模糊CMAC及其在机器人轨迹跟踪控制中的应用

小脑模型关节控制器(CMAC)具有结构简单,学习快速的优点,但是它的空间划分方式不能在线进行调整,影响了其自适应能力的提高.本文将模糊理论引入CMAC,提出了一种能够反映人类小脑认知的模糊性和连续性的模糊小脑模型关节控制器(FCMAC).该控制器对CMAC的空间划分方式进行了模糊化处理,可通过BP学习算法对CMAC的空间划分方式进行在线调整,大大提高了CMAC的自适应能力.所提出的FCMAC被应用于机器人的轨迹跟踪控制系统以克服机器人系统中非线性和不确定性因素的影响.仿真实验结果表明,所提FCMAC与传统的CMAC相比性能上有了很大的改善.     

Adaptive recurrent neural network control using a structure adaptation algorithm

This paper proposes an adaptive recurrent neural network control (ARNNC) system with structure adaptation algorithm for the uncertain nonlinear systems. The developed ARNNC system is composed of a neural controller and a robust controller. The neural controller which uses a self-structuring recurrent neural network (SRNN) is the principal controller, and the robust controller is designed to achieve L ² tracking performance with desired attenuation level. The SRNN approximator is used to online estimate an ideal tracking controller with the online structuring and parameter learning algorithms. The structure learning possesses the ability of both adding and pruning hidden neurons, and the parameter learning adjusts the interconnection weights of neural network to achieve favorable approximation performance. And, by the L ² control design technique, the worst effect of approximation error on the tracking error can be attenuated to be less or equal to a specified level. Finally, the proposed ARNNC system with structure adaptation algorithm is applied to control two nonlinear dynamic systems. Simulation results prove that the proposed ARNNC system with structure adaptation algorithm can achieve favorable tracking performance even unknown the control system dynamics function.     

Internal model based robust inversion feedforward and feedback 2DOF control for LPV system with disturbance

To reduce the adverse effects on the control performance and disturbance rejection caused by system uncertainty, a novel internal model based robust inversion feedforward and feedback 2DOF control approach was proposed for LPV system with disturbance. The proposed control approach combines the internal model control and robust inversion based 2DOF control, it utilizes internal model based control to reject external disturbance, utilizes robust inversion 2DOF control to enhance the control resolution and guarantee the system control performance. At first, a LMI synthesis approach for LPV system model identification and a disturbance compensator optimization design method which could minimize H_∞ norm of output error caused by disturbance are presented. Then, combined with internal loop for disturbance compensation, a robust inversion feedforward controller is designed by robust inversion approach and the feedback controller which could render the requirements of reference signal tracking performance and robustness satisfied is obtained by the H_∞ mixed sensitivity synthesis approach. Finally, atomic force microscopy (AFM) vertical positioning simulation experiments are conducted and the experiment results showed that the proposed control approach could achieve better output performance and disturbance rejection compared with conventional internal model based control and robust inversion based 2DOF control approach.     

Robust adaptive self-structuring fuzzy control design for nonaffine,nonlinear systems

In this article, a robust adaptive self-structuring fuzzy control (RASFC) scheme for the uncertain or ill-defined nonlinear, nonaffine systems is proposed. The RASFC scheme is composed of a robust adaptive controller and a self-structuring fuzzy controller. In the self-structuring fuzzy controller design, a novel self-structuring fuzzy system (SFS) is used to approximate the unknown plant nonlinearity, and the SFS can automatically grow and prune fuzzy rules to realise a compact fuzzy rule base. The robust adaptive controller is designed to achieve an L ₂ tracking performance to stabilise the closed-loop system. This L ₂ tracking performance can provide a clear expression of tracking error in terms of the sum of lumped uncertainty and external disturbance, which has not been shown in previous works. Finally, five examples are presented to show that the proposed RASFC scheme can achieve favourable tracking performance, yet heavy computational burden is relieved.     

基于自组织小波小脑模型关节控制器的不确定非线性系统鲁棒自适应终端滑模控制

针对一类不确定非线性系统的跟踪控制问题,在考虑建模误差、参数不确定和外部干扰情况下,以良好的跟踪性能及强鲁棒性为目标,提出基于自组织小脑模型(self-organizing wavelet cerebellar model articulation controller,SOWCMAC)的鲁棒自适应积分末端(terminal)滑模控制策略.首先,将小脑模型、自组织神经网络和小波函数各自优势相结合,给出一种SOWCMAC,以保证干扰估计方法具有快速学习能力和更好的泛化能力.其次,设计两种改进的terminal滑模面构造方法,并分别给出各自的收敛时间.然后,基于SOWCMAC和改进的积分terminal滑模面,给出不确定非线性系统鲁棒自适应非奇异terminal控制器的设计过程,其中通过构造自适应鲁棒项抑制干扰估计误差对系统跟踪性能的影响,并利用Lyapunov理论证明闭环系统的稳定性.最后,将该方法应用于近空间飞行器姿态的控制仿真实验,结果表明所提出方法有效性.     

一种基于小脑模型关节控制器评论-策略家的机器人跟踪控制算法

本文提出了一种基于小脑模型关节控制器(CMAC)的评论–策略家算法,设计不依赖模型的跟踪控制器,来解决机器人的跟踪问题.该跟踪控制器包含位置控制器和角度控制器,其输出分别为线速度和角速度.位置控制器由评价单元和策略单元组成,每个单元都采用CMAC算法,按改进δ学习规则在线调整权值.策略单元产生控制量;评判单元在线调整策略单元学习速率.以双轮驱动自主移动机器人为例,与固定学习速率CMAC做比较,仿真数据表明,基于CMAC的评论–策略家算法的跟踪控制器具有跟踪速度快,自适应能力强,配置参数范围宽,不依赖数学模型等特点.     

A globally stable saturated desired compensation adaptive robust control for linear motor systems with comparative experiments

The recently proposed saturated adaptive robust controller is integrated with desired trajectory compensation to achieve global stability with much improved tracking performance. The algorithm is tested on a linear motor drive system which has limited control effort and is subject to parametric uncertainties, unmodeled nonlinearities, and external disturbances. Global stability is achieved by employing back-stepping design with bounded (virtual) control input in each step. A guaranteed transient performance and final tracking accuracy is achieved by incorporating the well-developed adaptive robust controller with effective parameter identifier. Signal noise that affects the adaptation function is alleviated by replacing the noisy velocity signal with the cleaner position feedback. Furthermore, asymptotic output tracking can be achieved when only parametric uncertainties are present.     

一阶参数不确定滞后过程的鲁棒PID控制器设计

提出一种针对一阶参数不确定滞后过程的鲁棒PI／PID控制器优化设计方法。首先基于D-分割法技术,给出确定一阶参数不确定滞后过程的整个PI／PID控制器的可行鲁棒稳定域算法;在定义一个与控制器给定点跟踪性能、鲁棒性能和抗扰动性能相关的目标函数的基础上,给出PI／PID控制器设计的约束优化问题;最后应用一种启发式粒子群优化（PSO）算法对该约束问题进行求解。仿真结果表明,所提出的方法可得到更小的调节时间、更小的超调、较强鲁棒性和更好的抗扰动性能,表明了所提出的方法的有效性。     

Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.     

Robust flight control system design using H∞ loop-shaping and recessive trait crossover genetic algorithm

A proposed approach to robust controller design is introduced. This approach combines the recessive trait crossover genetic algorithm with the loop-shaping design procedure using H_∞ synthesis. The requirements, design and simulation of a flight control system for precision tracking task are considered. The proposed method is applied to design a control system for the F-16 fighter aircraft model. The flight simulations reveal that the desired performance objectives are achieved and that the controller provides acceptable performance in spite of modeling errors and plant parameter variations.     

Design of a Fixed‐Order RST Controller for Interval Systems: Application to the Control of Piezoelectric Actuators

This paper presents a technique for designing a robust polynomial RST controller for parametric uncertain systems. The uncertain parameters are assumed to be bounded by intervals. The computation of the controller is addressed by introducing the interval arithmetic. The controller synthesis is formulated as a set inversion problem that can be solved using the SIVIA algorithm. The proposed method is afterwards applied to design a robust controller for a piezoelectric microactuator. The experimental results show the efficiency of the proposed method. Finally, a fine stability analysis is performed to analytically prove the robustness of the designed controller.     

A new approach to robust and non-fragile H∞ control for uncertain fuzzy systems

This paper investigates the robust H_∞ control and non-fragile control problems for Takagi-Sugeno (T-S) fuzzy systems with linear fractional parametric uncertainties. The robust H_∞ control problem is to design a state feedback controller such that the robust stability and a prescribed H_∞ performance of the resulting closed-loop system is ensured. And the non-fragile H_∞ control problem is to design a state feedback controller with parameter uncertainties. Based on the linear matrix inequality (LMI) approach, new sufficient conditions for the solvability of the two problems are obtained. It is shown that the desired state feedback fuzzy controller can be constructed by solving a set of LMIs. Numerical examples are also provided to demonstrate the effectiveness of the proposed design method.     

间隙度量与跟踪系统中的鲁棒控制器设计

为定量研究鲁棒控制器允许对象有尽可能大的不确定性.在引入间隙度量的基础上,定义了跟踪系统鲁棒控制器的鲁棒边界,并对某跟踪系统设计了基于间隙度量的鲁棒控制器.该控制器能兼顾对象的不确定性与控制器的不确定性.由仿真结果可以看出,与普通的PID控制器相比,具有较大鲁棒边界的鲁棒控制器不仅具有较强的干扰抑制能力.而且能够在模型加性不确定性存在的情况下具有很好的跟踪性能.     

未知离散非线性系统的鲁棒自适应NN控制

利用WNN(小波神经网络)逼近未知函数，将未知离散非线性系统转化为一类参数化严格反馈系统，进而对变换后的系统给出一个避免过参数化的自适应反推控制器，并证明该控制器可保证在存在参数不确定性和函数不确定性的条件下，整个自适应系统的状态全局有界，同时也可保证系统的跟踪误差落在一个大小与不确定性成比例的紧集中，仿真结果表明该控制器具有较强的鲁棒性，可适用于不同的对象。     

Robust discrete-time sliding mode control for tracking and model following of uncertain nonlinear systems

This paper proposes a discrete-time controller for robust tracking and model following of a class of nonlinear, multi-input multi-output, systems. For this purpose, a discrete-time sliding mode controller (DTSMC) is used to ensure the stability, robustness and an output tracking against the modelling uncertainties, even at relatively large sampling periods. In this way, Takagi–Sugeno (T–S) fuzzy modelling is used to decompose the nonlinear system to a set fuzzy-blended locally linearised subsystems. Implementation of the second Lyapunov theory for mismatched uncertain nonlinear T–S fuzzy models results in a set of linear matrix inequalities, which is used to design the sliding surface. A new method is then proposed to reach the quasi-sliding mode and stay thereafter. Simulation studies show that the proposed method guarantees the stability of closed-loop system and achieves small tracking error in the presence of parametric uncertainties at large sampling periods.     

Intelligent complementary sliding-mode control with dead-zone parameter modification

This paper proposes an intelligent complementary sliding-mode control (ICSMC) system which is composed of a computed controller and a robust controller. The computed controller includes a neural dynamics estimator and the robust compensator is designed to prove a finite L₂-gain property. The neural dynamics estimator uses a recurrent neural fuzzy inference network (RNFIN) to approximate the unknown system term in the sense of the Lyapunov function. In traditional neural network learning process, an over-trained neural network would force the parameters to drift and the system may become unstable eventually. To resolve this problem, a dead-zone parameter modification is proposed for the parameter tuning process to stop when tracking performance index is smaller than performance threshold. To investigate the capabilities of the proposed ICSMC approach, the ICSMC system is applied to a one-link robotic manipulator and a DC motor driver. The simulation and experimental results show that favorable control performance can be achieved in the sense of the L₂-gain robust control approach by the proposed ICSMC scheme.     

Nonlinear asymptotic attitude tracking control of an underactuated 3-degree-of-freedom helicopter using neural network feedforward term

This paper proposes a new asymptotic attitude tracking controller for an underactuated 3-degree-of-freedom (DOF) laboratory helicopter system by using a nonlinear robust feedback and a neural network (NN) feedforward term. The nonlinear robust control law is developed through a modified inner-outer loop approach. The application of the NN-based feedforward is to compensate for the system uncertainties. The proposed control design strategy requires very limited knowledge of the system dynamic model, and achieves good robustness with respect to system parametric uncertainties. A Lyapunov-based stability analysis shows that the proposed algorithms can ensure asymptotic tracking of the helicopter’s elevation and travel motion, while keeping the stability of the closed-loop system. Real-time experiment results demonstrate that the controller has achieved good tracking performance.     

Studies on improving vehicle handling and lane keeping performance of closed-loop driver–vehicle system with integrated chassis control

This study proposes a new integrated robust model matching chassis controller to improve vehicle handling performance and lane keep ability. The design framework of the H_∞ controller is based on linear matrix inequalities (LMIs), which integrates active rear wheel steering control, longitudinal force compensation and active yaw moment control. To comprehensively evaluate the performance of the integrated chassis control system, a closed-loop driver–vehicle system is used. The effectiveness of the integrated controller on handling performance improvement is tested by a vehicle without driver model under a crosswind disturbance. At the same time, both the handling and lane keeping improving performance of the closed-loop driver–vehicle system is evaluated by tracking an S shape winding road. The simulation results reveal that the integrated chassis controller not only achieves preferable handling performance and stability, but also improves the vehicle lane keep ability significantly, and can alleviate the working load of the driver.     

基于QFT和ZPETC的高精度鲁棒跟踪控制器设计

阐述了定量反馈理论(QFT)和零相差跟踪控制器(ZOETC)的基本原理及设计方法,并给出了设计实例。在QFT和ZPETC的基础上,提出了一种是实现高精度鲁棒跟踪控制的方案,采用QFT控制保证系统的鲁棒性,通过ZPETC提高系统的跟踪精度。仿真表明,这种方法实现了QFT和ZPETC的完美结合,很适合高精度跟踪系统的鲁棒控制。     

Intelligent mixed H2/H∞ adaptive tracking control system design using self-organizing recurrent fuzzy-wavelet-neural-network for uncertain two-axis motion control system

In this paper, an intelligent adaptive tracking control system (IATCS) based on the mixed H₂/H_∞ approach under uncertain plant parameters and external disturbances for achieving high precision performance of a two-axis motion control system is proposed. The two-axis motion control system is an X–Y table driven by two permanent-magnet linear synchronous motors (PMLSMs) servo drives. The proposed control scheme incorporates a mixed H₂/H_∞ controller, a self-organizing recurrent fuzzy-wavelet-neural-network controller (SORFWNNC) and a robust controller. The combinations of these control methods would insure the stability, robustness, optimality, overcome the uncertainties, and performance properties of the two-axis motion control system. The SORFWNNC is used as the main tracking controller to adaptively estimate an unknown nonlinear dynamic function that includes the lumped parameter uncertainties, external disturbances, cross-coupled interference and frictional force. Moreover, the structure and the parameter learning phases of the SORFWNNC are performed concurrently and online. Furthermore, a robust controller is designed to deal with the uncertainties, including the approximation error, optimal parameter vectors and higher order terms in Taylor series. Besides, the mixed H₂/H_∞ controller is designed such that the quadratic cost function is minimized and the worst case effect of the unknown nonlinear dynamic function on the tracking error must be attenuated below a desired attenuation level. The mixed H₂/H_∞ control design has the advantage of both H₂ optimal control performance and H_∞ robust control performance. The sufficient conditions are developed for the adaptive mixed H₂/H_∞ tracking problem in terms of a pair of coupled algebraic equations instead of coupled nonlinear differential equations. The coupled algebraic equations can be solved analytically. The online adaptive control laws are derived based on Lyapunov theorem and the mixed H₂/H_∞ tracking performance so that the stability of the proposed IATCS can be guaranteed. Furthermore, the control algorithms are implemented in a DSP-based control computer. From the experimental results, the motions at X-axis and Y-axis are controlled separately, and the dynamic behaviors of the proposed IATCS can achieve favorable tracking performance and are robust to parameter uncertainties.