Stability and sensitivity analyses and multi-objective optimization control of the hydro-turbine generator unit

Oscillatory base manipulator (OBM) is a kind of mechanical system suffering from unexpected base oscillations. The oscillations affect tremendously system stability. Various control methods have been explored, but most of them require measurement or prediction of the oscillations. This study is concerned with a novel OBM—the autoloader, which is used in modern, autonomous main battle tanks. The base oscillation of the autoloader is hard to be obtained in practice. Furthermore, control synthesis for autoloaders is complicated with intrinsic payload uncertainty and actuator saturation. To address these issues, a novel robust control scheme is proposed in this work relying on the implicit Lyapunov method. Moreover, a novel two-degree-of-freedom manipulator operating on a vibrating base is constructed to realize the proposed control. To the best of the authors’ knowledge, this is the first study considering both control and hardware implementation for the OBM-like autoloaders. Simulation and experiment results demonstrate that, although without prior information of the base oscillation, the proposed controller exhibits good robustness against the base oscillation and payload uncertainty.

     

THE ROBUST CONTROL SCHEME FOR FREEFLOATING SPACE MANIPULATOR TO TRACK *THE DESIRED TRAJECTORY IN JOINTSPACE

The control of a free-floating space manipulator system is discussed. With the augmentation approach, the nonlinear parameterization problem of the dynamic equations of the space manipulator system is overcome. Based on the results, the robust control scheme for free-floating space manipulator with uncertain payload parameters to track the desired trajectory in jointspace is proposed, and the global convergence of the tracking is verified by using the Lyapunov method. The proposed control scheme is computationally simple, because we choose to make the controller robust to the uncertain inertial parameters rather than explicitly estimate them online. In particular, it needn't control the position and attitude of the floating base. A two-link planar space manipulator system is simulated to verify the control scheme proposed. Project supported by the National Natural Science Foundation of China (No. 19872032), Aeronautical Science Foundation, and Science Foundation of Fuzhou University.     

漂浮基空间机械臂关节轨迹跟踪的增广鲁棒控制方法

讨论了载体位置与姿态均不受控制的漂浮基空间机械臂系统的控制问题。基于增广变量法,解决了空间机械臂系统的控制方程关于系统惯性参数的非线性化问题;保持了控制方程关于系统惯性参数的线性关系。在此基础上,针对系统载荷参数不确定的情况下,提出了关节空间轨迹追踪 的增广鲁棒控制方法,并应用Lyapunov直接方法证明了 提到的控制方案能使系统满足渐近稳定性条件。通过仿真运算,证实了方法的有效性。     

存在关节力矩输出死区及外部干扰的漂浮基空间机械臂积分滑模神经网络自适应控制

探讨了载体位置和姿态都不受控时,漂浮基空间机械臂在带有关节力矩输出死区及外部干扰情况下轨迹跟踪的控制算法设计问题。死区与外部干扰影响系统的跟踪精度与稳定性。为此引入积分型切换函数,减少外部干扰引起的稳态误差,并利用径向基函数神经网络逼近动力学方程的未知部分,设计了一种积分滑模神经网络控制方案。控制算法的优点是,在死区斜率与边界参数不确定及最优逼近误差上确界未知的条件下,可以利用最优逼近误差、死区及干扰的补偿项来消除影响。李亚普诺夫稳定性分析证明了闭环系统的稳定性,且轨迹跟踪误差将收敛到0的某个小邻域内。仿真算例证实了该控制算法的有效性,实现了空间机械臂的轨迹跟踪控制。     

Neural network based robust hybrid control for robotic system: an H �� approach

A novel robust hybrid tracking control for robotic system is proposed. This hybrid control scheme combines computed torque control (CTC) with neural network, variable structure control (VSC) and nonlinear H _?? control methods. It is assumed that the nominal system of robotic system is completely known, which is controlled by using CTC method. Neural network is designed to approximate parameter uncertainties, VSC is used to eliminate the effect of approximation error, and H _?? control is employed to achieve a desired robust tracking performance. Based on Lyapunov stability theorem, it can be guaranteed that all signals in closed loop are bounded and a specified H _?? tracking performance is achieved by employing the proposed robust hybrid control. The validity of the control scheme is shown by computer simulation of a two-link robotic manipulator.     

全柔性空间机器人运动振动一体化输入受限重复学习控制

探究基座、臂、关节全柔性影响下空间机器人动力学模拟、运动控制及基座、臂、关节三重柔性振动主动抑制的问题, 设计了不基于系统模型信息的运动振动一体化输入受限重复学习控制算法. 将柔性基座与关节等效为线性弹簧与扭转弹簧, 柔性臂视为欧拉-伯努利梁模型, 利用拉格朗日方程与假设模态法建立动力学模型, 然后, 用奇异摄动理论将模型分解为包含刚性变量与臂柔性振动的慢变子系统, 包含基座、关节柔性振动的快变子系统, 并分别设计相应的子控制器, 构成了带关节柔性补偿的一体化控制算法. 针对慢变子系统, 提出输入受限重复学习控制算法, 由双曲正切函数, 饱和函数与重复学习项构成, 双曲正切函数与饱和函数实现输入力矩受限要求, 重复学习项补偿周期性系统误差, 以完成对基座姿态、关节铰周期轨迹的渐进稳定追踪. 然而, 为了同时抑制慢变子系统臂的柔性振动, 运用虚拟力的概念, 构造同时反映臂柔性振动与系统刚性运动的混合轨迹, 提出了基于虚拟力概念的输入受限重复学习控制器, 保证基座、关节轨迹精确追踪的同时, 对臂的柔性振动主动抑制. 针对快变子系统, 采用线性二次最优控制算法抑制基座与关节的柔性振动. 仿真结果表明: 控制器适用于一般柔性非线性系统, 满足输入力矩受限要求, 实现对周期信号的高精度追踪, 有效抑制基座、臂、关节的柔性振动, 证实算法的可行性.      

考虑摩擦特性的柔性关节空间机器人自适应CMAC神经网络鲁棒控制

讨论了关节摩擦力矩影响下,具有柔性铰关节的漂浮基空间机器人系统的动力学控制问题.设计了基于高斯基函数的小脑神经网络(CMAC)鲁棒控制器和摩擦力矩补偿器.用奇异摄动理论对系统的动力学模型进行快慢变子系统分解,针对快变子系统,设计力矩微分反馈控制器来抑制机械臂关节柔性引起的振动;对于慢变子系统,设计了基于自适应CMAC神...     

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。     

无刷直流力矩电机系统的自适应扰动力矩补偿研究

陀螺漂移测试转台无刷直流力矩电机系统中存在波动力矩和负载力矩振动，这严重地影响了转台速率平稳度。为提高转台速率状态位置跟踪精度，设计了一种自适应补偿方法。该方法包含一个参自适应律和等效PID控制律，它利用前馈补偿原理，来估计电机中未知参数以及波动力矩和负载力矩参数并给与补偿。该自适应补偿方法保证了闭环系统全局稳定性和对期望位置信号的渐进跟踪。仿真结果证明：该方法有效地提高了转台速率状态跟踪精度。     

TRAJECTORY TRACKING CONTROL AND VIBRATION SUPPRESSION OF SPACE-BASED ROBOT WITH ELASTIC FOUNDATION

讨论了基座存在弹性情况下,载体位置无控、姿态受控的漂浮基空间机械臂惯性空间轨迹跟踪控制及基座弹性振动主动抑制问题。由系统位置几何关系及动量守恒关系,建立了系统运动Jacobi 关系;之后利用拉格朗日方法并结合系统动量守恒关系建立了系统动力学方程。基于奇异摄动理论的两种时间尺度假设,将该方程分解为描写系统刚性运动的慢变子系统与描写系统弹性振动的快变子系统。对慢变子系统设计了基于计算力矩法的轨迹跟踪控制器;对于快变子系统则设计了线性二次最优控制方案。数值仿真证实了提出的控制方法的有效性。     

Orientation-error observer-based tracking control of nonholonomic mobile robots

In this paper, a novel trajectory tracking controller is proposed for mobile robots with unknown orientation angle by employing the orientation-error observer (OEO). In order to overcome the local stability resulted from linearization design methods, an asymptotically stable controller is designed using Lyapunov’s direct method. This method breaks down nonlinear systems into low-dimensional systems and simplifies the controller design using virtual auxiliary error function and partial Lyapunov functions. A state-feedback controller for the nonlinear error dynamics of the mobile robot is combined with an observer that estimates the orientation-error based on available trajectory information and measurement of the position coordinates. The stability of the system is easily proved via the Lyapunov theory. Abundant simulation and experiment results validate the effectiveness and superiority of the proposed control method.     

基于干扰观测器的柔性关节空间机器人退步自适应控制

研究了载体位置、姿态均不受控的情况下,系统参数不确定的柔性关节空间机器人轨迹跟踪的控制问题.结合系统动量、动量矩守恒关系,利用拉格朗日法推导出系统的动力学模型.为减小系统柔性关节对系统控制精度的影响,采用关节柔性补偿器来等效降低系统关节的柔性.再借助奇异摄动法,针对系统参数不确定的情况,设计了柔性关节空间机器人基于干扰观测器的退步自适应滑模控制方案.该方案不需要对系统惯性参数进行线性化处理,控制器结构简单,且实现了空间机器人期望轨迹的精确跟踪控制.通过平面两杆空间机器人的数值仿真证明了该方法的有效性.     

Adaptive tracking control of wheeled mobile robots with unknown longitudinal and lateral slipping parameters

An adaptive control approach is proposed for trajectory tracking of wheeled mobile robot (WMR) with unknown longitudinal and lateral slipping. A kinematic model of tracked WMR is established in this paper, in which both longitudinal and lateral slipping are considered and processed as three time-varying parameters. Sliding mode observer is then introduced to real time estimate the slip parameters online. A stable tracking control law for this robot system is proposed by backstepping method, and the asymptotic stability is guaranteed by Lyapunov theory. Meanwhile, the controller gains are determined online by poles placement method. Simulation results show the effectiveness and robustness of the proposed method.     

惯性平台稳定回路基于Backstepping的动态滑模控制(英文)

针对带不匹配不确定非线性干扰的惯性平台稳定回路跟踪控制问题,提出了基于backstepping的动态滑模控制方法。首先,建立了惯性平台稳定回路的等价模型,该模型由一个线性模型加上一个不确定的非线性函数组成。然后,基于backstepping方法设计了带渐近稳定滑模面的动态滑模控制器,解决了模型不匹配的问题,并提高了系统的鲁棒性。进而应用Lyapunov稳定性理论证明了所设计的控制器不仅能保证闭环系统的稳定性,而且可以通过选择适当的控制器参数来调整跟踪误差的收敛率。最后,仿真结果表明,基于backstepping的动态滑模控制方法与PID控制方法相比,提高了系统的跟踪精度,增强了鲁棒性。     

Dynamics and adaptive control of a dual-arm space robot with closed-loop constraints and uncertain inertial parameters简

A dynamics-based adaptive control approach is proposed for a planar dual-arm space robot in the presence of closed-loop constraints and uncertain inertial parameters of the payload. The controller is capable of controlling the po- sition and attitude of both the satellite base and the payload grasped by the manipulator end effectors. The equations of motion in reduced-order form for the constrained system are derived by incorporating the constraint equations in terms of accelerations into Kane＇s equations of the unconstrained system. Model analysis shows that the resulting equations perfectly meet the requirement of adaptive controller design. Consequently, by using an indirect approach, an adaptive control scheme is proposed to accomplish position/attitude trajectory tracking control with the uncertain parameters be- ing estimated on-line. The actuator redundancy due to the closed-loop constraints is utilized to minimize a weighted norm of the joint torques. Global asymptotic stability is proven by using Lyapunov＇s method, and simulation results are also presented to demonstrate the effectiveness of the proposed approach.     

漂浮基空间机械臂关节空间轨迹的鲁棒自适应跟踪控制

本文讨论了具有不确定系统参数的漂浮基空间机械臂系统的控制问题,由于截体的位置与姿态均不变控制,空间机械臂系统的控制方程失去了关于系统惯性参数的线性性质,给控制系统设计带来极大的困难,基于增广变量的思想,我们克服了上述难点,保持了控制方程关于系统惯性参数的线性关系;在此基础上,针对系统中机械臂参数不确定,载荷参数未知的情况,提出了关节空间轨迹追踪的鲁棒自适应控制方案,并应用Lyapunov直接方法对上述控制方案的渐近稳定性条件作了证明,提出的控制方案适用于空间站舱内机械手控制系统设计,仿真运算证实了该方法的有效性。     

A note on polynomial chaos expansions for designing a linear feedback control for nonlinear systems

This paper presents a polynomial chaos-based framework for designing optimal linear feedback control laws for nonlinear systems with stochastic parametric uncertainty. The spectral decomposition of the original stochastic dynamical model in an orthogonal polynomial basis, prescribed by the Wiener–Askey scheme, provides a deterministic model from which the optimal linear control law is designed. Optimality of the proposed control law is proved by solving the Hamilton–Jacobi–Bellman equation, and asymptotic stability of the controlled nonlinear systems is guaranteed in the Lyapunov sense. We are especially interested in synchronization of chaotic systems. For this reason, the control strategy is applied in the trajectory tracking of periodic orbits for the Duffing oscillator and the Rössler system with uncertain stochastic parameters and initial conditions. The results are verified with Monte Carlo simulations.     

单足机器人周期跳跃控制的虚拟约束方法

 研究单足机器人周期跳跃控制问题．弹簧支撑倒立摆模型可以比较准确地描述动物的跳跃行为,但无控制的自然跳跃抗干扰能力较差,一般采用轨迹跟踪控制方法实现单足机器人周期跳跃．当系统存在比较大的误差时,传统的时间轨迹跟踪控制方法存在明显的不足．引入虚拟约束技术,采用基于空间路径跟踪的控制方法可以克服时间轨迹跟踪的不足．采用点足机器人模型,并通过控制腿伸缩的方式为系统提供动力,将跳跃过程分为地面摆动和腾空飞行两个阶段,并通过起飞和着陆两个事件完成两个阶段之间的转换,整个系统模型属于欠驱动非光滑动力学系统．根据简化的动力学方程获得系统的虚拟约束解析表达式,并采用部分反馈线性化方法结合PD 控制设计系统的控制律．分析了系统的混合零动力学方程,并证明了闭环系统的临界稳定性．仿真结果表明,提出的控制方法可以实现单足机器人的周期跳跃控制,并且对外部干扰具有较强的鲁棒性．     

飞轮控制航天器的自适应姿态跟踪

本文讨论飞轮控制航天器的姿态控制问题。由于航天器在运行过程中受各种复杂环境因素的影响，难以确定其动力学参数的准确值，因此姿态控制系统必须具有自适应能力，以适应不可预计的干扰和航天器自身参数的改变。文中基于滑模控制方法提出一种改进的自适应姿态控制规律，适用于有内扰动力短存在的航天器。此控制规律可在不须提供动力学参数的情况下，使飞轮控制航天器跟踪期望的姿态变化规律以实现姿态机动。利用Lyapunov直接方法证明了控制系统的渐近稳定性。对于受随机扰动力短作用的航天器姿态跟踪的时间历程进行了数值仿真，计算结果证实上述方法的有效性。     

弹性关节空间机械臂级联智能滑模控制

谐波减速器和力矩传感器等柔性元件因其独特性能而广泛应用在空间机器人关节系统中,以获取高减速比．但同时这些柔性元件的存在为空间机械臂系统引入了关节柔性,使得对其稳定控制变得更为复杂．基于此,文中讨论了基于自适应回归小波神经网络(Self-Recurrent Wavelet Neural Networks, SRWNN)的弹性关节空间机械臂系统动力学建模及级联智能滑模控制．首先,利用级联系统理论及第二类拉格朗日方法推导出了由外环刚性臂子系统和内环关节电机转子子系统组成的系统级联动力学模型;其次,为两个子系统分别设计了内、外环自适应滑模回归小波神经网络控制．外环控制算法以期望轨迹为控制量,而其控制信号作为抑制弹性关节振动的内环控制算法的控制量,整个控制系统由内、外环控制系统叠加而成;而后,基于Lyapunov稳定性理论证明了整个控制系统的稳定性并设计了自适应回归小波神经网络的各权值参数在线学习算法．所提的控制算法有效地消除了模型不确定的影响,避免了复杂的求导计算和角加速度可测的要求,同时,控制系统设计过程中未涉及惯常奇异摄动双时标分解操作,在理论上适合任意大小的关节柔性刚度．最后,系统对比仿真试验证明了所提的级联智能控制算法优于惯常基于奇异摄动法和基于柔性铰补偿奇异摄动法的控制方案．