An Intelligent Design for a PID Controller for Nonlinear Systems

The proportional integral derivative (PID) controller is the most frequently used controller design for industrial applications because of its favorable response and simplicity of adjustment. However, PID manual tuning is traditionally based on engineering experience, and adjusting nonlinear or unknown systems is extremely difficult. In promoting an intelligent controller design theory that can be applied to the control of various systems, this paper proposes a nonlinear control design method that involves determining the optimal solution and obtaining the transfer function of an unknown system by using sequential quadratic programming. In addition, this paper presents a case study of an induction motor V/F speed control to demonstrate the effectiveness of the proposed method based on MATLAB simulation. The results prove that the design of the proposed intelligent PID controller is more robust than traditional controller designs.     

Block sparse design of distributed controllers for dynamical network systems

This study proposes a controller design method based on block sparse optimization for dynamical network systems. The objective of the controller is to stabilize dynamical network systems with a given convergence rate. The block sparse optimization minimizes the number of controlled nodes. This study is unique in that the structure of the controller is constrained by the network topology of the system. Additionally, the proposed design problem is separable in terms of the distributed optimization over networks. The proposed method is applicable to controller design for the pinning control of consensus systems and the optimal vaccine allocation for epidemic spreading processes.     

多变量时滞非方系统的分数阶Smith预估控制

针对多变量时滞非方系统,提出一种基于反向解耦的分数阶Smith预估控制方法.首先,将反向解耦方法推广应用于$m\times n$非方系统中,给出非方解耦矩阵的设计方法,同时为了保证解耦矩阵的稳定正则,给出其实现的必要条件以及条件不满足时的补偿方法;然后,针对解耦后的各个单回路系统设计分数阶Smith预估控制器,根据内模控制与Smith预估控制结构上的等价关系简化控制器的设计,克服时滞环节对系统性能的影响,并且基于最大灵敏度推导出一种控制器参数解析整定方法;最后,通过典型的Shell标准控制问题对所提出方法进行验证.仿真结果表明,反向解耦方法设计简单易于实现,能达到系统完全解耦,控制器参数较少,整定方便,并且具有良好的跟踪能力、抗干扰性和鲁棒性.     

一类高阶非线性不确定系统自适应稳定控制

研究了一类高阶非线性不确定性系统的自适应稳定控制设计问题.因该系统的非线性程度高,其控制系数不等同、符号已知、但数值未知,故在此之前其稳定控制设计问题没有得到解决.本文应用自适应技术,结合设计参数的适当选取,从而得到了设计该类非线性系统状态反馈稳定控制器的新方法,并基于反推技术,给出了稳定控制器的设计步骤.所设计的状态反馈控制器使得闭环系统的状态全局渐近收敛于零,其余闭环信号一致有界.最后通过一个仿真例子说明了控制设计方法的有效性.     

Hybrid state-space fuzzy model-based controller with dual-ratesampling for digital control of chaotic systems

We develop a hybrid state-space fuzzy model-based controller with dual-rate sampling for digital control of chaotic systems. A Takagi-Sugeno (TS) fuzzy model is used to model the chaotic dynamic system and the extended parallel-distributed compensation technique is proposed and formulated for designing the fuzzy model-based controller under stability conditions. The optimal regional-pole assignment technique is also adopted in the design of the local feedback controllers for the multiple TS linear state-space models. The proposed design procedure is as follows: an equivalent fast-rate discrete-time state-space model of the continuous-time system is first constructed by using fuzzy inference systems. To obtain the continuous-time optimal state-feedback gains, the constructed discrete-time fuzzy system is then converted into a continuous-time system. The developed optimal continuous-time control law is finally converted into an equivalent slow-rate digital control law using the proposed intelligent digital redesign method. The main contribution of the paper is the development of a systematic and effective framework for fuzzy model-based controller design with dual-rate sampling for digital control of complex such as chaotic systems. The effectiveness and the feasibility of the proposed controller design method is demonstrated through numerical simulations on the chaotic Chua circuit     

基于参数辨识和T-S模糊模型的一类非线性系统镇定控制器设计

针对一类非线性不确定控制系统,首先采用参数辨识的方法构造出对应的Takagi-Sugeno（T-S）模糊模型;然后运用平行分布补偿（PDC）控制器设计方法进行系统的稳定控制器设计;最终达到镇定原非线性系统的目的.给出一种从T-S模糊模型参数辨识到PDC控制器设计的非线性控制器的设计方法.针对单级倒立摆系统的仿真结果验证了所提出方法的有效性.     

网络控制系统的鲁棒L1跟踪控制

将L1性能约束引入网络控制系统中,研究具有时延和不确定性的网络控制系统的模型参考输出跟踪问题。基于线性矩阵不等式方法推出了该网络控制系统的稳定性和控制器设计的充分条件,并将控制器的设计转化为一个凸优化的求解问题。所设计的控制器能够保证相对于所有峰值有界的外界扰动信号,网络控制系统具有L1性能约束水平。仿真实例证实了该设计方法的有效性。     

混沌系统的模糊-混沌混合控制

提出一种新的混沌系统控制方法,控制策略分为两个阶段：第一阶段利用混沌系统固有性质开环控制,系统状态到达预定区域后,进入第二阶段,切换至状态反馈模糊控制器。采用线性矩阵不等式方法设计了模糊控制器反馈增益。最后,通过一个Lorenz混沌系统设计实例验证了这种方法的有效性。     

Output control design and separation principle for a class of port‐Hamiltonian systems

Using a structure preserving observer, a dynamic output controller is proposed for a class of port‐Hamiltonian systems. The core of this method is based on the notion of contractive port‐Hamiltonian systems. The proposed method utilizes an extended form of IDA‐PBC (interconnection and damping assignment passivity‐based control), a well‐known controller design method for port‐Hamiltonian systems and paves the way for using IDA‐PBC in output control design of challenging control objectives, such as output tracking for underactuated mechanical systems. In the line of output control design, a useful separation principle for a class of port‐Hamiltonian systems is achieved, which is valuable in the field of nonlinear systems. Some simulations on magnetic levitation and ball on wheel testbeds show the potency and applicability of the proposed method.     

非线性大系统的分散自适应模糊控制*

本文针对非线性大系统，利用模糊系统的逼近能力，提出了一种分散自适应模糊控制器设计的系统方法。控制结构中采用分散模糊系统去自适应补偿过程不确定性，同时用模糊控制器的输出代替常规变结构控制律中的符号函数。利用李亚普诺夫理论，证明了控制算法是全局稳定的，跟踪误差可收敛到零的一个领域内。     

随机网络中的H∞输出跟踪控制研究

研究随机网络控制系统的输出跟踪问题。采用线性矩阵不等式方法,推导控制器存在的充分条件,介绍控制器的设计方法。该控制器能够保证相对于所有能量有界的外界扰动信号,随机网络控制系统的H∞性能指标小于一个定值γ。仿真实验结果表明,利用该方法设计的控制器输出误差较小。     

A methodology for designing controllers for industrial systems based on nonlinear separation model and control

A feasible method of controller design for industrial nonlinear dynamic systems is proposed, by separating the nonlinear dynamic system into nonlinear static parts and a linear dynamic part. The proposed method of industrial controller design reduces the existing gaps between the control theory and the actual field. In the controller construction procedure of a system, the nonlinearities are eliminated by using the inversion functions of the nonlinear static parts. Any conventional control theory is ideally applicable to a linear dynamic part of the system. Based on the proposed method of nonlinear separation, controllers were constructed for three different systems: a chemical reactor, a temperature level-controlled tanked water system, and the contour-following control of an articulated robot arm. Some encouraging control performances, superior to those of other existing controllers, showed its significant potential for application to industrial systems with nonlinearities.     

Adaptive backstepping sliding mode control with tuning functions for nonlinear uncertain systems

An adaptive backstepping tuning functions sliding mode controller is proposed for a class of strict-feedback nonlinear uncertain systems. In this control design, adaptive backstepping is used to deal with unknown or uncertain parameters and the matching condition restricting the Lyapunov based design. The main drawback of the Lyapunov based adaptive backstepping which is the overparametrisation is eliminated by the tuning functions. The adaptive backstepping tuning functions design is combined with the sliding mode control in order to overcome quickly varying parametric and unstructured uncertainties, and to obtain chattering free control. The proposed controller not only provides robustness property against uncertainty but also copes with the overparametrisation problem. Experimental results of the proposed controller are compared with those of the standard sliding mode controller. The proposed controller exhibits satisfactory transient performance, good estimates of the uncertain parameters, and less chattering.     

Multiobjective optimization–based fault‐tolerant flight control system design

The loss of measurements used for controller scheduling or envelope protection in modern flight control systems due to sensor failures leads to a challenging fault‐tolerant control law design problem. In this article, an approach to design such a robust fault‐tolerant control system, including full envelope protections using multiobjective optimization techniques, is proposed. The generic controller design and controller verification problems are derived and solved using novel multiobjective hybrid genetic optimization algorithms. These algorithms combine the multiobjective genetic search strategy with local, single‐objective optimization to improve convergence speed. The proposed strategies are applied to the design of a fault‐tolerant flight control system for a modern civil aircraft. The results of an industrial controller verification and validation campaign using an industrial benchmark simulator are reported.     

Design and analysis of optimal controller for fuzzy systems with input constraint

In this paper, we present a design method of the optimal and robust controller subject to the constraint on control inputs for continuous-time Takagi-Sugeno (TS) fuzzy systems. In order to establish this design method, we consider an optimal and robust control problem for nonlinear dynamic systems. For this problem, we present an analytic way which can provide the optimal controller for nonlinear dynamic systems by the dynamic programming approach and the inverse optimal approach. Moreover, we analyze the robustness property of the proposed optimal controller with respect to a class of input uncertainties by the passivity approach. Then, based on the theoretical results presented in this paper, we formulate the design problem of the optimal and robust controller with input constraint for continuous-time TS fuzzy systems as the semidefinite programming problem, and find the controller by solving it. The usefulness of the proposed approach is illustrated by considering the three-axis attitude stabilization problem of rigid spacecraft.     

Stabilisation of discrete-time polynomial fuzzy systems via a polynomial lyapunov approach

This paper deals with the problem of designing a controller for a class of discrete-time nonlinear systems which is represented by discrete-time polynomial fuzzy model. Most of the existing control design methods for discrete-time fuzzy polynomial systems cannot guarantee their Lyapunov function to be a radially unbounded polynomial function, hence the global stability cannot be assured. The proposed control design in this paper guarantees a radially unbounded polynomial Lyapunov functions which ensures global stability. In the proposed design, state feedback structure is considered and non-convexity problem is solved by incorporating an integrator into the controller. Sufficient conditions of stability are derived in terms of polynomial matrix inequalities which are solved via SOSTOOLS in MATLAB. A numerical example is presented to illustrate the effectiveness of the proposed controller.     

具有通信序列的网络控制系统LQG控制器设计

研究了具有通信序列的网络控制系统控制器设计问题.通过在系统的输入输出端设计通信序列,分时占用网络,以降低带宽约束对系统的影响,进而构建了具有通信序列的网络控制系统模型;在此基础上,将网络控制器设计分为2个部分,首先,基于输出值设计Kalman滤波器,用于提供最优状态估计值;然后,在考虑通信序列的基础上利用LQG方法协同...     

控制方向未知的高次非线性系统的鲁棒自适应控制

研究了一类具有不可控不稳定线性化的非线性系统的自适应控制问题.该类系统的控制方向未知且含有不确定时变非线性参数.应用Nussbaum-type增益技术和adding a power integrator递推设计方法,设计了一种鲁棒自适应状态反馈控制器.所设计的控制器能够保证闭环系统的所有信号全局一致有界,且系统的状态渐近趋于零.除了假设未知参数及不确定性有界外,所设计的控制策略不需要控制系数的任何先验知识.仿真例子验证了算法的有效性.     

基于神经网络的一类非线性系统自适应H∞控制

基于神经网络提出一种自适应Ｈ∞控制方法。控制器由等效控制器和Ｈ∞控制器两部分组成,用神经网络逼近未知非线性函数,Ｈ∞控制器用于减弱外部及神经网络逼近误差对跟踪误差的影响。所设计的控制器不仅保证了闭环控制系统的稳定性,而且使外部干扰及神经网络逼近误差对跟踪误差的影响减小到预定的性能指标。     

奇异摄动系统的H∞控制: 基于奇异系统的方法

实际系统的奇异系统模型往往通过忽略系统的奇异摄动系统模型微分项系数矩阵中的小时间参数得到.然而, 传统的奇异系统控制器设计很少考虑微分项系数矩阵的摄动. 本文拓展了现有奇异系统输出动态反馈H_∞控制器的设计, 使得当实际闭环系统中存在小时间参数时, 仍然能保持稳定并且满足一定的H_∞性能指标. 仿真算例说明了本文提出方法的有效性.