习控制器抑制非重复性扰动

为了抑制迭代方向上已知重复样式的非重复性输出扰动,提出了迭代学习控制(Iterative Learning Control,ILC)的分域算法。时间域内设计传统PID型迭代学习控制器,并且优化其参数;迭代域内利用内模原理抑制非重复性输出扰动,跟踪期望轨迹;利用加权思想将两者相结合,得到迭代学习控制器的分域设计算法。相对于已有算法,建立了针对一般扰动的设计框架,并且合理配置了算法的参数,使收敛速度及精度有所提高。仿真结果说明了该算法的有效性。     

防抱制动系统滑模状态观测和控制系统仿真

该文在考虑不平路面随机激励作用下车辆垂向振动的基础上 ,首先建立了四分之一车辆制动模型 ,而后充分运用滑移模式变结构的分析和设计方法 ,提出了车轮最佳滑移率的滑模实时在线辨识滑模优化算法 ,在对系统可观测性论证的基础上 ,设计了非线性滑模状态观测器 ,给出了单通道防抱制动系统基于滑移率的滑模控制算法 ,通过计算机仿真 ,验证了该控制算法的可行性和有效性 ,为设计具有高鲁棒性的防抱制动系统做了一定的理论探索和仿真工作     

Virtual reference feedback tuning: a direct method for the design of feedback controllers

This paper considers the problem of designing a controller for an unknown plant based on input/output measurements. The new design method we propose is direct (no model identification of the plant is needed) and can be applied using a single set of data generated by the plant, with no need for specific experiments nor iterations. It is shown that the method searches for the global optimum of the design criterion and that, in the case of restricted complexity controller design, the achieved controller is a good approximation of the restricted complexity global optimal controller. A simulation example shows the effectiveness of the method.     

基于蜂窝结构的改进混合无线传感器网络覆盖优化算法

基于蜂窝结构的混合无线传感器网络（HWSN）覆盖优化算法HWSNBCS存在移动节点平均移动距离较大的问题,为此,提出一种改进的HWSN覆盖优化算法IHWSNBCS。寻找移动传感器节点初始位置与通过HWSNBCS算法得出的候选目标位置之间的最优匹配,将移动节点移动距离之和最小化问题转化为二分图最优匹配问题,利用带权二分图匹配算法KM寻找该匹配问题的最优解,从而得到移动节点最终的目标位置,并实现对HWSNBCS算法移动节点平均移动距离的进一步优化。实验结果表明,IHWSNBCS算法在取得与HWSNBCS算法相同网络覆盖率的前提下,移动节点的平均移动距离减少幅度达到38.87%～43.28%,单个移动节点的最大移动距离减少幅度达到22.65%～66.58%,降低了系统因重新部署移动传感器节点所产生的能耗以及单个传感器节点因能量耗尽而失效的概率,从而延长了网络生命周期,同时,IHWSNBCS的ΔCov-Dist性能指标为HWSNBCS算法的1.64～1.76倍,表明移动节点移动相同距离时IHWSNBCS算法的网络覆盖率提升更大。     

PLS based dEWMA run-to-run controller for MIMO non-squared semiconductor processes

This paper focuses on the modification of the PLS (partial least squares) modeling. The new method allows incorporation of a dEWMA (double exponentially weighted moving average) control algorithm into the standard run-to-run controller design for semiconductor processes. The resulting structure of the PLS model can extract the strongest relationship between the input and the output variables. It is particularly useful for inherent noise suppression. In addition, the resulting non-square MIMO control system can be decomposed into a multi-loop control system by employing pre-compensators and post-compensators of the PLS model, which is constructed from the input and output loading matrices. Subsequently, the conventional dEWMA controller can be separately and directly applied to each SISO control loop. The performance of the proposed method is illustrated through a chemical–mechanical polishing process in the manufacturing of the semiconductor.     

Optimal interval controller design for uncertain systems

The interval controller design is a hot issue for uncertain systems, whereas how to design an optimal interval controller under the premise of ensuring system stability is a difficult problem that needs further study. This paper mainly aims at the single input single output uncertain system to propose an optimal interval controller based on the Kharitonov theorem and an interval optimization algorithm, which can guarantee the stability and optimization of a closed-loop interval system. According to the Kharitonov theorem, the optimal interval controller design can be transformed into an optimal controller synthesis issue of multiple vertex objects. An interval particle swarm optimization (IPSO) algorithm is then used to optimize the quadratic performance index with interval variables for each vertex object to obtain the solution domains of the controller parameters, and the vertex method is utilized to prevent interval width expansion or divergence in the iteration. Finally, the intersections of the solution domains for all vertex objects are obtained as the optimal interval solution of interval controller parameters. In addition, the stability verification approach of the closed-loop system and the empirical rule to select the interval particle width are given. Simulation results for typical examples demonstrate that the designed interval controller not only performs optimally but also can robustly stabilize the interval system.     

Data-driven output-feedback fault-tolerant control for unknown dynamic systems with faults changing system dynamics

This paper studies the data-driven output-feedback fault-tolerant control (FTC) problem for unknown dynamic systems with faults changing system dynamics. In a framework of active FTC, two basic issues are addressed: the fault detection employing only the measured input–output information; the controller reconfiguration to achieve optimal output-feedback control in the presence of multiple faults. To detect faults and write the system state via the input–output data, an approach to data-driven design of a residual generator with a full-rank transformation matrix is presented. An output-feedback approximate dynamic programming method is developed to solve the optimal control problem under the condition that the unknown linear time-invariant discrete-time plant has multiple outputs. According to the above results and the proposed input–output data-based value function approximation structure of time-varying plants, a model-free output-feedback FTC scheme considering optimal performance is given. Finally, two numerical examples and a practical example of a DC motor control system are used to demonstrate the effectiveness of the proposed methods.     

H∞ control via output feedback for linear systems with time-varying delays in state and control input

In this paper, an observer-based H_∞ controller for systems with time-varying delays in state and control input is proposed. Using the solution of proposed modified algebraic Riccati equation, we can construct a controller which depends on the maximum value of the time derivative of time-varying delay and guarantees a prescribed H_∞ norm bound of the closed-loop transfer matrix from the disturbance to the controlled output. A given example illustrates the availability of the proposed design method.     

Multi‐objective robust fuzzy fractional order proportional–integral–derivative controller design for nonlinear hydraulic turbine governing system using evolutionary computation techniques

The present paper proposes a novel multi‐objective robust fuzzy fractional order proportional–integral–derivative (PID) controller design for nonlinear hydraulic turbine governing system (HTGS) by using evolutionary computation techniques. The fuzzy fractional order PID (FOPID) controller takes closed loop error and its fractional derivative as inputs and performs fuzzy logic operations. Then, it produces the output through the fractional order integrator. The predominant advantages of the proposed controller are its capability to handle complex nonlinear processes like HTGS in heuristic manner, due to fuzzy incorporation and extending an additional flexibility in tuning the order of fractional derivative/integral terms to enhance the closed loop performance. The present work formulates the optimal tuning problem of fuzzy FOPID controller for HTGS as a multi‐objective one instead of a traditional single‐objective one towards satisfying the conflicting criteria such as less settling time and minimum damped oscillations simultaneously to ensure the improved dynamic performance of HTGS. The multi‐objective evolutionary computation techniques such as non‐dominated sorting genetic algorithm‐II (NSGA‐II) and modified NSGA‐II have been utilized to find the optimal input/output scaling factors of the proposed controller along with the order of fractional derivative/integral terms for HTGS system under no load and load turbulence conditions. The performance of the proposed fuzzy FOPID controller is compared with PID and FOPID controllers. The simulations have been conducted to test the tracking capability and robust performance of HTGS during dynamic set point changes for a wide range of operating conditions and model parameter variations, respectively. The proposed robust fuzzy FOPID controller has ensured better fitness value and better time domain specifications than the PID and FOPID controllers, during optimization towards satisfying the conflicting objectives such as less settling time and minimum damped oscillations simultaneously, due to its special inheritance of fuzzy and FOPID properties.     

Second-order terminal sliding mode control of uncertain multivariable systems

A second-order terminal sliding mode controller for uncertain multivariable systems is proposed in this paper. The controller adopts the hierarchical control structure. The paper derives the state transform matrices which are used to transform a multivariable linear system to the block controllable form consisting of two subsystems, an input–output subsystem and a stable internal dynamic subsystem. The proposed controller utilizes a non-singular terminal sliding mode manifold for the input–output subsystem to realize fast convergence and better tracking precision. Meanwhile, a chattering-free second-order terminal sliding mode control law is presented. The stability of uncertain multivariable systems can be realized using the proposed controller. A derivative estimator is utilized in the paper to estimate the derivatives of the sliding mode functions for the controller. The simulation results are presented to validate the design method.     

嵌入式电子节能控制器的设计与实现

节能控制能够有效降低能耗,对保护环境等方面具有重要影响;但目前大多数电子节能控制器都是通过采用单片机技术和双向晶闸管过零触发交流调压电路对电子节能控制器进行设计;通过介绍电子的负荷特点和节能原理,分析电子节能控制器的硬件组成电路,并对电子节能控制器的主要软件程序的流程图进行设计,完成电子节能控制器设计;但这种方法节能控制效果较低,难以保证电子节能控制器性能,为此,提出一种基于模糊PID控制的嵌入式电子节能控制器设计与实现方法;首先通过对嵌入式电子节能控制器的处理器、电源电路、复位电路、系统时钟电路、JTAG接口电路、D/A转换电路、功放电路、双极性电源电路以及嵌入式电子节能控制器硬件PCB板器件布局等的设计,完成嵌入式电子节能控制器硬件设计;在此基础上,选用模糊PID控制方法对嵌入式电子节能控制器进行设计;通过分析模糊PID控制原理,介绍加入自调节因子的模糊PID控制的算法设计,以此确定输入输出隶属度函数,再利用模糊推理和模糊规则,得到电子节能控制器的模糊控制过程,从而完成嵌入式电子节能控制器的设计;实验证明,所提方法能够有效提高嵌入式电子节能控制器的节能控制效果,具有良好的使用价值。     

LQ suboptimal decentralised controllers with disturbance rejection property for hierarchical systems

This article is concerned with decentralised output regulation of hierarchical systems subject to input and output disturbances. It is assumed that the disturbance can be represented as the output of an autonomous linear time invariant (LTI) system with an unknown initial state. The primary objective is to design a decentralised controller with the property that not only does it reject the degrading effect of the disturbance on the output (to achieve a satisfactory steady-state performance), it also results in a small linear quadratic (LQ) cost function (implying a good transient behaviour). To this end, the underlying problem is treated in two phases. In the first step, a number of modified systems are defined in terms of the original system. The problem of designing an LQ centralised controller which stabilises all the modified systems and rejects the disturbance in the original system is considered, and it is shown that this centralised controller can be found efficiently by solving a linear matrix inequality (LMI) problem. In the second step, a method recently presented in the literature is exploited to decentralise the designed centralised controller. It is shown that the obtained controller satisfies the prescribed design specifications including disturbance rejection. Finally, in a more pragmatic context, the system is assumed to be subject to input delay, and a robustness analysis is carried out accordingly. Simulation results elucidate the efficacy of the proposed control law.     

Adaptive task-space synchronisation of networked robotic agents without task-space velocity measurements

In this paper, we investigate the problem of task-space synchronisation of multiple robotic agents in the presence of uncertain kinematics and dynamics. Our control objective is to realise synchronisation without the measurements of task-space velocity. The communication topology is assumed to be directed graphs containing a spanning tree. A decentralised task-space observer with kinematic parameter updating is proposed to avoid the reliance of task-space velocity. Based on the observer, we propose the distributed adaptive synchronisation controller for two cases: (1) the leaderless consensus case and (2) the leader-follower case, where all the followers track the convex hull spanned by the virtual leaders and for each follower, it is required that there exists at least one leader that has a directed path to the follower. The asymptotic synchronisation is proved with Lyapunov analysis and input–output stability analysis tools. Simulations with multiple robotic agents are performed to show the effectiveness of the proposed schemes.     

The design of suboptimal asymptotic stabilising controllers for nonlinear slowly varying systems

The design of asymptotic stabilising controllers for slowly varying nonlinear systems is considered in this paper. The designed control law is based on finding a slowly varying control Lyapunov function. Also, consideration of the Hamilton–Jacobi–Bellman equation showed that the proposed controller is a suboptimal controller and the response of the system may be very close to its optimal solution. The maximum admissible rate of changes of the system dynamic is also evaluated. This technique is first applied to a created example and then to a practical example (optimal autopilot design for an air vehicle). The air vehicle is modelled as a nonlinear slowly varying system and the efficiency of the designed autopilot in terms of transient responses, control signals and the values of cost function are shown by numerical simulations.     

An optimal stochastic multivariable PID controller: a direct output tracking approach

This paper deals with the design of an optimal stochastic controller possessing tracking capability of any reference output trajectory in the presence of measurement noise. We consider multi-input multi-output linear time-invariant systems and a proportional-integral-derivative (PID) controller. The system under consideration needs not be stable. A recursive algorithm providing optimal time-varying PID gains is proposed for the case where the number of inputs is larger than or equal to the number of outputs. The development of the proposed algorithm aims for per-time-sample minimisation of the mean-square output error in the presence of erroneous initial conditions, measurement noise, and process noise. Necessary and sufficient conditions are provided for the convergence of the output error covariance. In addition, convergence results are presented for discretised continuous-time plants. Simulation results are included to illustrate the performance capabilities of the proposed algorithm. Performance comparison with an optimal stochastic iterative learning control scheme, an optimal PID controller, an adaptive PID controller, and a recent optimal stochastic PID controller are also included.     

Bounded robust control of nonlinear systems using neural network�Cbased HJB solution

In this paper, a Hamilton–Jacobi–Bellman (HJB) equation–based optimal control algorithm for robust controller design is proposed for nonlinear systems. The HJB equation is formulated using a suitable nonquadratic term in the performance functional to tackle constraints on the control input. Utilizing the direct method of Lyapunov stability, the controller is shown to be optimal with respect to a cost functional, which includes penalty on the control effort and the maximum bound on system uncertainty. The bounded controller requires the knowledge of the upper bound of system uncertainty. In the proposed algorithm, neural network is used to approximate the solution of HJB equation using least squares method. Proposed algorithm has been applied on the nonlinear system with matched and unmatched type system uncertainties and uncertainties in the input matrix. Necessary theoretical and simulation results are presented to validate proposed algorithm.