基于PID参数自整定的双容系统抗扰控制

针对双容液面调节系统的非线性、参数时变的特点,基于现代控制理论设计了带扰动观测器的模糊自整定控制器.把这种控制器应用在双容液面调节系统中,既实现了PID控制器参数的在线自整定,又增强了系统的抗扰能力.仿真试验表明,这种方案极大地提高了被控对象的静态和动态性能,对参数时变的适应能力强,鲁棒性好.该方案同样可以用于其他非线性、时变系统.     

Finite-time extended dissipative control for fuzzy systems with nonlinear perturbations via sampled-data and quantized controller

This paper investigates the problem of finite-time extended dissipative control for T–S fuzzy time-varying delay systems with nonlinear perturbations via sampled-data and quantized controller. The definition of finite-time bounded mixed extended dissipative of fuzzy systems is first proposed. Based on the constructed Lyapunov–Krasovskii functional(LKF) and Peng–Parks integral inequality, some sufficient conditions are obtained in the form of linear matrix inequalities(LMIs), which are less conservative than other papers. By combining the input delay approach and dynamic quantizer, the sampled-data and quantized controller is designed to guarantee that the T–S fuzzy system is finite-time bounded mixed extended dissipative. Finally, some numerical examples and practical examples are presented to verify the feasibility and effectiveness of the proposed methods.     

Indirect predictive type-2 fuzzy neural network controller for a class of nonlinear input - delay systems

In this paper a new indirect type-2 fuzzy neural network predictive (T2FNNP) controller has been proposed for a class of nonlinear systems with input-delay in presence of unknown disturbance and uncertainties. In this method, the predictor has been utilized to estimate the future state variables of the controlled system to compensate for the time-varying delay. The T2FNN is used to estimate some unknown nonlinear functions to construct the controller. By introducing a new adaptive compensator for the predictor and controller, the effects of the external disturbance, estimation errors of the unknown nonlinear functions, and future sate estimation errors have been eliminated. In the proposed method, using an appropriate Lyapunov function, the stability analysis as well as the adaptation laws is carried out for the T2FNN parameters in a way that all the signals in the closed-loop system remain bounded and the tracking error converges to zero asymptotically. Moreover, compared to the related existence predictive controllers, as the number of T2FNN estimators are reduced, the computation time in the online applications decreases. In the proposed method, T2FNN is used due to its ability to effectively model uncertainties, which may exist in the rules and data measured by the sensors. The proposed T2FNNP controller is applied to a nonlinear inverted pendulum and single link robot manipulator systems with input time-varying delay and compared with a type-1 fuzzy sliding predictive (T1FSP) controller. Simulation results indicate the efficiency of the proposed T2FNNP controller.     

Compensatory fuzzy neural network control with dynamic parameters estimation for linear voice coil actuator

The object of this study is to develop an intelligent control strategy, which comprises a compensatory fuzzy neural network (CFNN) controller with a dynamic particle swarm optimization (DPSO) based estimator, for on-line parameter estimation and control of a linear voice coil actuator (VCA). Because the plant Jacobian of the VCA is nonlinear and time-varying, it is difficult to derive the learning algorithm for the CFNN by using the conventional back-propagation (BP) method directly. Therefore, it is strongly desirable that an on-line manner can provide a reasonably good estimation of the plant Jacobian in the practical applications. In this study, the operating principle and dynamic analysis of the VCA are introduced first. Subsequently, the algorithms of the DPSO and CFNN are given where the DPSO and CFNN are utilized to obtain the control signal and estimate the plant Jacobian, respectively. Moreover, a convergence analyses is given to derive specific learning rates for ensuring the convergence of the control error. Finally, the proposed control strategy is implemented on a 32-bit floating-point digital signal processor (DSP) for experimental verification. Experimental results demonstrate the improved tracking performance and robustness of the proposed CFNN-DPSO controller with online Jacobian estimation compared with the conventional CFNN controller with constant one, for the VCA control system.     

Adaptive fuzzy sliding-mode control for the Ti6Al4V laser alloying process

It is well known that surface alloying quality may vary significantly with respect to process parameter variation. Thus a feedback control system is required to monitor the operating parameters for yielding a good quality control. Since this multi-input and multi-output (MIMO) system has nonlinear coupling and time-varying dynamic characteristics, it is very difficult to establish an accurate process model for designing a model-based controller. Hence an adaptive fuzzy sliding-mode controller (AFSMC) which combines an adaptive rule with fuzzy and sliding-mode control is employed in this study. It has an on-line learning ability for responding to a system’s nonlinear and time-varying behaviours. Two adaptive fuzzy sliding-mode controllers are designed for tuning the laser power and the traverse velocity simultaneously to tackle the absorptivitiy and geometrical variations of the work pieces. The simulation results show that good surface lapping performance is achieved by using this intelligent control strategy.     

Neural network controller for robotic motion control

Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations.     

Robust preview control for a class of uncertain discrete-time systems with time-varying delay

This paper proposes a concept of robust preview tracking control for uncertain discrete-time systems with time-varying delay. Firstly, a model transformation is employed for an uncertain discrete system with time-varying delay. Then, the auxiliary variables related to the system state and input are introduced to derive an augmented error system that includes future information on the reference signal. This leads to the tracking problem being transformed into a regulator problem. Finally, for the augmented error system, a sufficient condition of asymptotic stability is derived and the preview controller design method is proposed based on the scaled small gain theorem and linear matrix inequality (LMI) technique. The method proposed in this paper not only solves the difficulty problem of applying the difference operator to the time-varying matrices but also simplifies the structure of the augmented error system. The numerical simulation example also illustrates the effectiveness of the results presented in the paper.     

H∞ output-feedback fuzzy proportional-integral control of fully delayed input/output systems

This paper presents the output-feedback fuzzy proportional-integral (PI) controller design for uncertain nonlinear systems with both fully delayed input and output. Based on the Takagi–Sugeno (T–S) fuzzy model representation, the output-feedback PI control is realized via parallel distributed PI compensation and novel LMI gain design. Although the T–S fuzzy PI controller is simple, asymptotic output regulation is assured to overcome the effect of uncertainty, state delay, and full input/output delays. When considering disturbance and measurement noise, the control performance is achieved by robust gain design. Furthermore, state observers and bilinear matrix inequality conditions are removed in this paper. Finally, time-delay Chua׳s circuit system and a continuous-time stirred tank reactor are taken as applications to show the expected performance.     

Model-free adaptive fractional order control of stable linear time-varying systems

This paper presents a new model-free adaptive fractional order control approach for linear time-varying systems. An online algorithm is proposed to determine some frequency characteristics using a selective filtering and to design a fractional PID controller based on the numerical optimization of the frequency-domain criterion. When the system parameters are time-varying, the controller is updated to keep the same desired performances. The main advantage of the proposed approach is that the controller design depends only on the measured input and output signals of the process. The effectiveness of the proposed method is assessed through a numerical example.     

A new discrete-time robust adaptive time-delay control for a class of uncertain nonlinear strict-feedback systems using sliding mode

This paper presents a new discrete-time adaptive second-order sliding mode control with time delay estimation (TDE) for a class of uncertain nonlinear time-varying strict-feedback systems. The existing researches on time delay control (TDC) are conventionally established based on a stability criterion that is subject to the infinitesimal time delay assumption. Recently, this criterion was rejected and a new criterion was proposed for the development of a controller for systems with fully known dynamics. In this study, this approach is extended to uncertain systems. Specifically, a new criterion is developed for the stability of the TDE-error within an adaptive robust controller design without the infinitesimal time delay assumption. With the proposed adaptive robust control, there is no need for determination of uncertainties upper-bounds. Simulation results illustrate the efficacy of the proposed controller.     

Adaptive neural network controller for the molten steel level control of strip casting processes

The twin-roll strip casting process is a steel-strip production method which combines continuous casting and hot rolling processes. The production line from molten liquid steel to the final steel-strip is shortened and the production cost is reduced significantly as compared to conventional continuous casting. The quality of strip casting process depends on many process parameters, such as molten steel level in the pool, solidification position, and roll gap. Their relationships are complex and the strip casting process has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive neural network controller is developed to overcome this problem. The proposed control strategy is based on a neural network structure combined with a sliding-mode control scheme. An adaptive rule is employed to on-line adjust the weights of radial basis functions by using the reaching condition of a specified sliding surface. This surface has the on-line learning ability to respond to the system’s nonlinear and time-varying behaviors. Since this model-free controller has a simple control structure and small number of control parameters, it is easy to implement. Simulation results, based on a semiexperimental system dynamic model and parameters, are executed to show the control performance of the proposed intelligent controller. In addition, the control performance is compared with that of a traditional PID controller.     

Grasping force controller for parallel grip with fuzzy rules emulated networks

A grasping force regulation for industrial parallel grips is developed without any requirement of mathematic model regarding to the contact mechanism and system dynamic. The physical system including the grasping dynamic and contact mechanism is considered as a class of unknown nonlinear discrete-time systems. An adaptive network called multi-input fuzzy rules emulated network (MiFREN) is implemented as the controller. This control scheme is performed by if-then rules which can be directly defined by human knowledge regarding to the gripper’s specification and objects. The learning algorithm based on gradient search is developed to tune all adjustable parameters inside MiFREN. The system performance and stability can be guaranteed by the time-varying learning rate. An industrial parallel grip SCHUNK-WSG 50 with the proposed controller demonstrates the performance via the experimental setup. Furthermore, the performance can be spontaneously improved within the next iteration of the learning process.     

基于PAC的模糊PID液冷系统的设计与实现

针对温度系统的非线性、时变、时滞等特性,以及特定项目的循环冷却特点,设计了一种基于PAC的模糊PID控制系统。该设计方法结合了模糊控制的鲁棒性强与PID的调节性能好等优点,利用PAC（可编程自动化控制器）的优秀计算能力和控制性能,解决了系统对非线性、时变、强耦合的循环液体冷却系统的调节问题,对于此类系统提出了一种有效的设计实现方法。系统设计重点是模糊PID控制器的设计与控制规则表的建立。实际应用表明,系统可靠性高,控制效果好,具有很好的实用性。     

液压弯辊系统的优化神经网络内模控制

针对轧机液压弯辊系统存在非线性、时变性等特点,采用基于前馈神经网络的内模控制方法,将优化网络用于神经网络辨识器和内模控制器的离线训练,采用学习率自适应调整的改进BP算法在线调整网络权值。仿真研究表明,将优化网络用于液压弯辊系统控制,提高了液压弯辊系统的动态响应速度和稳态跟踪精度,具有较强的快速性和鲁棒性,能够取得理想的控制效果。     

联合收割机双闭环负荷控制系统的设计

联合收割机是一个高阶时变非线性系统,存在大滞后,工况复杂,简要分析联合收割机数学模型及作业期间可能存在的各种内部、外部扰动。针对收割机负荷控制系统中利用一个控制器综合多个扰动,各参数相互影响,参数调整困难,系统动态性能不够好的问题,提出一种双闭环负荷控制系统,将收割机系统分为行走、作业两部分,分别利用车速闭环和滚筒转速闭环进行控制。车速闭环采用单神经元比例积分微分(Proportion,integration,differentiation,PID)算法,根据车辆行走系统的工况变化不断调整控制参数,尽可能跟踪给定车速。滚筒转速闭环采用直接广义预测控制算法,也可自动适应系统参数和工况的变化,保证滚筒恒定在最佳转速。整个控制算法实时计算量较小,可保证系统具有足够高的实时性。仿真和试验证明双闭环负荷控制系统能获得较高的控制精度和优良的动态特性。     

一种基于Lyapunov约束的学习控制方法及应用

针对非线性系统的稳定控制器直接设计问题,提出一种基于Lyapunov稳定性条件的学习控制器设计方法框架,将传统的控制器设计与稳定性证明分析问题转化为以控制器为求解项,Lyapunov稳定条件为约束的最优化问题,提供了一种直接求解全局稳定的最优控制器的新途径。首先建立了以系统跟踪误为目标函数与以Lyapunov稳定条件为约束的最优化问题,接着给出了一类基于神经网络实现的PID结合前馈控制器设计形式,最后分析并设计了学习控制器求解方法,采用相关深度学习技术以提升求解能力。二阶线性与非线性系统仿真测试与一级旋转倒立摆模拟实验表明所提方法具有快速收敛、低误差、控制输出平滑且低幅值等特点;在加入扰动、噪声、参数不确定性和不同的测试期望输出条件下的反步法对比测试结果表明所提方法对扰动与噪声具有强抑制能力,同时学习控制器具有高泛化能力。基于V-Rep的一级旋转倒立摆模拟与四旋翼单轴控制实物实验结果验证了所提方法对物理系统控制问题具有高控制精度与强抗扰能力。     

Fuzzy time-optimal controller (FTOC) for second order nonlinear systems

The motivation behind this paper is to seek alternative techniques to achieve a near optimal controller for non-linear systems without solving the analytical problem. In classical optimal control systems, the system states and optimization co-state parameters generate a two-point boundary value problem (TPBVP) using Pontryagin’s minimum principle (PMP). The paper contributes a new fuzzy time-optimal controller to the existing fuzzy controllers which has two regular inputs and one bang-bang output. The proposed controller closely approximates the output of the classical time-optimal controller. Further, input membership function are tuned on-line to improve the time-optimal output. The new controller exhibits optimal behaviour for second order non-linear systems. The rules are selected to satisfy the stability and optimality conditions of the new fuzzy time-optimal controller. The paper describes a systematic procedure to design the controller and how to achieve the desired result. To benchmark the new controller performance, a sliding mode controller is used for guidance and comparison purpose. Simulation of three non-linear examples shows promising results. The work described here is expected to incite researcher’s interest in fuzzy time-optimal controller design.     

Output feedback controller design for input time-delayed nonlinear system

The linearization of an input/output controller has been designed for an input time delay nonlinear time discretized nonlinear system. The time discretized nonlinear model has been obtained based on Taylor-Lie series expansion method and zero order hold assumption. The resulting control algorithm enables the time delay nonlinear system control, while the continuous time controller cannot handle a time delay nonlinear system due to its infinite dimensionality. The performance of the proposed controller is evaluated by using two different case studies: a Van Der Pol equation and a Continuous Stirred Tank Reactor (CSTR) system that all exhibit nonlinear behavior and input time delay. For all the case studies, the results validate the proposed methods.     

阀控非对称伺服缸基于粒子群优化的模糊PID控制器研究

针对阀控非对称伺服缸非线性、参数时变的特点,考虑到油液压缩特性的影响,建立了包含变体积弹性模量的系统数学模型。提出一种基于粒子群算法优化(PSO)的模糊自适应PID控制方法(简称PSO_FPID)。模糊逻辑推理在线调整PID控制器的比例、积分和微分系数,以粒子群算法实现对模糊控制比例因子和量化因子的参数寻优,两种方法的组合保证了系统最佳参数匹配下的自适应控制。同时,利用AMESim与Simulink联合仿真研究不同含气量的阀控缸模型在传统PID与PSO_FPID两种控制方法下的动态响应特性。结果表明:PSO_FPID综合了PID控制器高精度的优点和模糊控制器快速、适应性强的特点,能够有效抑制油液动态压缩特性的非线性影响,使系统具有良好的动、稳态特性。     

Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor

A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results.