Identification of nonlinear Wiener-Hammerstein systems by a novel adaptive algorithm based on cost function framework

This paper investigates parameter identification of nonlinear Wiener-Hammerstein systems by using filter gain and novel cost function. Taking into account the system information is corrupted by noise, the filter gain is exploited to extract the system data. By using several auxiliary filtered variables, an extended estimation error vector is developed. Then, based on the discount term of the extended estimation error and the penalty term on the initial estimate, a novel cost function is developed to obtain the optimal parameter adaptive law. Compared with the conventional cost function which is composed of the square sum of output error, the proposed algorithm based on the cost function of this paper can provide faster convergence rate and higher estimation accuracy. Furthermore, the convergence analysis of the proposed scheme indicates that the parameter estimation error can converge to zero. The effectiveness and practicality of the proposed scheme are validated through the simulation example and experiment on the turntable servo system.     

Repetitive control approach towards automatic tuning of Smith predictor controllers

This paper proposes a new method for automatic tuning of the Smith predictor controller based on a Repetitive Control (RC) approach. The method requires the input of a periodic reference signal which can be derived from a relay feedback experiment. A modified repetitive control scheme repetitively changes the control signal to achieve tracking error convergence. Once a satisfactory performance is achieved through the learning control, the parameters of the Smith predictor controller can be computed from the signals using a nonlinear least squares algorithm. The same relay feedback experiment can provide an initial parameter vector for an efficient implementation of the parameter estimation. Simulations and experimental results will be furnished to illustrate the effectiveness of the proposed tuning method.     

Adaptive control of nonlinear uncertain active suspension systems with prescribed performance

This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation) concerning the vehicle safety and mechanical constraints. An augmented neural network is developed to online compensate for the unknown nonlinearities, and a novel adaptive law is developed to estimate both NN weights and uncertain model parameters (e.g., sprung mass), where the parameter estimation error is used as a leakage term superimposed on the classical adaptations. To further improve the control performance and simplify the parameter tuning, a prescribed performance function (PPF) characterizing the error convergence rate, maximum overshoot and steady-state error is used to propose another adaptive control. The stability for the closed-loop system is proved and particular performance requirements are analyzed. Simulations are included to illustrate the effectiveness of the proposed control schemes.     

城轨列车减速度反馈制动力闭环控制方法

针对城轨列车在目前理论减速度开环控制模式下,实际制动减速度精度较低的问题,提出一种基于参数估计的制动力闭环控制方法.将列车运行于坡道、弯道等路段产生的附加运行阻力和摩擦副摩擦系数变化等导致的实际制动力偏差等效为列车在制动过程中所受的总扰动,以列车减速度和制动缸压力等作为输入,通过梯度估计方式对该扰动进行求解.根据总扰动...     

考虑传感器与执行器故障的EPS主动容错控制

针对传感器及执行器故障对EPS助力性能的影响,提出一种EPS主动容错控制方法。建立含参数不确定性、传感器与执行器故障的EPS系统模型,将系统不确定性转化为故障估计误差系统的扰动,基于未知输入观测器及线性矩阵不等式推导故障估计误差系统稳定并对扰动具有鲁棒性的充分条件,采用LMI区域极点配置法提升故障估计性能;在此基础上,针对执行器故障设计控制律补偿容错控制算法,针对传感器故障设计信号重构容错控制算法。Matlab/Simulink环境下的仿真结果表明,当传感器与执行器单独或同时发生故障时,设计的故障估计算法均可较为准确地估计故障幅值,故障估计的误差较小;针对不同故障对助力性能的影响,提出的容错控制方法均可使故障EPS系统的助力性能有所恢复。基于LabVIEW PXI的硬件在环试验进一步验证容错控制应用于EPS系统的有效性,提升汽车转向行驶的安全性及可靠性。     

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.     

基于改进自适应趋近律的弹丸协调臂滑模控制

针对某火炮弹丸协调臂电液伺服系统在传统滑模控制趋近律下存在抖振现象,收敛速度慢等问题,提出一种基于改进自适应趋近律的弹丸协调臂滑模控制。当系统状态变量距离切换面较远的时候,幂次项起主要作用;当系统状态变量距离切换面较近时,自适应变速项起主要作用,随着状态变量变化自适应调节变速项系数,直到状态变量收敛到稳定点。当系统存在参数不确定性和外界扰动时,滑模状态变量可在有限时间收敛到边界层宽度为2.6的稳定误差界内。仿真结果表明,控制策略能有效提高系统的动态精度和到位精度,提高系统的鲁棒性。     

永磁同步电机位置伺服系统改进变结构自抗扰控制

为进一步提高传统变结构自抗扰控制器的控制精度,增强永磁伺服驱动系统的抗干扰能力,提出一种改进变结构自抗扰控制策略。该方法在基于变结构原理设计的扩张状态观测器中引入位置、速度的观测误差以实现状态变量的无差估计,采用基于指数趋近律设计的非线性状态误差反馈控制律实现线性控制与非线性控制的平滑过渡,并在此基础上引入位置跟踪误差,提高伺服系统的跟踪性能。通过实验分析比较了改进变结构自抗扰控制与传统变结构自抗扰控制两种控制策略,结果显示改进控制策略较传统控制策略的位置跟踪误差减少了约30%。当负载突变时,传统控制策略的跟踪误差约为负载突变前最大跟踪误差的3.4倍,而改进变结构自抗扰控制策略仍能准确跟踪给定信号。     

Adaptive backstepping fault-tolerant control for flexible spacecraft with unknown bounded disturbances and actuator failures

In this paper, a robust adaptive fault-tolerant control approach to attitude tracking of flexible spacecraft is proposed for use in situations when there are reaction wheel/actuator failures, persistent bounded disturbances and unknown inertia parameter uncertainties. The controller is designed based on an adaptive backstepping sliding mode control scheme, and a sufficient condition under which this control law can render the system semi-globally input-to-state stable is also provided such that the closed-loop system is robust with respect to any disturbance within a quantifiable restriction on the amplitude, as well as the set of initial conditions, if the control gains are designed appropriately. Moreover, in the design, the control law does not need a fault detection and isolation mechanism even if the failure time instants, patterns and values on actuator failures are also unknown for the designers, as motivated from a practical spacecraft control application. In addition to detailed derivations of the new controller design and a rigorous sketch of all the associated stability and attitude error convergence proofs, illustrative simulation results of an application to flexible spacecraft show that high precise attitude control and vibration suppression are successfully achieved using various scenarios of controlling effective failures.     

考虑间隙特性的双机械臂模糊自适应鲁棒控制

为了克服间隙特性和未知扰动对双机械臂系统抓取物体性能的影响,设计了模糊自适应鲁棒控制律。首先建立了双机械臂系统数学模型和间隙特性模型,然后引入自适应律来估计未知扰动,利用模糊系统来估计系统模型参数,同时设计了自适应间隙逆模型来补偿间隙特性,并对系统进行了稳定性分析,最后实现了考虑间隙特性的双机械臂系统精确控制。仿真结果表明,设计的控制方法具有更好的快速性和准确性,能够确保物体在0.5 s内稳定跟踪轨迹指令,最大跟踪误差仅为0.5 cm;设计的自适应律能够快速准确估计未知扰动,最大估计误差仅为0.3 N·m,估计精度较高。     

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input–output direct feed-through term

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input–output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection.     

Barrier Lyapunov function-based model-free constraint position control for mechanical systems

In this article, a motion constraint control scheme is presented for mechanical systems without a modeling process by introducing a barrier Lyapunov function technique and adaptive estimation laws. The transformed error and filtered error surfaces are defined to constrain the motion tracking error in the prescribed boundary layers. Unknown parameters of mechanical systems are estimated using adaptive laws derived from the Lyapunov function. Then, robust control used the conventional sliding mode control, which give rise to excessive chattering, is changed to finite time-based control to alleviate undesirable chattering in the control action and to ensure finite-time error convergence. Finally, the constraint controller from the barrier Lyapunov function is designed and applied to the constraint of the position tracking error of the mechanical system. Two experimental examples for the XY table and articulated manipulator are shown to evaluate the proposed control scheme.

     

Robust disturbance rejection control of a biped robotic system using high-order extended state observer

This study addressed the problem of robust control of a biped robot based on disturbance estimation. Active disturbance rejection control was the paradigm used for controlling the biped robot by direct active estimation. A robust controller was developed to implement disturbance cancelation based on a linear extended state observer of high gain class. A robust high-gain scheme was proposed for developing a state estimator of the biped robot despite poor knowledge of the plant and the presence of uncertainties. The estimated states provided by the state estimator were used to implement a feedback controller that was effective in actively rejecting the perturbations as well as forcing the trajectory tracking error to within a small vicinity of the origin. The theoretical convergence of the tracking error was proven using the Lyapunov theory. The controller was implemented by numerical simulations that showed the convergence of the tracking error. A comparison with a high-order sliding-mode-observer-based controller confirmed the superior performance of the controller using the robust observer introduced in this study. Finally, the proposed controller was implemented on an actual biped robot using an embedded hardware-in-the-loop strategy.     

Velocity-free attitude coordinated tracking control for spacecraft formation flying

This article investigates the velocity-free attitude coordinated tracking control scheme for a group of spacecraft with the assumption that the angular velocities of the formation members are not available in control feedback. Initially, an angular velocity observer is constructed based on each individual's attitude quarternion. Then, the distributed attitude coordinated control law is designed by using the observed states, in which adaptive control method is adopted to handle the external disturbances. Stability of the overall closed-loop system is analyzed theoretically, which shows the system trajectory converges to a small set around origin with fast convergence rate. Numerical simulations are performed to demonstrate fast convergence and improved tracking performance of the proposed control strategy.     

Robust cubature Kalman filter based on variational Bayesian and transformed posterior sigma points error

An improved robust cubature Kalman filter (RCKF) based on variational Bayesian (VB) and transformed posterior sigma points error is proposed in this paper, which not only retains the robustness of RCKF, but also exhibits adaptivity in the presence of time-varying noise. First, a novel sigma-point update framework with uncertainties reduction is developed by employing the transformed posterior sigma points error. Then the VB is used to estimate the time-varying measurement noise, where the state-dependent noise is addressed in the iteratively parameter estimation. The new filter not only reduces the uncertainty on sigma points generation but also accelerates the convergence of VB-based noise estimation. The effectiveness of the proposed filter is verified on integrated navigation, and numerical simulations demonstrate that VB-RCKF outperforms VB-CKF and RCKF.     

Synthesis of nonlinear adaptive controller for a batch distillation

A nonlinear adaptive control strategy is proposed for a binary batch distillation column. The hybrid control algorithm comprises a generic model controller (GMC) and a nonlinear adaptive state estimator (ASE). The adaptive observation scheme mainly estimates the imprecisely known parameters based on the available tray temperature measurements. The sensitivity of the proposed estimator is investigated with respect to the effect of initialization error, unmeasured disturbance and uncertainty. Then, a comparative study is carried out between the derived nonlinear GMC-ASE controller and a traditional proportional integral law in terms of set point tracking and disturbance rejection performance. The study also includes the effect of measurement noise and parametric uncertainty on the closed-loop performance. The proposed adaptive control algorithm is shown to be quite promising due to the exponential error convergence capability of the ASE estimator in addition to the high-quality control action provided by the GMC controller.     

Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems

This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator.     

Disturbance observer based hierarchical control of coaxial-rotor UAV

This paper propose an hierarchical controller based on a new disturbance observer with finite time convergence (FTDO) to solve the path tracking of a small coaxial-rotor-typs Unmanned Aerial Vehicles (UAVs) despite of unknown aerodynamic efforts. The hierarchical control technique is used to separate the flight control problem into an inner loop that controls attitude and an outer loop that controls the thrust force acting on the vehicle. The new disturbance observer with finite time convergence is intergated to online estimate the unknown uncertainties and disturbances and to actively compensate them in finite time.The analysis further extends to the design of a control law that takes the disturbance estimation procedure into account. Numerical simulations are carried out to demonstrate the efficiency of the proposed control strategy.     

车辆质心侧偏角估计综述

车辆质心侧偏角是车辆稳定性控制系统中至关重要的状态变量。分别从横向车速估计方法、纵向车速估计方法和估计过程中的模型参数自适应估计三个方面回顾车辆行驶过程中的质心侧偏角估计问题。通过对比分析质心侧偏角估计中运动学估计方法和动力学估计方法的各自优缺点,指出多方法融合估计质心侧偏角的优势。比较分析不同种类的观测器技术对质心侧偏角估计效果的影响。分析指出参数自适应估计是提高质心侧偏角估计精度的有效手段,并分别介绍路面峰值附着系数、轮胎侧偏刚度、路面坡度角等参数的自适应估计方法。分析分布式驱动电动汽车结构特点对质心侧偏角估计问题带来的影响,指出充分利用电动机转矩信息是提高质心侧偏角估计的重要措施。针对分布式驱动电动汽车,先后分别列举分析横向车速、纵向车速和参数自适应估计方法的特殊之处和优势所在。