运输机地面转弯轨迹跟踪滑模控制器设计及仿真

大型运输机飞行控制包括较宽范围的姿态机动、轨迹跟踪。通道间高度耦合、外界条件干扰等条件使得轨迹跟踪控制设计变得较为复杂。为此,提出了一种鲁棒解耦的多变量跟踪控制方法——滑模控制方法,对运输机地面转弯轨迹进行了精确跟踪。运输机地面转弯轨迹运动学模型是一个多输入非线性系统,所以滑模控制中切换函数设计是很重要的。给出了Backstepping方法设计切换函数,通过对实例的计算仿真,滑膜控制对运输机轨迹跟踪具有较高的准确度。     

Online constrained optimization based adaptive robust control of a class of MIMO nonlinear systems with matched uncertainties and input/state constraints

A performance oriented two-loop control approach is proposed for a class of multiple-input–multiple-output (MIMO) systems with input saturation, state constraints, matched parametric uncertainties and input disturbances. In the inner loop, a constrained adaptive robust control (ARC) law is synthesized to achieve the required robust tracking performances with respect to on-line replanned trajectory in the presence of input saturation and various types of matched uncertainties. In the outer loop, a replanned trajectory is generated by solving a constrained optimization algorithm online to minimize the converging time of the overall system response to the desired trajectory while not violating various constraints. Interaction of the two loops is explicitly characterized by a set of inequalities that the design variables of each loop have to satisfy. It is theoretically shown that the resulting closed-loop system can track feasible desired trajectories with a guaranteed converging time and steady-state tracking accuracy without violating the state constraints. Since the system in study is most appropriate to describe the dynamics of the robotic systems, the control of a two-axis planar robotic manipulator is used as an application example. Comparative simulation results demonstrate the advantage of the proposed approach over the traditional approaches in practical applications.     

多移动机器人轨迹跟踪控制系统设计与实现

针对轮式移动机器人的轨迹跟踪控制问题,在分析了机器人运动学模型的基础上,构建多机器人的领航-追随模型;采用跟踪微分器在输入输出两端安排过渡过程,设计了一种基于多变量解耦的非线性PID轨迹跟踪控制器;搭建以Arduino Mega 1280控制板为核心的移动机器人实验平台,采用速度PID控制器以满足机器人驱动电机的实时调速要求,基于ROS提出一种结构化和模块化的多机器人控制系统;在此基础上进行实验,并将实验结果与传统PID方法控制的实验结果进行对比;实验结果验证了文章所提算法的有效性,控制器易于实现且具有一定的鲁棒性。     

机械手的模糊逆模型鲁棒控制

提出一种基于模糊聚类和滑动模控制的模糊逆模型控制方法,并将其应用于动力学 方程未知的机械手轨迹控制.首先,采用C均值聚类算法构造两关节机械手的高木-关野 (T-S)模糊模型,并由此构造模糊系统的逆模型.然后,在提出的模糊逆模型控制结构中, 离散时间滑动模控制和时延控制(TDC)用于补偿模糊建模误差和外扰动,保证系统的全局 稳定性并改进其动态和稳态性能.系统的稳定性和轨迹误差的收敛性可以通过稳定性定理来 证明.最后,以两关节机械手的轨迹跟随控制为例,揭示了该设计方法的控制性能.     

板球系统的间接模糊自适应控制

针对具有强耦合、不确定摩擦力的多变量非线性板球系统,利用Lyapunov稳定理论,设计一种间接模糊自适应控制器。该控制器可以在确保系统变量在有限范围内变动的同时保持收敛性,并且在系统的增益矩阵不可逆时,使得板球系统稳定并跟踪误差收敛到零邻域内。控制器是由监督、间接模糊自适应和自适应补偿3种控制算法结合的。仿真实验表明,所提出的控制方法能够确保板球系统跟踪控制的稳定性和收敛性。     

Optimal Decoupling Control Method and Its Application to a Ball Mill Coal-pulverizing System

The conventional optimal tracking control method cannot realize decoupling control of linear systems with a strong coupling property. To solve this problem, in this paper, an optimal decoupling control method is proposed, which can simultaneously provide optimal performance. The optimal decoupling controller is composed of an inner-loop decoupling controller and an outer-loop optimal tracking controller. First, by introducing one virtual control variable, the original differential equation on state is converted to a generalized system on output. Then, by introducing the other virtual control variable, and viewing the coupling terms as the measurable disturbances, the generalized system is open-loop decoupled. Finally, for the decoupled system, the optimal tracking control method is used. It is proved that the decoupling control is optimal for a certain performance index. Simulations on a ball mill coal-pulverizing system are conducted. The results show the effectiveness and superiority of the proposed method as compared with the conventional optimal quadratic tracking (LQT) control method.      

共轴式无人直升机建模与鲁棒跟踪控制

针对共轴式无人直升机非线性、强耦合的动力学特性,本文提出了一种基于动态反馈线性化方法的鲁棒跟踪控制策略.首先根据叶素理论、Pitt-Peters动态入流模型、上下旋翼气动干扰分析建立了共轴式无人直升机的数学模型.然后对于高度-姿态子系统,通过扩展状态变量对其进行了动态反馈线性化,分析了零动态特性.根据内环期望跟踪特性对解耦后的子系统进行极点配置.通过设计鲁棒补偿器实现了对高度与姿态指令的鲁棒跟踪.在此基础上,针对水平面内的位置子系统设计了外环比例微分(proportional-derivative,PD)控制器以实现位置跟踪.最后,通过内环跟踪仿真验证了反馈线性化方法良好的解耦特性,通过干扰条件下的轨迹跟踪仿真验证了所设计控制器具有较好的控制性能与鲁棒性.     

A performance oriented multi-loop constrained adaptive robust tracking control of one-degree-of-freedom mechanical systems: Theory and experiments

A performance oriented multi-loop approach to the adaptive robust tracking control of one-degree-of-freedom mechanical systems with input saturation, state constraints, parametric uncertainties and input disturbances is presented. The control system contains three loops. In the outer loop, constrained optimization algorithms are developed to generate a replanned trajectory on-line at a low sampling rate so that the converging speed of the overall system response to the desired target is maximized while not causing input saturation and the violation of state constraints. In the inner loop, a constrained adaptive robust control (ARC) law is synthesized and implemented at high sampling rate to achieve the required robust tracking performances with respect to the replanned trajectory even with various types of uncertainties and input saturation. In the middle loop, a set-membership identification (SMI) algorithm is implemented to obtain a tighter estimate of the upper bound of the inertia so that more aggressive replanned trajectory could be used to further improve the overall system response speed. Interaction of the three loops is explicitly characterized by a set of inequalities that the design variables of each loop have to satisfy. It is theoretically shown that the resulting closed-loop system can track feasible desired trajectories with a guaranteed converging time and steady-state tracking accuracy without violating the state constraints. Experiments have been carried out on a linear motor driven industrial positioning system to compare the proposed multi-loop constrained ARC algorithm with some of the traditional control algorithms. Comparative experimental results obtained confirm the superior performance of the proposed algorithm over existing ones.     

基于滑模观测器的直线伺服系统反馈线性化速度跟踪控制

在许多高速、高精的直线伺服系统中,要求能实现对速度的快速精确跟踪,但其模型的非线性和变量间的耦合给控制带来难度.对高速、高精速度跟踪控制中,电流和速度的变化过程在时间尺度上相对接近,不能简单地采用磁场定向矢量控制方法实现静态解耦,否则电流和速度间的非线性耦合将破坏速度跟踪品质.采用状态反馈线性化方法来实现永磁直线同步电动机(PMLSM)模型的精确线性化和动态解耦.利用非线性坐标变换和非线性反馈将系统解耦成独立的线性电流子系统和速度子系统.通过扩展滑模观测器来实现对所需要的动子速度、加速度和负载扰动的鲁棒观测.并利用李雅普诺夫理论对由反馈线性化和滑模观测器构成的非线性闭环系统的稳定性进行了证明.仿真结果表明该方案使PMLSM伺服系统具有良好的鲁棒速度跟踪性能.     

An adaptive neural network switching control approach of robotic manipulators for trajectory tracking

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.     

基于RBF神经网络的作业型AUV自适应终端滑模控制方法及实验研究

研究了作业型AUV （自主水下机器人）的轨迹跟踪控制问题.实际作业中,水下机械手展开作业过程将引起AUV动力学性能变化,进而影响AUV轨迹跟踪控制;并且水流环境干扰亦将影响AUV轨迹跟踪控制.针对上述AUV轨迹跟踪控制问题,提出一种基于RBF （径向基函数）神经网络的AUV自适应终端滑模运动控制方法.该方法在李亚普诺夫稳定性理论框架下,采用RBF网络对机械手展开引起的AUV动力学性能变化和水流环境干扰进行在线逼近,并结合自适应终端滑模控制器对神经网络权值和AUV控制参数进行自适应在线调节.通过李亚普诺夫稳定性理论,证明AUV系统轨迹跟踪误差一致稳定有界.针对滑模控制项引起的控制量抖振问题,提出一种变滑模增益的饱和连续函数滑模抖振降低方法,以降低滑模控制量抖振.通过AUV实验样机的艏向和垂向的轨迹跟踪实验,验证了本文AUV系统控制方法和滑模降抖振方法的有效性.     

基于模糊不确定观测器的四旋翼飞行器自适应动态面轨迹跟踪控制

针对具有未知外界扰动和系统不确定性的四旋翼飞行器,提出了一种基于模糊不确定观测器（Fuzzy uncertainty observer,FUO）的自适应动态面轨迹跟踪控制方法.通过将四旋翼飞行器系统分解为位置、姿态角和角速率三个动态子系统,使得各子系统虚拟控制器能够充分考虑欠驱动约束;采用一阶低通滤波器重构虚拟控制信号及其一阶导数,实现四旋翼跟踪控制设计的迭代解耦;设计了一种模糊不确定观测器,用以估计和补偿未知外界扰动与系统不确定性,从而确保闭环系统的稳定性和跟踪误差与其他系统信号的一致有界性.仿真研究验证了所提出的控制方法的有效性和优越性.     

Neurodynamic programming and tracking control scheme of constrained-input systems via a novel event-triggered PI algorithm

In this paper, a new event-triggered approach for neurodynamic programming and optimal tracking control problem of constrained-input systems is proposed, where the desired trajectory can be generated as a large class of useful command trajectories. Firstly, the complex tracking problem is converted to a stabilizing control optimization problem by reconstructing a novel augmented tracking system with discounted performance index. Secondly, instead of the conventional time-driven control, an event-triggered policy iteration (PI) algorithm is designed to drive the system dynamic to track the reference trajectory, which requires less computation and fewer transmissions during the solving. Thirdly, the novel tracking control can be bounded as desired, which overcomes the unconstrained steady-state control from the general adaptive dynamic programming based tracking solution. Moreover, only critic neural network is used in the implementation of iteration learning, which simplifies the actor-critic architecture and reduces computational load. And by the means of Lyapunov method, the ultimately boundedness of the tracking system under the event-triggered PI algorithm is proved. Finally, the developed approach is applied to track a sinusoidal waveform and a periodic rectangular step signal in the simulations, where the effectiveness is also demonstrated.     

移动机器人轨迹跟踪快速终端滑模自抗扰控制

针对移动机器人动力学模型难以精确建立、运动过程中各种干扰对高精度轨迹跟踪造成偏航等问题,构造出一种快速终端滑模自抗扰控制器,实现了高速高精度轨迹跟踪控制目标.首先建立非完整移动机器人的干扰控制模型;然后运用扩张状态观测器实时监测系统未建模动态与各种干扰;同时将扩张状态量和系统反馈量作为快速终端滑模算法的系统变量;最后设...     

空速/高度精确解耦跟踪控制研究及仿真

飞行器空速／高度的精确解耦跟踪,控制本质上是非最小相位输出精确解耦跟踪控制问题,由于存在内动态不稳定性,经典逆系统方法无法使用。将稳定逆办法与滑模控制相结合来解决存在的问题。首先针对稳定逆非因果性带来的问题,给出了一种稳定逆因果近似解的求法,经计算证明了近似结果误差的渐近收敛特性;然后利用稳定逆困果近似解将输出跟踪问题转化为不确定系统的鲁棒镇定问题,并对滑模控制和干扰观测器设计了相应的反馈镇定控制器。经过仿真表明综合稳定逆因果近似解和滑模控制的方案既发挥了逆系统方法的动态解耦能力,又利用了滑模控制的鲁棒性实现飞行器空速／高度精确解耦跟踪控制,也可以很好的兼顾控制器的鲁棒性和抗干扰性。     

下肢康复机器人离散自适应控制的研究

康复训练是脑卒中、脊髓损伤患者主要的治疗手段.机器人辅助步行训练是近年来广泛应用的下肢康复训练方式.针对具有步态自适应功能的下肢康复机器人,提出了一种柔顺协调控制方法.基于非线性逆系统解耦理论,系统分解为几个独立的二阶积分系统.在解耦子系统中,采用阻抗和自适应控制来进行步态轨迹的规划和跟踪.最后,在Matlab-Adams联合仿真系统中对设计的控制器进行仿真并验证算法的正确性.     

Aggressive Longitudinal Aircraft Path Tracking Using Nonlinear Control

We study the problem of converting a trajectory tracking controller to a path tracking controller for a nonlinear non-minimum phase longitudinal aircraft model. The solution of the trajectory tracking problem is based on the requirement that the aircraft follows a given time parameterized trajectory in inertial frame. In this paper we introduce an alternative nonlinear control design approach called path tracking control. The path tracking approach is based on designing a nonlinear state feedback controller that maintains a desired speed along a desired path with closed loop stability. This design approach is different from the trajectory tracking approach where aircraft speed and position are regulated along the desired path. The path tracking controller regulates the position errors transverse to the desired path but it does not regulate the position error along the desired path. First, a trajectory tracking controller, consisting of feedforward and static state feedback, is designed to guarantee uniform asymptotic trajectory tracking. The feedforward is determined by solving a stable noncausal inversion problem. Constant feedback gains are determined based on LQR with singular perturbation approach. A path tracking controller is then obtained from the trajectory tracking controller by introducing a suitable state projection.     

A unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes

In this paper, a unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes is proposed. A feasible reference trajectory taking constraints into consideration is first generated offline by the symplectic pseudospectral optimal control method. Then, a trajectory tracking model predictive controller also based on the symplectic pseudospectral method is developed to track the reference trajectory. At each sampling instant, the trajectory tracking controller works by solving an open‐loop optimal control problem where linearized system dynamics is used instead to improve the computational efficiency. Since the symplectic pseudospectral optimal control method is the core algorithm for both offline trajectory planning and online trajectory tracking, constraints on state variables and control inputs can be easily imposed and hence theoretically guaranteed in solutions. By selecting proper weighted matrices on tracking error and control, the developed controller could achieve control objectives in both accurate trolley positioning and fast suppressing of residual swing angles. Simulations for 3D overhead crane systems in the presence of perturbations in initial conditions, an abrupt variation of system parameter, and various external disturbances demonstrate that the developed controller is robust and of excellent control performance.     

Dynamic adaptive trajectory tracking control of nonholonomic mobile robots using multiple models approach

This paper presents an approach for the trajectory tracking control of nonholonomic wheeled mobile robots (WMR) by combining one of the existing adaptive control methods and multiple identification models. The overall system includes two types of controllers in the control scheme. A kinematic controller developed by using kinematic model produces the required linear and angular velocities of the robot for tracking a reference trajectory. These required velocities are used to calculate the torques using an adaptive dynamic controller with multiple models. The proposed method uses the multiple models of the WMR for the identification of the dynamic parameters and performs switching between the given models. The models used in the identification are identical, except for the initial estimates of the parameters. By using an adaptive dynamic controller with multiple models of the WMR, enhancement in transient response is obtained. Stability analysis of the overall system is given, and simulation results are presented to demonstrate the effective performance of the adaptive control by using multiple models approach.     

Obstacle avoidance tracking control with antiswing and tracking errors constraint for underactuated automated lifting robots with load hoisting/lowering

The existing automated lifting robot technology focuses merely on motion control and ignores the surrounding environment. In practice, obstacles inevitably exist in the movement path of the automated lifting robot, which affects construction safety. Furthermore, due to the underactuated characteristics of the automated lifting robot, the load can be difficult to control when it swings violently, which undoubtedly poses huge challenges to obstacle avoidance trajectory planning and controller design. In this paper, an obstacle avoidance trajectory and its tracking controller with antiswing and tracking errors constraint are proposed. To ensure accurate load positioning and effective obstacle avoidance, the proposed control method introduces a four-segment polynomial trajectory interpolation curve to construct an obstacle avoidance trajectory based on analyzing the geometric relationship between variables. To improve the transient coupling control performance of the system, combined with the passive analysis of the automated lifting robot system, this method constructs a potential function that limits the tracking error and a coupling signal that enhances the coupling relationship between the system variables. Barbalat's lemma and Lyapunov techniques are used to analyze the stability of the system. Simulation and experimental results show that the proposed control method can significantly suppress or even eliminate load oscillation, accurately locate the load, avoid obstacles, improve the safety and efficiency of the working automated lifting robot, and have strong robustness to changes in system parameters and the addition of external disturbances.