基于解耦的柔性关节机械臂振动抑制方法研究

针对多自由度柔性关节机器人手臂残余振动抑制问题,对双杆机械臂关节耦合及振动抑制方法进行了研究,提出了基于解耦的柔性关节振动抑制控制算法。首先,根据拉格朗日方法建立了系统的动力学模型。然后,分析了关节间存在的相互作用力及关节间的相互耦合对机械臂振动抑制的影响。利用模糊补偿控制方法对二自由度柔性关节机械臂进行了模糊解耦,并在解耦的基础上引入零振荡整形器(ZV整形器)对解耦后的各个单关节进行进一步控制,以抑制各臂杆由于关节刚度不足导致的残余振动。最后,通过数值算例进行了仿真,验证了该算法的有效性。研究结果表明,加入解耦环节之后,两杆的相互干扰明显减弱,耦合度明显降低,使该非线性系统近似成为线性系统,再通过零振荡整形器对各关节运动加以优化,使得机械臂的残余振动得到了有效的抑制。     

Constrained incremental predictive controller design for a flexible joint robot

In this paper, an improved predictive control algorithm for controlling a typical nonlinear flexible-joint robot (FJR) with input constraint is proposed. The receding horizon algorithm, called generalized incremental predictive control (GIPC), utilizes both present and previous states rather than present states only. The GIPC algorithm includes the weighted difference of the current and the previous states and the summation of the control action increments. In order to illustrate the effectiveness of the proposed control strategy, it is implemented to the FJR and the results are compared with those of generalized predictive control (GPC). It is demonstrated that the proposed GIPC algorithm is more robust than the standard GPC method. Furthermore, the constrained GIPC algorithm using the quadratic programming removes instabilities caused by actuator saturation.     

基于Matlab的车辆主动悬挂控制研究

建立了一个具有六自由度的客车横向振动力学模型及动力学运动方程,运用线性二次型最优控制方法,用MATLAB软件编写程序并计算出最优反馈矩阵K,并用Simulink进行了仿真计算及分析.结果表明采用主动悬挂技术可以有效改善车辆的横向振动性能.     

Improved finite-control-set model predictive control for active front-end rectifiers with simplified computational approach and on-line parameter identification

In this paper, an improved finite-control-set model predictive control method is proposed for active front-end rectifiers where the computational effort and parameter mismatch problems are taken into account simultaneously. Specifically, a desired voltage vector which only requires one exploration is directly selected by using a single cost function, and the process of selection of the desired voltage vector is optimized by using a sector distribution method. Meanwhile, a model reference adaptive system-based online parameter identification approach is presented to alleviate the parameter mismatch problem. The advantages of the proposed method summarized as follows: First, the proposed algorithm reduces the eight possible voltage vectors to one. The exhaustive exploration can be avoided while the control performance is not deteriorated. Second, the proposed controller can mitigate performance degradation caused by the model parameter mismatch. Simulation results under various parameters operating conditions are presented to demonstrate the efficacy of the proposed method.     

Grouped-neural network modeling for model predictive control

A group of feed-forward neural networks (NNs), each providing the prediction of an individual process output at a future step, is used as the dynamic prediction model for the model-based predictive control (MPC) scheme in the proposed work. These NNs are parallel (independent) rather than cascaded--they are trained and implemented in parallel. Therefore, the complexity and effort in the training stage is decreased and compounded error propagation is eliminated from the prediction. A new strategy of compensating for the process-model mismatch under this grouped-NN model structure is also developed. Effectiveness of the scheme as a general nonlinear MPC is demonstrated by simulation results.     

Trajectory and vibration control of a flexible joint manipulator using interval type-2 fuzzy logic

This study presents the development of a novel interval type-2 fuzzy logic controller for real time trajectory and vibration control of a flexible joint manipulator. The controller is designed on the Mamdani based interval type-2 fuzzy logic toolbox, which is developed by the authors, using interval triangular membership functions and Karnik–Mendel type reduction algorithm. The closed-loop stability of the system is proved based on Lyapunov stability theorem. In order to observe the effectiveness and robustness of the proposed controller to variations of system parameters (change in link length and payload), the experimental results of interval type-2 and conventional type-1 fuzzy logic controllers are compared. The results show that proposed controller clearly improves the link vibration and trajectory tracking behavior of the system.     

Robust adaptive vibration control of a flexible structure

Different types of L1 adaptive control systems show that using robust theories with adaptive control approaches has produced high performance controllers. In this study, a model reference adaptive control scheme considering robust theories is used to propose a practical control system for vibration suppression of a flexible launch vehicle (FLV). In this method, control input of the system is shaped from the dynamic model of the vehicle and components of the control input are adaptively constructed by estimating the undesirable vibration frequencies. Robust stability of the adaptive vibration control system is guaranteed by using the L1 small gain theorem. Simulation results of the robust adaptive vibration control strategy confirm that the effects of vibration on the vehicle performance considerably decrease without the loss of the phase margin of the system.     

Robust-optimal active vibration controllers design for the uncertain flexible mechanical systems possessing integrity via genetic algorithm

In this paper, a robust-optimal control approach is proposed to treat the active vibration control (or active vibration suppression) problem of flexible mechanical systems under mode truncation, linear time-varying parameter uncertainties in both the controlled and residual parts, feedback gain perturbations, estimator gain perturbations and partial actuator failures. A sufficient condition is proposed to ensure that the flexible mechanical systems with time-varying structured parameter uncertainties are asymptotically stable against partial actuator failures. Systems which have such a property of keeping stable under partial actuator failures are said to possess integrity, and this is an inherent property of MIMO systems. Based on the robust stability constraint and the minimization of a defined H₂ performance, a hybrid Taguchi-genetic algorithm (HTGA) is applied to solve the optimal state feedback controller and observer design problem of uncertain flexible mechanical systems. A design example of a flexible rotor system is given to demonstrate the applicability of the proposed approach. It is shown that the proposed approach can obtain satisfactory results.     

Solution of low-dimensional constrained model predictive control problems

Large benefits are possible by utilizing the solution of the constrained optimization problem involved in model predictive control. For a special case of these problems, the solution can be obtained relatively easily from its relationship with the unconstrained optimum. In this paper, a visualization of the relationship between the constrained and unconstrained optimum is presented. Based upon this relationship, a method for finding the constrained optimum is proposed that is suitable for low-dimensional control systems. A comparison with a linear programming formulation on 2 × 2 and 3 × 3 problems shows that the computational effort can be 10–35 times lower. For such processes, the proposed approach may allow one to avail the benefits of optimization by using the small process control systems already present in many plants.     

基于压电模糊控制的2R柔性机械臂主动抑振实验研究

柔性机械臂在运动过程中不可避免产生弹性振动,对柔性机械臂的振动抑制成为重要研究课题.基于压电陶瓷的模糊控制抑振,利用电阻应变传感器检测机械臂振动信息,经动态应变仪采集到计算机系统,根据控制规律进行模糊运算,得到控制电压,再以该电压驱动压电陶瓷片,产生与振动方向相反控制力矩,抑制机械臂柔性变形.实验结果显示,模糊控制方法可靠,压电抑振效果显著.     

Iterated non-linear model predictive control based on tubes and contractive constraints

This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.     

用于大载荷主动振动控制平台的柔性铰链设计和实验研究

具体设计要求,给出了结构优化后的设计参数,确定满足一定载荷条件下柔性铰链结构形式,同时利用ANSYS软件分析了柔性铰链最大受力情况下的应力分布情况,对其进行了强度校核,最后通过实验得到柔性铰链的弯曲刚度,同理论计算值一致,进一步验证该复合式柔性铰链设计的可行性.     

Experiments of optimal delay extraction algorithm using adaptive time-delay filter for improved vibration suppression

In the previously introduced direct adaptive command shaping filter (ACSF), the time-delay value is fixed and only the magnitudes of the impulses are learned. The performance of the direct adaptive time-delay command shaping filter, however, depends on the select time-delay. In this paper, the authors introduce a new scheme to extract the optimal time-delay value for the improved vibration suppression in flexible motion system. To develop the optimal time-delay extraction scheme, the authors have analyzed the effect of the time-delay value on the performance of the direct ACSF. Based on the analysis result the authors have established a set of equations to extract the optimal time-delay toward the optimal vibration suppression performance of ACSF. Experimental results using a gantry robot with a single flexible link show the effectiveness of the proposed time-delay adaptation approach for the improved vibration suppression. This paper was presented at the 4th Asian Conference on Multibody Dynamics(ACMD2008), Jeju, Korea, August 20–23, 2008. Joo Han Park received the B.S. and M.S. degrees in Mechanical Engineering from the Kyung Hee University, Korea, in 2005 and 2007. He is currently a candidate for the PhD at Kyung Hee University, Korea. His research interests include robotics and vibration control. Sungsoo Rhim received his B.S. and M.S. degrees in Mechanical Engineering from Seoul National Univ., Korea, in 1990 and 1992 respectively. He then received his Ph.D. degree from Georgia Institute of Technology in 2000. He worked for CAMotion, Inc. in GA, USA, as Research Director from 2000 until 2003 and he is currently an Assistant Professor at the Dept. of Mechanical Engineering in Kyung Hee Univ., Korea. His research interests include system dynamics, control, robotics and vibration.     

A novel tuning strategy for multivariable model predictive control

Model predictive control (MPC) has established itself as the most popular form of advanced multivariable control in the chemical process industry. However, the benefits of this technology cannot be realized unless the controller can be operated with desirable performance for an extended period of time. The objective of this work is to present an easy-to-use and reliable tuning strategy that enables the control practitioner to maintain MPC at peak performance with minimal effort. A novel analytical expression that computes the move suppression coefficients, guidelines to select the additional adjustable parameters, and their demonstration in an overall tuning strategy are some of the significant contributions of this work. The compact form for the analytical expression that computes the move suppression coefficients is derived as a function of a first order plus dead time (FOPDT) model approximation of the process dynamics. With tuning parameters computed. MPC is then implemented in the classical fashion using an internal model formulated from step response coefficients of the actual process. Just as a FOPDT model approximation has proved a valuable tool in tuning rules such as Cohen-Coon. ITAE and IAE for PID implementations, the tuning strategy presented here is significant because it offers an analogous approach for multivariable MPC.     

Model validation and controller design for vibration suppression of flexible rotor using AMB

This paper discusses the model validation and vibration suppression of an AMB flexible rotor via additional LQG controller. The main difficulty in the vibration suppression of the flexible rotor using AMB is to realize a controller that can minimize resonance without injuring the stabilized rigid modes. In order to solve this problem, simple scheme for system modeling and controller design are developed. Firstly, the AMB flexible rotor is stabilized with a PID controller, which leads to a new stable rotor-bearing system. Then, authors propose the model validation procedure using measured open-loop frequency responses to obtain an accurate model of the AMB flexible rotor system. After that, LQG controller with modal weighting is designed to suppress resonances of the stable rotor-bearing system. Due to the poor controllability and observability of flexible modes compared to rigid ones, balancing of two Gramians is prerequisite for the fair LQG controller design. Simulation with step disturbance and experimental results of unbalance response up to 10,000 rpm verified the effectiveness of the proposed scheme.     

基于输入整形技术的柔性关节机械手控制研究

柔性机械手具有双时标特性,现有控制策略中需要对电机转角及机械臂转角的状态反馈,增加了控制策略的复杂性.建立了柔性关节机械手动力学模型,针对目前控制策略存在的算法复杂问题,提出了基于输入整形技术的柔性关节机械手运动控制策略.以电机转角作为反馈,根据机械手末端输出与目标输入之间的传递函数,设计了脉冲序列,通过与目标轨迹的卷积进行整形,可以实现轨迹跟踪.通过仿真实例表明,通过对目标轨迹的整形,能够有效减小超调和残余振动,具有一定的可行性.     

Multi-objective optimization for model predictive control

This paper presents a technique of multi-objective optimization for Model Predictive Control (MPC) where the optimization has three levels of the objective function, in order of priority: handling constraints, maximizing economics, and maintaining control. The greatest weights are assigned dynamically to control or constraint variables that are predicted to be out of their limits. The weights assigned for economics have to out-weigh those assigned for control objectives. Control variables (CV) can be controlled at fixed targets or within one- or two-sided ranges around the targets. Manipulated Variables (MV) can have assigned targets too, which may be predefined values or current actual values. This MV functionality is extremely useful when economic objectives are not defined for some or all the MVs. To achieve this complex operation, handle process outputs predicted to go out of limits, and have a guaranteed solution for any condition, the technique makes use of the priority structure, penalties on slack variables, and redefinition of the constraint and control model. An engineering implementation of this approach is shown in the MPC embedded in an industrial control system. The optimization and control of a distillation column, the standard Shell heavy oil fractionator (HOF) problem, is adequately achieved with this MPC.     

基于并联机构的主动运动隔离系统研究

研究了一种基于并联机构的主动运动隔离系统，并介绍了这种主动运动隔离系统的基本组成、工作原理以及控制流程。     

Comparison of some modeling and control issues for a flexible two link manipulator

The nonlinear dynamics of a flexible two link manipulator presents a challenging modeling and control problem with its lumped actuator dynamics coupled with distributed link dynamics. Issues related to modeling uncertainty and control robustness as well as a good understanding of the experimental issues are essential for advanced modeling and control for such systems. This paper reports some of the advanced modeling, control, and experimental issues for a flexible two link manipulator, including some novel approaches developed by the authors.