Adaptive input shaping for maneuvering flexible structures

In the control of flexible structures many methods are used to reduce residual vibration due to the excitation of flexible modes. Input shaping, a feed-forward method, typically convolves the input with a sequence of impulses that are independent of the system maneuver. While reducing the residual vibration, input shaping extends the duration of the maneuver command by the length of the input shaper. This paper explores the idea of adaptive input shaping which allows a fast input shaper to be used while providing robustness to parameter uncertainty by tuning the shaper to the flexible mode frequency. The adaptive input shaping method presented can adapt between maneuvers or during maneuvers. Analysis yields a large range of convergence that is verified by simulation and shows this method to be less complex than other adaptive approaches.     

Adaptive input shaping for manoeuvring flexible structures using an algebraic identification technique

Input shaping is an efficient feedforward control technique which has motivated a great number of contributions in recent years. Such a technique generates command signals with which manoeuvre flexible structures without exciting their vibration modes. This paper presents a novel adaptive input shaper based on an algebraic non-asymptotic identification. The main characteristic of the algebraic identification in comparison with other identification methods is the short time needed to obtain the system parameters without defining initial conditions. Thus, the proposed adaptive control can update the input shaper during each manoeuvre when large uncertainties are present. Simulations illustrate the performance of the proposed method.     

An impulse-time perturbation approach for enhancing the robustness of extra-insensitive input shapers

In this paper, perturbation-based extra-insensitive input shapers (PEI-ISs) are proposed to enhance the robustness of the input shaping technique. The extra-insensitive input shaper (EI-IS) has been known to be more robust than the so-called derivative input shapers such as ZVD, ZVDD, and ZVDDD shapers. However, the robustness of the known EI-IS is restricted by the symmetric property in the sensitivity curve. To address this, the PEI-IS is devised by multiplying a series of input shapers in the Laplace domain, of which the impulse times are slightly perturbed from those of the zero vibration (ZV) shaper. For a single-hump case, a closed-form solution to the PEI-IS is provided. For two- and three-hump cases, the approximate solutions are presented. The robustness is evaluated by simulations and assessed by means of the insensitivity. It will be shown that the proposed PEI-IS does improve the robustness and that it can be easily designed.     

A concurrent design of input shaping technique and a robust control for high-speed/high-precision control of a chip mounter

Input Shaping Technique (IST) and Time Delay Control (TDC), a robust feedback control law, were combined to achieve fast and precise point-to-point motion of a chip mounter. TDC was used as a feedback control to overcome disturbances and parameter variations, an IST was used to suppress the residual vibration induced in the closed loop system. TDC was designed first for the machine, and a discrete version of IST was designed on the basis of the closed-loop dynamics. In the design of TDC, a better set of gains was available thanks to the use of IST than TDC alone; in the design of IST, too, a better design was possible than IST alone. As the result of the concurrent design and synergy of the two methods, point-to-point motion could be achieved with no overshoot, the settling time of about 0.05 s and few steady-state errors to position commands of 1.5 mm. This result is far better than a conventional PID control or TDC alone could achieve, thereby showing the effectiveness of the concurrent design.     

采摘机械臂自适应输入整形控制研究

采摘机械臂在夹住柔性果茎后运输果实时,执行器末端的加减速运动使得果实在移动过程中产生摆动,易引发掉落,进而导致采摘失败.本文以单个西红柿作为负载,将果茎近似为柔性连杆.由于每一个果实的质量是不同的,因此,针对机械臂抓取可变柔性负载移动过程中的振动抑制问题,提出了自适应输入整形控制方法.当系统模型由于负载的不确定性发生变化后,传统的输入整形算法无法抑制柔性连杆移动过程中产生的振动.因此采用自适应输入整形算法,实时计算脉冲的幅值和时间.构造二次性能指标函数,通过对机械臂移动的加速度和负载的摆角实时数据进行迭代运算,达到零残余振动的目的.仿真实验结果表明,在变负载情况下,自适应输入整形算法有良好的末端振动抑制能力,获得满意的控制效果.     

Closed‐loop input shaping control of vibration in flexible structures using discrete‐time sliding mode

Input shaping technique is widely used in reducing or eliminating residual vibration of flexible structures. It is easy to implement and achieve the exact elimination of the residual vibration if the dynamics of the system are known accurately. However, it is not very robust to parameter uncertainties and external disturbances. In this paper, a closed‐loop input shaping method is developed for reducing or eliminating residual vibration of flexible structure systems with parameter uncertainties and external disturbances. The algorithm is based on input shaping control and discrete‐time sliding mode control. It is shown that the proposed scheme guarantees closed‐loop system stability, and yields good performance and robustness in the presence of parameter uncertainties and external disturbances as well. The selection of switching surface and the existence of sliding mode are two important issues, which have been addressed. The knowledge of upper bound of uncertainties is not required. Furthermore, it is shown that increasing the robustness to parameter uncertainties does not lengthen the duration of the impulse sequence. Simulation results demonstrate the efficacy of the proposed closed‐loop input shaping control scheme. Copyright © 2010 John Wiley & Sons, Ltd.     

双桅杆式起重机的时变输入整形控制方法

为了满足实际生产需求, 在工业中常使用两台或者多台起重机协作完成大型运输任务. 双桅杆式起重机负载能力强, 工作姿态灵活, 在大型建筑工地上得到了广泛应用. 然而, 现有控制方法大多针对动力学特性相对简单的双桥式起重机, 而对耦合性更强、动力学特性更复杂的双桅杆式起重机关注不足. 为解决此类系统的控制难题,本文通过分析系统的几何约束与动力学模型, 得到状态变量及其高阶导数之间的关系, 从而实现双桅杆式起重机非线性动力学模型的合理变换. 随后, 本文准确分析了吊臂俯仰角与负载姿态角之间的关系, 通过计算得到双桅杆式起重机的时变振荡周期, 设计了一种极不灵敏型输入整形器. 最后, 实验结果验证了所提时变输入整形控制方法能够实现起重机的准确定位及良好的消摆效果     

Neural observer-based adaptive compensation control for nonlinear time-varying delays systems with input constraints

This paper describes a neural network state observer-based adaptive saturation compensation control for a class of time-varying delayed nonlinear systems with input constraints. An advantage of the presented study lies in that the state estimation problem for a class of uncertain systems with time-varying state delays and input saturation nonlinearities is handled by using the NNs learning process strategy, novel type Lyapunov-Krasovskii functional and the adaptive memoryless neural network observer. Furthermore, by utilizing the property of the function tan h²(?/?)/?, NNs compensation technique and backstepping method, an adaptive output feedback controller is constructed which not only efficiently avoids the problem of controller singularity and input saturation, but also can achieve the output tracking. And the proposed approach is obtained free of any restrictive assumptions on the delayed states and Lispchitz condition for the unknown nonlinear functions. The semiglobal uniform ultimate boundedness of all signals of the closed-loop systems and the convergence of tracking error to a small neighborhood are all rigorously proven based on the NN-basis function property, Lyapunov method and sliding model theory. Finally, two examples are simulated to confirm the effectiveness and applicability of the proposed approach.     

A globally stable saturated desired compensation adaptive robust control for linear motor systems with comparative experiments

The recently proposed saturated adaptive robust controller is integrated with desired trajectory compensation to achieve global stability with much improved tracking performance. The algorithm is tested on a linear motor drive system which has limited control effort and is subject to parametric uncertainties, unmodeled nonlinearities, and external disturbances. Global stability is achieved by employing back-stepping design with bounded (virtual) control input in each step. A guaranteed transient performance and final tracking accuracy is achieved by incorporating the well-developed adaptive robust controller with effective parameter identifier. Signal noise that affects the adaptation function is alleviated by replacing the noisy velocity signal with the cleaner position feedback. Furthermore, asymptotic output tracking can be achieved when only parametric uncertainties are present.     

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.     

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed. First, robust motion and force tracking is considered, where a performance criterion containing disturbance and estimated parameter attenuations is presented. Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties. After solving a simple linear matrix inequality for gain conditions, the effect from disturbance and estimated parameter errors to motion/force errors is attenuated to an arbitrary prescribed level. Moreover, if the disturbance and estimated parameter errors are square-integrable, then an asymptotic motion tracking is achieved while the force error is as small as the inversion of control gain. Finally, numerical simulation results for a constrained planar robot illustrate the expected performance.     

Improved robust backstepping adaptive control for nonlinear discrete-time systems without overparameterization

This paper reports a robust backstepping adaptive controller for output tracking of nonlinear discrete-time systems. The result improves the previous one in Zhang, Wen, and Soh [(2001). Robust adaptive control for nonlinear discrete-time systems without overparameterization. Automatica, 37, 551-558] by removing the lower bound constraints on the system nonlinearities.     

A new approach to adaptive control: no nonlinearities

In adaptive control the goal is to design a controller to control an uncertain system whose parameters may be changing with time. Typically the controller consists of an identifier (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that good asymptotic behaviour is achieved, such as model matching (for minimum phase systems) or stability. However, a typical adaptive controller does not track time-varying parameters very well, and it is often highly nonlinear, which can result in undesirable behaviour, such as large transients or a large control signal. Furthermore, most adaptive controllers provide only asymptotic tracking, with no ability to design for a pre-specified settling time.Here we propose an alternative approach, which yields a linear periodic controller. Rather than estimating the plant or compensator parameters, instead we estimate what the control signal would be if the plant parameters were known. In this paper we argue the utility of this approach and then examine the first order case in detail, including a simulation. We also explore the benefits and limitations of the approach.     

双连杆刚柔机械臂无残余振动位置控制

针对双连杆刚柔机械臂,提出一种基于轨迹规划的无残余振动位置控制方法,在将机械臂的末端执行器从任意初始位置移动到目标位置的同时,确保系统没有残余振动产生.首先,建立系统的动力学模型,并通过分析该模型得到系统的状态约束方程.其次,基于状态约束方程,运用双向轨迹规划方法规划一条系统前向轨迹和一条系统反向轨迹.然后,利用时间倒转方法及基于遗传算法的轨迹优化方法对两条轨迹进行拼合,得到一条从系统初始状态到目标状态的期望轨迹.最后,设计轨迹跟踪控制器使系统沿期望轨迹到达目标状态,实现系统的无残余振动位置控制目标.仿真结果验证了本文所提方法的有效性.     

Robust discrete-time set-based adaptive predictive control for nonlinear systems

The problem of robust adaptive predictive control for a class of discrete-time nonlinear systems is considered. First, a parameter estimation technique, based on an uncertainty set estimation, is formulated. This technique is able to provide robust performance for nonlinear systems subject to exogenous variables. Second, an adaptive MPC is developed to use the uncertainty estimation in a framework of min–max robust control. A Lipschitz-based approach, which provides a conservative approximation for the min–max problem, is used to solve the control problem, retaining the computational complexity of nominal MPC formulations and the robustness of the min–max approach. Finally, the set-based estimation algorithm and the robust predictive controller are successfully applied in two case studies. The first one is the control of anonisothermal CSTR governed by the van de Vusse reaction. Concentration and temperature regulation is considered with the simultaneous estimation of the frequency (or pre-exponential) factors of the Arrhenius equation. In the second example, a biomedical model for chemotherapy control is simulated using control actions provided by the proposed algorithm. The methods for estimation and control were tested using different disturbances scenarios.     

Robust H-infinity control for discrete-time T-S fuzzy systems with input delay

Delay-dependent robust H-infinity control for discrete-time Takagi-Sugeno (T-S) fuzzy systems with interval time-varying input delay is considered.By constructing a new Lyapunov-Krasovskii functional and using convex combination method,a delay-dependent condition is established,under which the resulted closed-loop systems via a fuzzy state feedback are robust asymptotically stable with given H-infinity norm bound.Then,an iterative algorithm based on the modified SLPMM algorithm is proposed to solve the fuzz...     

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.     

Adaptive input shaping for single-link flexible manipulators using an algebraic identification

This work proposes an adaptive control scheme applied to single link-flexible manipulators, which combines a feedback controller of the joint angle with an adaptive input shaper updated by an algebraic non-asymptotic identification. The feedback controller is designed to guarantee trajectory tracking of the joint angle, simplifying thus the input shaper, which can be designed for the arm dynamics only. The input shaper is updated by an algebraic identification of the natural frequency corresponding to the first vibration mode of the arm. In addition, the influence of the assumptions adopted to derive the algebraic identification on the performance of the estimation is studied. Finally, the proposed adaptive control strategy is implemented in practice.     

Robust adaptive vibration control for an uncertain flexible Timoshenko robotic manipulator with input and output constraints

The problems of the constraints and the vibration suppression are investigated for the flexible Timoshenko robotic manipulator in this paper. Robust adaptive boundary control laws with the disturbance observes are designed to guarantee the convergence of the feedback flexible Timoshenko robotic manipulator system with the uncertain parameters and the states are proven to be uniform bounded. In addition, the proposed boundary controls are verified to be effectiveness by the numeral experiments.     

Finite-horizon neuro-optimal tracking control for a class of discrete-time nonlinear systems using adaptive dynamic programming approach

In this paper, a finite-horizon neuro-optimal tracking control strategy for a class of discrete-time nonlinear systems is proposed. Through system transformation, the optimal tracking problem is converted into designing a finite-horizon optimal regulator for the tracking error dynamics. Then, with convergence analysis in terms of cost function and control law, the iterative adaptive dynamic programming (ADP) algorithm via heuristic dynamic programming (HDP) technique is introduced to obtain the finite-horizon optimal tracking controller which makes the cost function close to its optimal value within an ?-error bound. Three neural networks are used as parametric structures to implement the algorithm, which aims at approximating the cost function, the control law, and the error dynamics, respectively. Two simulation examples are included to complement the theoretical discussions.