Integrated predictive iterative learning control based on updating reference trajectory for point-to-point tracking

A novel control technique is proposed by combining iterative learning control (ILC) and model predictive control (MPC) with updating-reference trajectory for point-to-point tracking problem of batch process. In this paper, a batch-to-batch updating-reference trajectory, which passes through the desired points, is firstly designed as the tracking trajectory within a batch. The updating control law consists of P-type ILC part and MPC part, in which P-type ILC part can improve the performance by learning from previous executions and MPC part is used to suppress the model perturbations and external disturbances. Convergence properties of the integrated predictive iterative learning control (IPILC) are analyzed theoretically, and the sufficient convergence conditions of output tracking error are also derived for a class of linear systems. Comparing with other point-to-point tracking control algorithms, the proposed algorithm can perform better in robustness. Furthermore, updating-reference relaxes the constraints for system outputs, and it may lead to faster convergence and more extensive range of application than those of fixed-reference control algorithms. Simulation results on typical systems show the effectiveness of the proposed algorithm.     

欠约束多机器人并联协调吊运系统运动控制

针对吊运过程中如何协调控制各机器人以实现负载高精度快速运动问题,采用了一种基于动力学模型前馈补偿+PD反馈的欠约束多机并联协调吊运系统轨迹跟踪控制方法。利用位置几何关系进行了逆运动学分析,并采用拉格朗日方程建立了系统的逆动力学模型。考虑到模型不确定性及外界扰动,采用前馈补偿+PD反馈控制方法进行轨迹跟踪控制。为了使被吊运物的轨迹跟踪控制更加快速准确,采用遗传算法对PD参数进行优化。理论分析和数值仿真结果表明,控制方法实现简单,能够快速有效地跟踪被吊运物的运动轨迹,满足被吊运物轨迹跟踪精度的要求。     

基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应控制

针对三自由度全驱动船舶存在模型不确定和未知外部环境扰动的情况,设计出一种基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应神经网络控制方法.该方法综合考虑船舶位置和速度误差之间关系设计递归滑模面,引入神经网络对船舶模型不确定部分进行逼近,设计带σ-修正泄露项的自适应律对神经网络逼近误差与外界环境扰动总和的界进行估计,并应用一种非线性增益函数构造动态面控制律,选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快、精度高,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.     

基于PD控制的机器人轨迹跟踪性能研究与比较

定义同一个Lyapunov函数，分析了基于PD的3种常用机器人轨迹跟踪算法的稳定性和鲁棒性，得到了新的结论，PD加前馈控制按指数收敛到0，PD及修改的PD加前馈控制收敛到一封闭球，增大反馈系数可使球半径任意小，基于PD的轨迹跟踪算法对模型误差及有界不确定性干扰具有鲁棒性，实验研究验证了分析结果，并对3种轨迹跟踪算法的控制性能进行比较。     

融合迭代学习与干扰观测器的压电微动平台精密运动控制

针对运动系统中常见的重复参考轨迹,尽管迭代学习控制（iterative learning control,ILC）可以通过迭代有效消除重复误差,但其对于非重复性干扰十分敏感．为实现在非重复干扰环境下压电微动平台的精密运动,提出了融合ILC与干扰观测器（disturbance observer,DOB）的控制策略．为避免复杂的迟滞建模,将迟滞非线性视为迭代过程中的重复性输入干扰．为保证控制策略的稳定性,推导其收敛条件并分析对非重复性干扰的抑制作用从而降低收敛误差．最后在压电微动平台进行了对比实验,结果表明：所提控制策略可以在无迟滞模型的前提下有效补偿迟滞非线性．针对理想环境下的5Hz、10Hz、20Hz三角波跟踪,其跟踪误差的均方根在行程的0.4%以内;而在非重复干扰环境下,跟踪误差的均方根为10.24nm,与内置的控制器、单独的反馈控制器、ILC相比,分别降低了98.73%、98.67%与88.24%．而且在干扰环境下,所提控制策略加快了ILC的收敛速度．实验结果充分验证了所提控制策略的有效性,实现了压电微动平台的精密运动．     

基于强化迭代学习的四旋翼无人机轨迹控制

为进一步提升在未知环境下四旋翼无人机轨迹的跟踪精度,提出了一种在传统反馈控制架构上增加迭代学习前馈控制器的控制方法。针对迭代学习控制（ILC）中存在的学习参数整定困难的问题,提出了一种利用强化学习（RL）对迭代学习控制器的学习参数进行整定优化的方法。首先,利用RL对迭代学习控制器的学习参数进行优化,筛选出当前环境及任务下最优的学习参数以保证迭代学习控制器的控制效果最优;其次,利用迭代学习控制器的学习能力不断迭代优化前馈输入,直至实现完美跟踪;最后,在有随机噪声存在的仿真环境中把所提出的强化迭代学习控制（RL-ILC）算法与未经参数优化的ILC方法、滑模变结构控制（SMC）方法以及比例-积分-微分（PID）控制方法进行对比实验。实验结果表明,所提算法在经过2次迭代后,总误差缩减为初始误差的0.2%,实现了快速收敛;并且与SMC控制方法及PID控制方法相比,RL-ILC算法在算法收敛后不会受噪声影响产生轨迹波动。由此可见,所提算法能够有效提高无人机轨迹跟踪的准确性和鲁棒性。     

基于干扰观测器的机械臂广义模型预测轨迹跟踪控制

为实现对多自由度机械臂关节运动精确轨迹跟踪,提出一种基于非线性干扰观测器的广义模型预测轨迹跟踪控制方法。针对机械臂轨迹跟踪运动学子系统,采用广义预测控制（Generalized Predictive Control,GPC）方法设计期望的虚拟关节角速度。对于机械臂轨迹跟踪动力学子系统,考虑机械臂的参数不确定性和未知外界扰动,利用GPC方法设计关节力矩控制输入,基于非线性干扰观测器方法实时估计和补偿系统模型中的不确定性。在李雅普诺夫稳定性理论框架下证明了机械臂关节角位置和角速度的跟踪误差最终收敛于零的小邻域。数值仿真验证了所提出控制方法的有效性和优越性。     

Active DIsturbance Rejection Control of Surface Vessels Using Composite Error Updated Extended State Observer

In this paper, a composite‐errors‐based active disturbance rejection control law is proposed for surface vessels with exogeneous disturbances. The low‐frequency disturbances from wind, wave and ocean currents are estimated by a novel composite‐errors‐based extended state observer (ESO). Since the composite errors are composed of trajectory tracking errors and estimation errors, the disturbance rejection control is feedforward‐feedback composite control. The advantages of feedforward control and feedback control are exploited to reject system disturbances. Compared with conventional ESO‐based active disturbance rejection control, smaller estimation errors and smaller tracking errors can be achieved by the proposed disturbance compensation control. The effectiveness and superiority of the designed control law are illustrated by theoretical analysis and simulation results.     

Adaptive Iterative Learning Boundary Control of a Flexible Manipulator with Guaranteed Transient Performance

This paper investigates the iterative learning control (ILC) problem of a flexible manipulator in the presence of external disturbances and output constraints. The dynamic behavior of the flexible manipulator is represented by partial differential equations (PDEs). We propose an ILC law to track the desired trajectory and suppress the vibration of the elastic deflection. The control scheme is based on a prescribed performance bound (PPB) which characterizes the maximum restrictions and convergence rate of the tracking error and deflection error. It is shown that the errors satisfy the prescribed performance bond all the time at any iterations. The established theoretical results are illustrated using numerical simulations for control performance verification.     

State Space Constrained Iterative Learning Control for Robotic Manipulators

Real‐life work operations of industrial robotic manipulators are performed within a constrained state space. Such operations most often require accurate planning and tracking a desired trajectory, where all the characteristics of the dynamic model are taken into consideration. This paper presents a general method and an efficient computational procedure for path planning with respect to state space constraints. Given a dynamic model of a robotic manipulator, the proposed solution takes into consideration the influence of all imprecisely measured model parameters, making use of iterative learning control (ILC). A major advantage of this solution is that it resolves the well‐known problem of interrupting the learning procedure due to a high transient tracking error or when the desired trajectory is planned closely to the state space boundaries. The numerical procedure elaborated here computes the robot arm motion to accurately track a desired trajectory in a constrained state space taking into consideration all the dynamic characteristics that influence the motion. Simulation results with a typical industrial robot arm demonstrate the robustness of the numerical procedure. In particular, the results extend the applicability of ILC in robot motion control and provide a means for improving the overall trajectory tracking performance of most robotic systems.     

Trajectory‐keeping in satellite formation flying via robust periodic learning control

This paper proposes an iterative learning control (ILC) scheme to ensure trajectory‐keeping in satellite formation flying. Since satellites rotate the earth periodically, position‐dependent disturbances can be considered time‐periodic disturbances. This observation motivates the idea of repetitively compensating for external disturbances such as solar radiation, magnetic field, air drag, and gravity forces in an iterative, orbit‐to‐orbit manner. It is shown that robust ILC can be effectively utilized for satellite trajectory tracking, thus enabling time‐variant formation flying between the leader‐ and follower‐satellites. The validity of the results is illustrated through computational simulations. Copyright © 2009 John Wiley & Sons, Ltd.     

Adaptive ILC algorithms of nonlinear continuous systems with non-parametric uncertainties for non-repetitive trajectory tracking

In this article, two adaptive iterative learning control (ILC) algorithms are presented for nonlinear continuous systems with non-parametric uncertainties. Unlike general ILC techniques, the proposed adaptive ILC algorithms allow that both the initial error at each iteration and the reference trajectory are iteration-varying in the ILC process, and can achieve non-repetitive trajectory tracking beyond a small initial time interval. Compared to the neural network or fuzzy system-based adaptive ILC schemes and the classical ILC methods, in which the number of iterative variables is generally larger than or equal to the number of control inputs, the first adaptive ILC algorithm proposed in this paper uses just two iterative variables, while the second even uses a single iterative variable provided that some bound information on system dynamics is known. As a result, the memory space in real-time ILC implementations is greatly reduced.     

A novel trajectory planning-based adaptive control method for 3-D overhead cranes

A three-dimensional (3-D) overhead crane is a complicated nonlinear underactuated mechanical system, for which high-speed positioning and anti-sway control are the kernel objective. Existing trajectory-based methods for 3-D overhead cranes focus on combining efficient and smooth trajectories with anti-sway tracking controllers without regard for payload motion; moreover, the exact value of plant parameters is required for accurate compensation during the control process. Motivated by these facts, we present a two-step design tracking strategy which consists of a trajectory planning stage and an adaptive tracking control design stage for 3-D overhead cranes. As shown by Lyapunov techniques and Barbalat's Lemma, the proposed controller guarantees asymptotic swing elimination and trolley positioning result in the presence of system uncertainties including unknown parameters and external disturbances. Simulation results also showed the applicability of the proposed method with good robustness against parameter uncertainties and external disturbances.     

执行器约束下基于数据驱动的参数化前馈控制器设计

针对执行器约束下非重复性点到点运动的轨迹跟踪问题, 提出了一种在执行器约束下基于数据驱动的参数化输入整形滤波器和前馈控制器优化设计算法. 首先对输入整形滤波器以及前馈控制器进行参数化, 然后在目标函数中加入控制信号变化量与控制信号能量的约束, 再采用基于数据驱动的迭代寻优算法得到最优参数, 在该参数下可以实现满足执行器约束条件下的运动控制系统轨迹最优跟踪性能. 并且由于采用了前馈参数化设计方法, 在点到点轨迹发生变化时所提出算法依然能够保持良好的轨迹跟踪性能. 仿真与实验结果表明在执行器约束下所提出算法能够实现最优点到点轨迹跟踪性能, 并且对非重复性点到点轨迹跟踪具有一定的鲁棒性.     

Decentralized adaptive control of electrically-driven manipulators

This paper presents two new decentralized strategies for motion control of uncertain electrically-driven manipulators. The first controller is an adaptive position regulation scheme which ensures semiglobal asymptotic convergence of the position error if no external disturbances are present and semiglobal convergence of the error to an arbitrarily small neighborhood of zero in the presence of bounded disturbances. It is shown that the regulation scheme can be modified to provide accurate trajectory tracking control through the introduction of adaptive feedforward elements in the control law; this second control strategy retains the simple decentralized structure of the first controller and ensures arbitrarily accurate tracking in the presence of bounded disturbances. Each of the adaptive schemes is very efficient computationally and requires virtually no information concerning either the manipulator or actuator models. The results of computer simulations and laboratory experiments with both terrestrial and space manipulators demonstrate that accurate and robust motion control can be achieved by using the proposed approach.     

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.     

An inverse-model approach to multivariable norm optimal iterative learning control with auxiliary optimisation

Motivated by the commonly encountered problem in which tracking is only required at selected intermediate points within the time interval, a general optimisation-based iterative learning control (ILC) algorithm is derived that ensures convergence of tracking errors to zero whilst simultaneously minimising a specified quadratic objective function of the input signals and chosen auxiliary (state) variables. In practice, the proposed solutions enable a repeated tracking task to be accurately completed whilst simultaneously reducing undesirable effects such as payload spillage, vibration tendencies and actuator wear. The theory is developed using the well-known norm optimal ILC (NOILC) framework, using general linear, functional operators between real Hilbert spaces. Solutions are derived using feedforward action, convergence is proved and robustness bounds are presented using both norm bounds and positivity conditions. Algorithms are specified for both continuous and discrete-time state-space representations, with the latter including application to multi-rate sampled systems. Experimental results using a robotic manipulator confirm the practical utility of the algorithms and the closeness with which observed results match theoretical predictions.     

An iterative learning controller with initial state learning

In iterative learning control (ILC), a common assumption is that the initial states in each repetitive operation should be inside a given ball centred at the desired initial states which may be unknown. This assumption is critical to the stability analysis, and the size of the ball will directly affect the final output trajectory tracking errors. In this paper, this assumption is removed by using an initial state learning scheme together with the traditional D-type ILC updating law. Both linear and nonlinear time-varying uncertain systems are investigated. Uniform bounds for the final tracking errors are obtained and these bounds are only dependent on the system uncertainties and disturbances, yet independent of the initial errors. Furthermore, the desired initial states can be identified through learning iterations     

基于大数据分析的移动对象轨迹预测方法

对移动对象的轨迹预测将在移动目标跟踪识别中具有较好的应用价值。移动对象轨迹预测的基础是移动目标运动参量的采集和估计,移动目标的运动参量信息特征规模较大,传统的单分量时间序列分析方法难以实现准确的参量估计和轨迹预测。提出一种基于大数据多传感信息融合跟踪的移动对象轨迹预测算法。首先进行移动目标对象进行轨迹跟踪的控制对象描述和约束参量分析,对轨迹预测的大规模运动参量信息进行信息融合和自正整定性控制,通过大数据分析方法实现对移动对象运动参量的准确估计和检测,由此指导移动对象轨迹的准确预测,提高预测精度。仿真结果表明,采用该算法进行移动对象的运动参量估计和轨迹预测的精度较高,自适应性能较强,稳健性较好,相关的指标性能优于传统方法。