基于奇异摄动的双连杆柔性臂模糊控制

讨论了双连杆柔性臂位置控制问题。应用拉格朗日-假设模态法建立系统的动力学方程，并用奇异摄动法将双连杆柔性臂系统分解为两个降阶的慢变子系统和快变子系统。针对柔性臂强非线性、强耦合性及不确定性等特点，给出一种慢变子系统在反馈线性化后采用模糊控制、快变子系统因呈线性系统而采用简单的最优控制的混合控制方法。其中，模糊控制是二维PD型控制器，其输入为关节角跟踪误差及其导数。最后进行了计算机仿真，结果表明，该方法不仅能实现柔性臂轨迹的快速、准确跟踪，有效的抑制弹性振动，并且对负载的变化具有强的鲁棒性。     

全局收敛的带有输出约束柔性关节机械臂分段控制

针对带有输出约束和模型不确定的柔性关节机械臂系统,运用奇异摄动法将系统解耦成慢子与快子系统且分别进行控制器设计,从而实现与刚性控制方法的联系且能减少计算量.针对快子系统,采用速度差值反馈来抑制关节柔性引起的系统弹性振动.针对慢子系统提出了一种全局收敛的分段控制策略,将收敛域拓展到全局,克服了基于tan-障碍Lyapuov函数(BLF)反演控制需要系统初始误差在收敛域内的缺陷,且应用径向基(RBF)神经网络消除未知干扰和模型不确定性引起的误差,至此保证了系统的轨迹跟踪和输出约束要求.仿真对比表明,所提方法能使柔性关节机械臂在任意初始位置均能保持良好的跟踪性能,体现了控制器的有效性和优越性.     

单连杆柔性臂负载自适应模糊滑模控制

针对单连杆柔性臂，提出了负载自适应模糊滑模控制与最优控制相结合的混合控制方法。首先，采用奇异摄动将系统分为慢变和快变两个子系统。然后，对慢变子系统采用负载自适应模糊滑模控制，快变子系统采用最优控制。最后，仿真结果表明，该方法不仅能实现柔性臂轨迹的快速、准确跟踪，有效地抑制弹性振动，并且对负载的变化具有强的鲁棒性。     

双连杆柔性臂轨迹跟踪的鲁棒控制

研究了双连杆柔性臂轨迹跟踪的鲁棒控制问题·基于假设模态法和奇异摄动法,导 出了双连杆柔性臂系统的动力学方程,并将系统模型分离为慢变和快变两个子系统.针对柔 性臂的特点,提出了关节角的补偿控制思想,并且给出了补偿控制算法.对两个子系统分别采 用滑模变结构控制和H∞控制,由此得到的组合控制使系统精确跟踪目标轨迹.研制了双连 杆柔性臂实验台,并对文中提出的方法进行了实验.     

力矩受限的柔性空间机器人模糊神经网络自适应跟踪控制及振动抑制

针对力矩受限和存在参数不确定情况下,自由漂浮柔性空间机器人（FFFSR）关节轨迹跟踪控制与柔性振动抑制的问题,利用奇异摄动法将系统分解为关节轨迹跟踪的慢变子系统和描述柔性振动的快变子系统,进而提出含慢、快变控制项的组合控制器。对于慢变子系统,设计一种无需模型的模糊径向基函数（RBF）神经网络（FRBFNN）自适应跟踪控制方案,利用神经网络观测器估计关节角速度信息,并对系统的未知非线性函数进行逼近;对于快变子系统,采用扩张状态观测器（ESO）对不易测量的柔性模态坐标导数和不确定扰动进行估计,并结合线性二次调节器（LQR）方法抑制柔性振动。数值仿真结果表明,当控制力矩限制在±20 N·m和±10 N·m范围内时,该组合控制器能够在2.5 s实现稳定的关节轨迹跟踪,并将柔性振动幅值限制在±1×10^-3 m内。     

力矩输入有界的柔性关节机器人轨迹跟踪控制

为解决柔性关节机器人在关节驱动力矩输出受限情况下的轨迹跟踪控制问题,提出一种基于奇异摄动理论的有界控制器.首先,利用奇异摄动理论将柔性关节机器人动力学模型解耦成快、慢两个子系统.然后,引入一类平滑饱和函数和径向基函数神经网络非线性逼近手段,依据反步策略设计了针对慢子系统的有界控制器.在快子系统的有界控制器设计中,通过关节弹性力矩跟踪误差的滤波处理加速系统的收敛.同时,在快、慢子系统控制器中均采用模糊逻辑实现控制参数的在线动态自调整.此外,结合李雅普诺夫稳定理论给出了严格的系统稳定性证明.最后,通过仿真对比实验验证了所提出控制方法的有效性和优越性.     

一种基于非线性滤波的倒立摆控制方法研究

对于倒立摆这样的强非线性系统,采用传统的BP算法存在着收敛速度慢、易陷入局部极小值的缺陷,而采用卡尔曼滤波方法则会带来很大的模型误差。为了解决上述问题,提出了基于粒子滤波优化神经网络的方法。首先建立了倒立摆神经网络控制器的物理模型并将模型粒子化,而后用粒子滤波算法对粒子进行优化估计,将估计结果作为网络的权值应用到倒立摆控制中,采用离线训练方式,仿真比较了卡尔曼滤波和粒子滤波两种方法控制效果,结果表明,新算法较卡尔曼滤波方法在控制性能上有明显提高。     

基于积分流形的柔性机械手组合控制

为解决柔性机械手非最小相位的控制问题以及克服运动中的抖振,采用积分流形和奇异摄动理论,将柔性机械手系统分解为快慢两个子系统.对于慢变子系统,设计一种基于一阶鲁棒微分估计器的二阶滑模控制策略,使其轨迹跟踪期望值;对于快变子系统,采用频率成形滤波器设计动态补偿器来抑制弹性振动,并基于线性二次型最优控制方法给出相应的最优控制规律,使系统的输出快速趋于稳定.仿真结果表明了该控制策略的有效性.     

柔性连杆机器人的多速率神经网络混合控制器设计

提出了一种用于动力学部分已知柔性连杆机器人的多速率神经网络自适应混合控制器，基于奇异摄动方法和两时标分解，柔性连杆机器人的模型分解成两个子系统；慢子系统和快子系统。这样可以根据每个分立的子系统设计系统的慢和快控规律，然后组合成一个混合控制。系统的慢控制由基于神经网络的自适应控制器实现，用于控制等效的刚性连杆机器人，而快控制用于在慢子系统建立的平衡轨迹附近稳定快子系统。     

双连杆柔性机械手负载自适应模糊滑模控制

讨论了柔性机械手末端负载变化时的控制问题.应用奇异摄动将双连杆柔性机械手系统分解为慢变、快变两个子系统.提出一种慢变子系统采用自适应模糊滑模控制、快变子系统采用最优控制的混合控制方法.仿真结果表明,该方法不仅能实现柔性机械手轨迹的快速、准确跟踪,有效的抑制弹性振动,并且对负载的变化具有强的鲁棒性.     

Synchronization and Tracking Control for Dual‐motor Driving Servo Systems with Friction Compensation

A nonsingular fast terminal sliding mode (NFTSM) controller is designed by incorporating the variable gain neural network (NN) observer, which is utilized to guarantee motor speed synchronization and load position tracking of dual‐motor driving servo systems. By designing the variable gain NN observer, the states and uncertain nonlinearities of servo systems are estimated with fast convergence rate and small steady‐state error, where the effects from external disturbance are suppressed as well. Based on the estimated states, the cross‐coupling synchronization strategy and NFTSM tracking scheme are designed to achieve the rapid speed synchronization and precise load tracking, where the NNs are introduced to approximate and compensate friction nonlinearities. In particular, a novel nonlinear synchronization factor characterizing the degree of speed synchronization is proposed to achieve switching between synchronization control and tracking control, which is proven to deal with the coupling problem of synchronization and tracking. Finally, the comparative simulations and experiments are included to verify the reliability and effectiveness.     

Online Adaptive Approximate Optimal Tracking Control with Simplified Dual Approximation Structure for Continuous-time Unknown Nonlinear Systems

This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control problem of continuous-time nonlinear systems with unknown dynamics. The requirement of the complete knowledge of system dynamics is avoided by employing an adaptive identifier in conjunction with a novel adaptive law, such that the estimated identifier weights converge to a small neighborhood of their ideal values. An adaptive steady-state controller is developed to maintain the desired tracking performance at the steady-state, and an adaptive optimal controller is designed to stabilize the tracking error dynamics in an optimal manner. For this purpose, a critic neural network (NN) is utilized to approximate the optimal value function of the Hamilton-Jacobi-Bellman (HJB) equation, which is used in the construction of the optimal controller. The learning of two NNs, i.e., the identifier NN and the critic NN, is continuous and simultaneous by means of a novel adaptive law design methodology based on the parameter estimation error. Stability of the whole system consisting of the identifier NN, the critic NN and the optimal tracking control is guaranteed using Lyapunov theory; convergence to a near-optimal control law is proved. Simulation results exemplify the effectiveness of the proposed method.      

Neural networks stabilization and disturbance attenuation for nonlinear switched impulsive systems

In this paper, we address the problem of neural networks (NNs) stabilization and disturbance rejection for a class of nonlinear switched impulsive systems. An adaptive NN feedback control scheme and an impulsive controller for output tracking error disturbance attenuation of nonlinear switched impulsive systems are given under all admissible switched strategy based on NN. The NN is used to compensate for the nonlinear uncertainties of switched impulsive systems, and the approximation error of NN is introduced to the adaptive law in order to improve the tracking attenuation quality of the switched impulsive systems. Impulsive controller is designed to attenuate effect of switching impulse. Under all admissible switching law, impulsive controller and adaptive NN feedback controller can guarantee asymptotic stability of tracking error and improve disturbance attenuation level of tracking error for the overall nonlinear switched impulsive system. Finally, a numerical example is given to demonstrate the effectiveness of the proposed control and stabilization methods.     

基于BP网络的飞轮电池电力转换控制研究

飞轮电池储能用集成电机时变非线性特点使得传统PID控制难以得到理想的控制性能,为此基于BP神经网络研究了一种新颖的飞轮电池电力转换器。该控制器结合BP神经网络自学习能力和PID控制的全局渐近稳定性能,通过神经网络在线优化调节PID参数,以实现对飞轮电池的高性能控制。其中,采用变学习速率的神经网络学习算法,学习速率随收敛过程误差的大小而自适应地进行调整,同时使用遗传算法(GA)优化得到PID参数的初始值,这可加快神经网络学习训练的收敛速度并避免陷入局部最小,进一步提高控制性能;另外,PWM采用SVPWM技术以增强能量转换效率和减小转矩脉动。数字仿真表明,基于所提出的BP-PID控制的电力转换矢量控制系统能够使飞轮电池在充放电两端都具有较快动态响应,较小超调,较高稳态精度以及较强的鲁棒性,控制效果明显比传统PID好。     

Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems

Though the control performances of the fuzzy neural network controller are acceptable in many previous published papers, the applications are only parameter learning in which the parameters of fuzzy rules are adjusted but the number of fuzzy rules should be determined by some trials. In this paper, a Takagi–Sugeno-Kang (TSK)-type self-organizing fuzzy neural network (TSK-SOFNN) is studied. The learning algorithm of the proposed TSK-SOFNN not only automatically generates and prunes the fuzzy rules of TSK-SOFNN but also adjusts the parameters of existing fuzzy rules in TSK-SOFNN. Then, an adaptive self-organizing fuzzy neural network controller (ASOFNNC) system composed of a neural controller and a smooth compensator is proposed. The neural controller using the TSK-SOFNN is designed to approximate an ideal controller, and the smooth compensator is designed to dispel the approximation error between the ideal controller and the neural controller. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived based on the Lyapunov stability theory, thus not only the system stability can be achieved but also the convergence of tracking error can be speeded up. Finally, the proposed ASOFNNC system is applied to a chaotic system. The simulation results verify the system stabilization, favorable tracking performance, and no chattering phenomena can be achieved using the proposed ASOFNNC system.     

一种稳定的机器人神经网络控制器研究

研究一种稳定的机器人神经网络（ＮＮ）控制器，提出了由神经网络控制器和监督控制器构成的控制方案，给出了控制器的设计方法及ＮＮ学习自适应律，并基于Ｌｙａｐｕｎｏｖ方法证明了控制系统的稳定性和ＮＮ参数收敛性，仿真结果表明该控制方案具有良好的鲁棒性和参数收敛性，从而证明控制器的有效性。     

Neural-network control of mobile manipulators

In this paper, a neural network (NN)-based methodology is developed for the motion control of mobile manipulators subject to kinematic constraints. The dynamics of the mobile manipulator is assumed to be completely unknown, and is identified online by the NN estimators. No preliminary learning stage of NN weights is required. The controller is capable of disturbance-rejection in the presence of unmodeled bounded disturbances. The tracking stability of the closed-loop system, the convergence of the NN weight-updating process and boundedness of NN weight estimation errors are all guaranteed. Experimental tests on a 4-DOF manipulator arm illustrate that the proposed controller significantly improves the performance in comparison with conventional robust control.     

基于遗传算法的神经网络自适应控制器的研究

提出了一种基于遗传算法的神经网络自适应控制方法。该方法是针对ＢＰ算法训练神经网络控制系统时收敛速度慢、动态特性不够理想等不足，用改进的遗传算法来优化神经网络辨识器与控制器的参数，以提高控制系统的性能，仿真实验表明该控制器对于非线性、时变、滞后等对象都具有很好的控制精度、鲁棒性和动态特性。     

Adaptive neural network control of nonlinear systems by state andoutput feedback

This paper presents a novel control method for a general class of nonlinear systems using neural networks (NNs). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a high-gain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). In addition, if the approximation accuracy of the neural networks is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussions.