Robust Speed Control of a Low Damped Electromechanical System Based on CRONE Control: Application to a Four Mass Experimental Test Bench

Robust speed control of a low damped electromechanical system with backlash is studied, controlled load angular speed being not measured. The proposed control strategy combines a Luenberger observer (load angular speed and load torque disturbance estimations) and a robust CRONE controller. The observer provides estimation of the load angular speed and of the disturbance torque applied on the load. Through the computation of only three independent parameters (as many as a PID controller), the CRONE controller permits to ensure the robust speed control of the load in spite of plant parametric variations and speed observation errors. The proposed control strategy is applied to a four mass experimental test bench.     

Decentralized CRONE control of nonsquare multivariable systems in path-tracking design

In this paper, motion control and robust path tracking were extended to nonsquare MIMO (Multi-Input Multi-Output) systems having more outputs than inputs. A path-tracking design based on fractional prefilter approach has been developed and extended to control nonsquare MIMO systems. The nonsquare relative gain array (NRG) is used to assess the performance of nonsquare control systems based on steady-state information. The CRONE control approach developed for multivariable plants based on third-generation SISO CRONE methodology is combined with MIMO-QFT (Quantitative Feedback Theory) robust design methodology, taking into account the plant uncertainties. After the determination of CRONE controller, the parameter of prefilter has been optimized considering physical constraints of actuators and the tracking performance specifications. The proposed design is applied to an example.     

Fuzzy speed control with an acceleration observer for a permanent magnet synchronous motor

Based on the Takagi–Sugeno fuzzy approach we design a fuzzy speed regulator as well as a fuzzy acceleration observer for a permanent magnet synchronous motor (PMSM). The proposed observer-based fuzzy speed regulator is independent of the load torque value. We derive the sufficient conditions for the existence of the regulator and the observer in terms of linear matrix inequalities (LMIs), and also give LMI parameterizations of the gain matrices. Simulation and experimental results are given to verify that the proposed control method can be used to accurately control the speed of a PMSM under model parameter and load torque variations.     

Nonlinear control of induction motors based on state error PCH and energy-shaping principle

A novel nonlinear control scheme of induction motor (IM) is presented based on state error port-controlled Hamiltonian (PCH) systems and energy-shaping (ES) principle. The PCH model of IM system is established. Using interconnection assignment and damping injection method, the desired state error PCH structure is assigned to the closed-loop IM system by the ES principle. The controllers are designed when the load torque is known and unknown, respectively. A load torque estimator is developed in the presence of the load torque disturbance. Moreover, an observer is proposed to estimate the unknown load torque. The stability of the closed-loop system is also verified. Finally, speed regulation of the IM drive system is implemented based on space vector pulse-width modulation technology. The simulation results show that the system has good load disturbance attenuation and speed tracking performances.     

Adaptive control of a chaotic permanent magnet synchronous motor

This paper proposes a simple adaptive controller design method for a chaotic permanent magnet synchronous motor (PMSM) based on the sliding mode control theory which has given an effective means to design robust controllers for nonlinear systems with bounded uncertainties. The proposed sliding mode adaptive controller does not require any information on the PMSM parameter and load torque values, thus it is insensitive to model parameter and load torque variations. Simulation results are given to verify that the proposed method can be successfully used to control a chaotic PMSM under model parameter and load torque variations.     

猎雷声纳基阵姿态稳定系统自适应反步法控制

针对带非线性摩擦力矩和负载扰动的高精度猎雷声纳基阵姿态稳定系统,提出了一种基于神经网络的自适应反步法控制方法。其中神经网络用于估计未知非线性摩擦力矩,进而设计反步法控制器和参数自适应律来对神经网络估计误差和负载扰动进行补偿。最后应用Lyapunov方法证明了所提出的自适应控制器能保证闭环系统的稳定性,并且可以通过选择适当的控制器参数来调整收敛率。仿真结果表明,基于神经网络的自适应反步法控制方法与PID控制相比,系统的动、静态性能指标及鲁棒性得到了全面的改善,与双闭环PID控制相比,跟踪精度提高了3倍多。     

Nonlinear robust and optimal control of robot manipulators

In this paper, we propose a new optimal control method for robust control of nonlinear robot manipulators. Many industrial robot systems are required to perform relatively large angular movement with sufficient accuracy. In real circumstances, highly nonlinear manipulator dynamics and uncertainties such as unknown load placed on the manipulator, external disturbance, and joint friction make the precise control of manipulators a very challenging task. The main contribution of this work is to develop a new robust control strategy to accomplish the precise control of robot manipulators under load uncertainty using a nonlinear optimal control formulation and solution. This methodology is based on the underlying relation between the robust stability and performance optimality. A class of robust control problems can be transformed to an equivalent optimal control problem by incorporating the uncertainty bounds into the cost functional. The θ-D optimal control approach is utilized to find an approximate closed-form feedback solution to the resultant nonlinear optimal control problem via a perturbation process. Numerical simulations show that the proposed robust controller is able to control the robot manipulator precisely under large load variations.     

Adaptive synchronization for uncertain complex dynamical network using fuzzy disturbance observer

This paper proposes a robust adaptive controller design method for synchronization of a complex dynamical network with uncertainty and disturbance. A fuzzy disturbance observer is used to estimate the overall disturbances without any prior knowledge about them. The proposed control method globally asymptotically synchronizes the network using adaptation laws obtained by using Lyapunov stability theory. The proposed method is applied to two chaotic systems and the results show the effectiveness of the approach.     

CRONE Control: Principles and Extension to Time-Variant Plants with Asymptotically Constant Coefficients

The principles of CRONE control, a frequency-domain robust control designmethodology based on fractional differentiation, are presented.Continuous time-variant plants with asymptotically constant coefficientsare analysed in the frequency domain, through their representation usingtime-variant frequency responses. A stability theorem for feedbacksystems including time-variant plants with asymptotically constantcoefficients is proposed. Finally, CRONE control is extended to robustcontrol of these plants.     

Control structure with disturbance identification for Lagrangian systems

A new control structure for a class of uncertain Lagrangian systems that solves the regulation and tracking control problems is proposed. This structure has good robustness properties, similar to sliding-mode-type controllers; and is free from chattering. The control structure is based on a discontinuous, robust observer, which displays a second-order sliding mode yielding an exponential convergence to the state of the plant in spite of the existence of non-vanishing disturbances and parameter uncertainties. At the same time, with the aid of a low-pass filter, this observer is employed to estimate the perturbations affecting the plant. This disturbance estimation is used to compensate the perturbations acting on the plant, improving the controller robustness. The performance of the proposed control structure is illustrated numerically and experimentally.     

Nonlinear system control using a self-organizing functional-linked neuro-fuzzy network

This study presents a self-organizing functional-linked neuro-fuzzy network (SFNN) for a nonlinear system controller design. An online learning algorithm, which consists of structure learning and parameter learning of a SFNN, is presented. The structure learning is designed to determine the number of fuzzy rules and the parameter learning is designed to adjust the parameters of membership function and corresponding weights. Thus, an adaptive self-organizing functional-linked neuro-fuzzy control (ASFNC) system, which is composed of a computation controller and a robust compensator, is proposed. In the computation controller, a SFNN observer is utilized to approximate the system dynamic and the robust compensator is designed to eliminate the effect of the approximation error introduced by the SFNN observer upon the system stability. Finally, to show the effectiveness of the proposed ASFNC system, it is applied to a chaotic system. The simulation results demonstrate that favorable control performance can be achieved by the proposed ASFNC scheme without any knowledge of the control plants and without requiring preliminary offline tuning of the SFNN observer.     

Path tracking design based on Davidson–Cole prefilter using a centralized CRONE controller applied to multivariable systems

To perfectly govern MIMO (Multi-Input Multi-Output) processes, a fully populated matrix controller has been developed due to its flexibility. Taking into account the plant uncertainties, the CRONE (Commande Robuste d’Ordre Non Entier) control approach is used with the non-diagonal quantitative feedback theory (QFT) procedure. The MIMO-QFT robust synthesis methodology has been used in order to generate the appropriate equivalent MISO (Multi-Input Single-Output) system structure from the MIMO plant. For each MISO structure, the CRONE control approach based on third-generation CRONE methodology is used to find the diagonal elements of the controller of the plant while considering the plant uncertainties. The non-diagonal part of the controller is determined to reach the aim of minimizing the coupling effects. After that, a fractional prefilter synthesis approach is developed to find the non-integer prefilter expression satisfying the tracking specifications. A SCARA robot manipulator has been used to verify the designed controller performances.     

折叠翼飞行器的动力学建模与稳定控制

折叠翼飞行器在变形过程中,其动力学模型呈现多刚体、多自由度和强非线性特点,同时气动力/力矩、压心、质心和转动惯量等参数也会大幅度变化,严重影响飞行稳定性.由此,本论文将对飞行器的多刚体动力学建模与变形稳定控制进行研究.基于凯恩方法建立了折叠翼飞行器的多刚体动力学模型,并从中得到了变形所产生的附加力和力矩表达式.通过气动计算拟合出气动参数与折叠角之间的函数关系,由此分析了不同折叠角速度下飞行器的纵向动态特性,结果表明,折叠翼飞行器变形过程中速度、高度和俯仰角均会发生变化,飞行器无法保持稳定飞行.为此提出了一种基于自抗扰理论的飞行器变形过程中的稳定控制方法.将折叠翼飞行器纵向非线性动力学模型中存在的非线性项、耦合项以及参数时变项都视为系统内外总扰动,利用扩张状态观测器对总扰动进行实时估计和补偿,针对补偿后的系统设计PD控制器,实现了速度通道和高度通道的解耦控制.通过Lyapunov稳定性原理证明了系统的稳定性,并进行数学仿真验证.仿真结果表明,基于自抗扰理论设计的稳定控制器能够解决飞行器变形所带来的强非线性和参数时变等问题,保证飞行器的高精度稳定控制.     

可重复使用运载器大姿态机动自抗扰控制

针对可重复使用运载器大俯仰角或偏航角转弯机动而产生的姿态角奇异的控制问题,提出了基于四元数的自抗扰控制方法。通过两级跟踪微分器从期望四元数中逐步得到三通道解耦的角加速度信号,然后利用扩张状态观测器观测模型中的不确定项,最终采用动态逆得到解耦的三通道发动机等效摆角或RCS(Reaction Control System)等控制信号,并设计了数字滤波器对弹性振动与液体晃动信号进行滤波处理。考虑到系统模型具有非线性、不确定性、11阶弹性振动、一阶液体晃动、风干扰和气动偏差等多种外部扰动条件,对可重复使用运载器从主动段到再入飞行段进行了非线性六自由度仿真分析。仿真结果表明,基于四元数的自抗扰姿态控制器具有快速、平稳、超调量小、抗干扰能力强、无系统抖振且控制参数较少的特点。     

DYNAMIC MODELING AND STABILITY CONTROL OF FOLDING WING AIRCRAFT 1)

折叠翼飞行器在变形过程中,其动力学模型呈现多刚体、多自由度和强非线性特点,同时气动力/力矩、压心、质心和转动惯量等参数也会大幅度变化,严重影响飞行稳定性. 由此,本论文将对飞行器的多刚体动力学建模与变形稳定控制进行研究.基于凯恩方法建立了折叠翼飞行器的多刚体动力学模型,并从中得到了变形所产生的附加力和力矩表达式.通过气动计算拟合出气动参数与折叠角之间的函数关系,由此分析了不同折叠角速度下飞行器的纵向动态特性, 结果表明,折叠翼飞行器变形过程中速度、高度和俯仰角均会发生变化,飞行器无法保持稳定飞行.为此提出了一种基于自抗扰理论的飞行器变形过程中的稳定控制方法.将折叠翼飞行器纵向非线性动力学模型中存在的非线性项、耦合项以及参数时变项都视为系统内外总扰动,利用扩张状态观测器对总扰动进行实时估计和补偿, 针对补偿后的系统设计PD控制器,实现了速度通道和高度通道的解耦控制.通过Lyapunov稳定性原理证明了系统的稳定性, 并进行数学仿真验证. 仿真结果表明,基于自抗扰理论设计的稳定控制器能够解决飞行器变形所带来的强非线性和参数时变等问题,保证飞行器的高精度稳定控制.     

Passive-based adaptive control with the full-order observer for induction motor without speed sensor

The conventional linear control methods are difficult to meet the control requirements of high-performance speed regulation of asynchronous motor due to the nonlinear and multi-variable problems of induction motor. A passive-based control method of induction motor with the full-order state observer is proposed with the Euler–Lagrange equation of motion of the induction motor. Based on the relationship between passivity and stability of induction motor, the state feedback is used for torque and speed tracking. The full-order state observer is adopted with rotor current and rotor flux as state variables, and the adaptive speed controller is designed to realize the passive-based control. The experimental results show that the errors between the estimated value based on the proposed full-order observer and the actual value of rotor current, speed and flux are small; the speed with the proposed adaptive control can reach the expected value quickly. The proposed control method can effectively meet the high-performance requirements of induction motor.

     

Reduced-order observer-based backstepping tracking control for a class of stochastic nonlinear systems

In this paper, an output feedback tracking control scheme is put forwarded for a class of stochastic nonlinear systems, whose dynamics involve not only unknown parameters but also unmeasured states multiplied by output nonlinearities. A type of reduced-order observer is first developed. By adding some output related items in the observer, the estimation error realize global asymptotic convergence under disturbance free condition, and global bounded convergence when considering disturbance. Besides, the dimension of the closed-loop system is reduced, and the update law of this observer gain is beneficial for steady tracking. After the observer was established, the controller is constructed by employing the adaptive backstepping approach, and a smooth nonsingular robust item is proposed to handle the influence of stochastic disturbance. All the signals in the closed system is proved to be globally bounded in probability. Moreover the output tracking error converges to an arbitrary small neighborhood of the origin by proper choosing of the design parameters. The simulation results based on current control scheme and the comparison with the previous method illustrate that the proposed output feedback scheme realizes good tracking performance and strong ability on stochastic disturbance attenuation.     

Backstepping control for the optoelectronic stabilized platform based on adaptive fuzzy logic system and nonlinear disturbance observer

A composite controller based on a backstepping controller with an adaptive fuzzy logic system and a nonlinear disturbance observer is proposed in this paper to address the disturbance and uncertainty issues in the control of the optoelectronic stabilized platform. The matched and unmatched disturbances and system uncertainty are included in the stabilized platform model. The system's uncertainty and disturbance are approximated and estimated using an adaptive fuzzy logic system and a nonlinear disturbance observer. Moreover, the backstepping control algorithm is utilized to control the system. The simulations are performed in four states to confirm the viability of the proposed control technique. The proportional integral controller, proportional integral-disturbance observer controller, and fuzzy backstepping controller are contrasted with the proposed controller. It has been noted that the proposed controller's instantaneous disturbance's highest value is 5.1°/s. The maximal value of the coupling output for the two gimbals utilizing the proposed controller, however, is 0.0008°/s and 0.0018°/s, respectively. The findings presented here demonstrate that the backstepping controller, which is based on an adaptive fuzzy logic system and a nonlinear disturbance observer, is capable of precise tracking and dynamic tracking of a stabilized platform under disturbance and uncertainty.

     

ATTITUDE CONTROL TECHNOLOGY FOR MASS MOMENT NANO-SATELLITE IN LOW EARTH ORBIT 1)

针对气动力矩严重影响低轨纳卫星姿态控制效果的问题,创新性地提出了利用质量矩技术将气动干扰转化为控制力矩的解决方法.由于气动力矩矢量垂直于大气来流速度方向,因而采用质量矩与磁力矩相结合的方式三轴全驱动控制卫星姿态,从而避免系统欠驱动. 建立双执行机构控制方式的姿态动力学模型,并根据各干扰项的影响简化了控制方程.针对气动力不确定、星体参数误差、未知环境影响等复杂干扰,设计了针对理想控制力矩基于干扰观测器的滑模控制器. 为减小滑块附加干扰力矩,研究了理想控制力矩的最优分配策略. 最后, 为双执行机构搭建了半物理仿真平台,结果表明: 姿态机动过程中, 与滑块加速度相关的附加惯性力矩远大于其他干扰项,最优力矩分配策略能够大幅减小快时变的附加干扰, 优化效果明显; 姿态保持过程中,干扰观测器能有效观测系统慢时变干扰, 提高滑模控制律的姿态控制精度,姿态角收敛误差小于$\pm $0.1$^\circ$.最终验证了在低轨纳卫星上利用质量矩技术控制姿态的可行性.     

Adaptive sliding mode control of dynamic system using RBF neural network

This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network (NN) for a class of time varying system in the presence of model uncertainties and external disturbance. Adaptive RBF neural network controller that can learn the unknown upper bound of model uncertainties and external disturbances is incorporated into the adaptive sliding mode control system in the same Lyapunov framework. The proposed adaptive sliding mode controller can on line update the estimates of system dynamics. The asymptotical stability of the closed-loop system, the convergence of the neural network weight-updating process, and the boundedness of the neural network weight estimation errors can be strictly guaranteed. Numerical simulation for a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive RBF sliding mode control scheme.