伺服电机驱动的连铸结晶器振动系统受限状态下预设性能控制

以伺服电机驱动的连铸结晶器振动位移系统为研究对象,针对系统输入和状态受限问题,考虑系统存在的参数不确定性和负载转矩扰动影响,设计一种基于观测器的预设性能自适应控制器.首先,针对系统存在的参数不确定性、负载转矩扰动等问题,基于Lyapunov函数设计变增益扩张状态观测器,在保证观测精度的同时,削弱峰值现象;其次,考虑状态和输入受限的情况,将预设性能函数与Backstepping技术相结合设计控制器,构建指令滤波器解决“计算膨胀”问题,引入动态补偿量对观测器及受限状态产生的误差进行补偿,并对所设计的控制器进行稳定性分析;最后,通过仿真对比实验验证控制器的有效性.     

A new adaptive backstepping method for nonlinear control of turbine main steam valve

A new approach for nonlinear adaptive control of turbine main steam valve is developed. In comparison with the existing controller based on ＂classical＂ adaptive backstepping, this method does not follow the classical certaintyequivalence principle in the design of adaptive control law. We introduce this approach, for the first time, to power systems and present a novel parameter estimator and dynamic feedback controller for a single machine infinite bus （SMIB） system with steam valve control. This system contains unknown parameters such as reactance of transmission lines. Besides preserving useful nonlinearities and the real-time estimation of uncertain parameters, the proposed approach possesses better performances with respect to the response of the system and the speed of adaptation. The simulation results demonstrate that the proposed approach is better than the design based on ＂classical＂ adaptive backstepping in terms of properties of stability and parameter estimation, and recovers the performance of the ＂full-information＂ controller. Hence, the proposed method provides an alternative for engineers in applications.     

带有未知死区模型的鲁棒自适应模糊控制

针对一类带有死区模型并具有未知函数控制增益的SISO非线性系统，根据滑模控制原理。利用Nussbaum函数的性质，提出一种自适应模糊控制器的设计方案．该方案取消了函数控制增益符号已知和死区模型参数上界、下界已知的条件．通过引入最优逼近误差的自适应补偿项来消除建模误差和参数估计误差的影响．理论分析证明了闭环系统的稳定性和跟踪误差收敛到零．仿真结果表明了该方法的有效性．     

Robust adaptive backstepping tracking control of stochastic nonlinear systems with unknown input saturation: A command filter approach

This paper studies the problem of adaptive observer‐based radial basis function neural network tracking control for a class of strict‐feedback stochastic nonlinear systems comprising an unknown input saturation, uncertainties, and unknown disturbances. To handle the issue of a non‐smooth saturation input signal, a smooth function is chosen to approximate the saturation function and the state observer is used to estimate unmeasured states. By the so‐called command filter method in the controller design procedure, the implementation complexity is reduced in the proposed backstepping method. Moreover, a radial basis function neural network is deployed to reconstruct the unknown nonlinear functions. In addition, the gains of all radial basis function neural networks are updated through one updating law leading to a minimal learning parameter which is independent of the number of neural nodes and the order of the system. Comparing with the existing results, the proposed approach can stabilize a constrained stochastic system more effectively and with less computational burden. Finally, a practical example shows the performance of the proposed controller design.     

A constrained recursive pseudo-linear regression scheme for on-line parameter estimation in adaptive control

In adaptive control of systems with poles close to the unit circle, application of the recursive estimation techniques can lead to excursions of the poles of the identified model outside the unit circle even when the process is open loop stable. These excursions can be of two types. The poles of the deterministic component of the model can drift outside unit circle even when the process has no unstable modes. Alternatively, the poles and/or zeros of the unmeasured disturbance (noise) model can drift outside the unit circle. In either case, the identified model is not suitable for on-line controller adaptation. In this work, a novel constrained recursive formulation is proposed for on-line parameter estimation based on the pseudo-linear regression (PLR) approach. The efficacy of the proposed approach is demonstrated by conducting experimental studies on a benchmark laboratory scale heater-mixer setup. The analysis of the open and closed loop experimental results reveals that the proposed constrained parameter estimation scheme provides a systematic and computationally attractive approach to ensure that the identified model parameters are restricted to the feasible region.     

NONLINEAR ROBUST CONTROL FOR PARALLEL AC/DC TRANSMISSION SYSTEMS: A NEW ADAPTIVE BACK-STEPPING APPROACH

ABSTRACT

By utilizing the controllability of High Voltage Direct Current (HVDC), which means that the power delivered can be modulated, to improve the stability and operation performances of the parallel AC/DC transmission system, a new adaptive back-stepping approach for this system is developed. Compared with the existing controller based on “classical” adaptive back-stepping, the approach does not follow the classical certainty-equivalence principle. We introduce this approach, for the first time, into parallel AC/DC systems containing unknown parameters and present a novel parameter estimator and dynamics feedback controller. Besides the preserving useful nonlinearities and the real-time estimation of uncertainty parameter, the proposed approach possesses better performances with respect to the response of the system and the speed of adaptation. Simulation results demonstrate that the proposed approach is better than the design based on “classical” adaptive back-stepping in terms of properties of stability and parameter estimation and that it recovers the performance of the “full-information” controller, which is obtained by assuming that the parameters are known and apply standard back-stepping, hence it will be an alternative to practice engineering and applications.     

约束非线性系统构造性模型预测控制

研究了连续时间约束非线性系统模型预测控制设计.利用控制Lyapunov函数离线构造单变量可调预测控制器,再根据性能指标在线优化可调参数,其中该参数近似于闭环系统的"衰减率".同时,控制Lyapunov函数保证了算法的可行性和闭环系统的稳定性.最后通过数值仿真验证了该算法的有效性.     

Model Predictive Control in Pulsed Electrochemical Machining

In this work a Model Predictive Control (MPC) approach is used for controlling a Pulsed Electrochemical Machining (PECM) process. The MPC problem is formulated in order to optimally reach a desired state while satisfying various restrictions. PECM is modeled as a constrained nonlinear system. In the first approach the system is input-output linearized and a linear MPC scheme is applied to control it. In comparison a second approach uses the linearization around the current working point resulting in a Linear Time Variant system. This linear system is controlled using Linear Time Variant MPC (LTV-MPC). The simulation results are compared and the most promising controller is implemented on a real time platform controlling a PECM plant. The experimental results with online parameter estimation are shown and discussed.     

仿生假手抓握力控制策略

为了使仿生假手完成各种精细作业,提出一种抓握力控制策略.在自由空间和约束空间中分别使用基于位置的阻抗控制和力跟踪阻抗控制.在过渡过程中使用模糊观测器切换控制模式.两种控制模式采用同一个基于位置的阻抗控制器,在约束空间向阻抗控制器中引入参考力,以满足约束空间的抓握力控制要求.这种方法可以使关节在自由空间和约束空间中分别实现良好的轨迹跟踪和力矩跟踪,在过渡过程中实现控制模式的可靠切换和系统的稳定过渡.提出一种自适应滑模摩擦力补偿方法,利用终端滑模思想设计了滑模函数,使得系统跟踪误差在有限时间内收敛,避免了传统线性滑模面状态跟踪误差无法在有限时间内收敛至0的问题.根据指数形式摩擦力的特点,利用终端滑模控制思想获得包含摩擦力参数估计的滑模控制律,并基于李亚普诺夫稳定性定理推导了估计参数的在线自适应律.对该抓握力控制策略在HIT假手上进行了抓取实验,实验结果证明了控制策略的有效性.     

Posture control of a 3-RPS pneumatic parallel platform with parameter initialization and an adaptive robust method

A control algorithm for a 3-RPS parallel platform driven by pneumatic cylinders is discussed. All cylinders are controlled by proportional directional valves while the kinematic and dynamic properties of the system are modeled. The method of adaptive robust control is applied to the controller using a back-stepping approach and online parameter estimation. To compensate for the uncertainty and the influence caused by estimations, a fast dynamic compensator is integrated in the controller design. To prevent any influence caused by the load applied to the moving platform changing in a practical working situation, the identification of parameters is taken as the initialization of unknown parameters in the controller, which can improve the adaptability of the algorithm. Using these methods, the response rate of the parameter estimation and control performance were improved significantly. The adverse effects of load and restriction forces were eliminated by the initialization and online estimation. Experiments under different situations illustrated the effectiveness of the adaptive robust controller with parameter initialization, approaching average tracking errors of less than 1%.     

Robust Adaptive Fuzzy Control of Nonlinear Systems with Unknown and Time‐Varying Saturation

In this paper, a robust adaptive fuzzy control approach is proposed for a class of nonlinear systems in strict‐feedback form with the unknown time‐varying saturation input. To deal with the time‐varying saturation problem, a novel controller separation approach is proposed in the literature to separate the desired control signal from the practical constrained control input. Furthermore, an optimized adaptation method is applied to the dynamic surface control design to reduce the number of adaptive parameters. By utilizing the Lyapunov synthesis, the fuzzy logic system technique and the Nussbaum function technique, an adaptive fuzzy control algorithm is constructed to guarantee that all the signals in the closed‐loop control system remain semiglobally uniformly ultimately bounded, and the tracking error is driven to an adjustable neighborhood of the origin. Finally, some numerical examples are provided to validate the effectiveness of the proposed control scheme in the literature.     

Simultaneous fault estimation and fault-tolerant tracking control for uncertain nonlinear discrete-time systems

This paper deals with simultaneous fault estimation and control for a class of nonlinear systems with parameter uncertainty, which is described by Takagi–Sugeno (T–S) fuzzy model with parameter uncertainties and unknown disturbance. In this paper, a fuzzy reference model is used to generate error dynamic for tracking control. By considering actuator fault as an auxiliary state vector, we construct an augmented error system and propose a fault estimator/controller to achieve simultaneous fault estimation and fault-tolerant tracking control. H_∞ approach is used in the design of estimator/controller to attenuate the effect of the unknown disturbance and parameter uncertainties. The design conditions are formulated into a set of linear matrix inequalities (LMIs), which can be efficiently solved. Finally, a pitch-axis nonlinear missile model is used to illustrate the effectiveness of the proposed method.     

Fault-tolerant control systems design via subdivision of parameter region

This paper presents a linear matrix inequality （LMI） approach to solve the fault-tolerant control （FTC） problem of actuator faults. The range of actuator faults is considered as a parameter region and subdivided into several subregions to achieve a certain desired performance specification. Based on the integral quadratic constraint （IQC） approach, a passive fault-tolerant controller for the whole fault region and multiple fault-tolerant controllers for each fault subregion are designed for guaranteeing stability and improving performance of the FTC system, respectively. According to the estimation of parameters by FDI process, the corresponding subregion controller is chosen for the stability and optimal performance of closed-loop systems when the fault occurs. The case of incorrect estimation is also considered by comparing the performance index between the switched controller and the passive fault-tolerant controller. The proposed design technique is finally evaluated in the light of a simulation example.     

A parameter space approach to constrained variance PID controller design

This paper presents a parameter space approach to constrained variance and minimum variance PID controller design for LTI models. The technique is based on rational transfer functions of the plant and noise models. Loci corresponding to a fixed variance can be mapped into parameter planes for PID and PI type controllers to graphically display regions which satisfy the constraint, thereby guiding a design to appropriate controller gains. Requirements for tracking regulation can be readily achieved without excessively increasing the output variance. The usual advantages of parameter space techniques apply where other design criteria may be superimposed, allowing multiple objectives to be achieved non-conservatively. The superposition of the parameter space boundaries from existing robust control techniques thus allows non-conservative robust minimum variance PID design. A design example compares the technique to an algebraic minimum variance design using an integrator when tracking is required.     

Noncertainty equivalent adaptive control of robot manipulators without velocity measurements

This paper presents a noncertainty equivalent adaptive motion control scheme for robot manipulators in the absence of link velocity measurements. A new output feedback adaptation algorithm, based on the attractive manifold design approach, is developed. A proportional-integral adaptation is selected for the adaptive parameter estimator to strengthen the passivity of the system. In order to relieve velocity measurements, an observer is designed to estimate the velocities. The controller guarantees semiglobal asymptotic motion tracking and velocity estimation, as well as L_∞ and L₂ bounded parameter estimation error. The effectiveness of the proposed controller is verified by simulations for a two-link robot manipulator and a four-bar linkage. The results are further compared with the earlier certainty-equivalent adaptive partial and full state feedback controller to highlight potential closed-loop performance improvements.     

Adaptive H∞ control with application to systemswith structural flexibility

We develop a robust adaptive control algorithm using a combination of H_∞ design and system identification. We derive frequency dependent bounds for the tolerated unmodeled dynamics and show that the approach gives a closed-loop system with bounded l_∞ , and l₂ gain when the model mismatch is small in the frequency range where the control gain is large. Our application focus is systems with structural flexibility. We present a parameter estimation algorithm that uses constrained least squares with prefiltering to overcome the problem of identifying lightly damped antiresonances (a common problem in identification of flexible systems). The estimation and control design are executed at a low frequency and only when parameter updating is needed. This allows us to apply computationally expensive control design and signal processing algorithms. It also eliminates many of the problems of earlier adaptive controllers (such as bursting, parameter drift, etc.) by turning off the estimator. We show results from the application of adaptive H_∞ control to a high-fidelity model of the Martin Marietta flexible beam testbed     

Constrained min-max predictive control: modifications of the objective function leading to polynomial complexity

In this note, an efficient way of implementing a constrained min-max predictive controller is presented. The new approach modifies the objective function in such a way that the resulting min-max problem can be solved in polynomial time. Different modifications are proposed. The main contribution of the note is to provide a robust constrained min-max predictive controller that can be implemented in real time. The new controller stabilizes the uncertain system.     

离散时间间接型模型参考自适应控制的一种系统化的分析方法

This paper presents the design and analysis of indirect model reference adaptive control(MRAC)with normalized adaptive law for a class of discrete-time systems.The main work includes three parts.Firstly,the constructed plant parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms but also avoids the possibility of division by zero.Secondly,by finding the relationship between the plant parameter estimate and controller parameter estimate and using the properties of plant parameter estimate,the similar properties of controller parameter estimate are also established. Thirdly,based on the relationship properties between the normalizing signal and all the signals in the closed-loop system and on some important mathematical tools on discrete-time systems,as in the continuous-time case,a systematic stability and convergence analysis approach to the discrete indirect MRAC scheme is developed rigorously.     

Adaptive parameter estimation for a constrained low-pass system

A novel algorithm is presented for adaptive parameter estimation for a constrained low-pass Butterworth system model. The algorithm will estimate the system cutoff frequency and gain online. When it is known that the true system has a low-pass Butterworth structure or some transfer function similar to it and its true order is used, the algorithm will lead to a substantial savings in computation and more accurate results than unconstrained algorithms. Potential applications include filter design and adaptive decision on Nyquist rate for systems