Optimization of large-scale systems using gradient-type interaction prediction approach

In this paper, a new decomposition–coordination framework is presented for two-level optimal control of large-scale nonlinear systems. In the proposed approach, decomposition is performed by defining an interaction vector, while coordination is based on a new interaction prediction approach. In the first level, sub-problems are solved for nonlinear dynamics using a gradient method, while in the second level, the coordination is done using the gradient of coordination errors. This is in contrast to the conventional gradient-type coordination schemes, where they use the gradient of Lagrangian function. It is shown that the proposed decomposition–coordination framework considerably reduces the number of iterations. Moreover, compared to the substitution-type coordination approaches which are well known for their fast convergence rates and limited convergence regions, the proposed approach is robust with respect to parameters’ variation in addition to its good convergence rate. The robustness and the convergence rate of the proposed approach in compare to the costate prediction method are shown through simulations of a benchmark problem. The results are also compared to the ones obtained using centralized approach.     

Applications of the passive systems approach to the stability analysis of adaptive controllers for robot manipulators

Utilizing the fact that a manipulator composed of rigid links is a passive system, an approach based on the properties of interconnected passive systems is proposed for the analysis and synthesis of fixed and adaptive controllers for robot manipulators. This approach clarifies the rationale behind the various fixed and adaptive control laws synthesized using Lyapunov functions and provides new adaptation algorithms.     

机器人的鲁棒自适应分散跟踪控制

提供了一种鲁棒自适应控制策略,用于不确定性机器人的发迹跟踪,该控制器由一个ＰＤ线性反馈、一个立方项偿和非线性项构成。控制律采用分散形式,通过对二自由度机器人的仿真,证明该方法能使跟踪误差快速趋近于零。     

基于反演设计的机器人非奇异终端模糊滑模控制

针对带有建模误差和外部干扰的多关节机器人轨迹跟踪问题,根据滑模控制原理,采用非奇异终端滑模面,基于反演设计方法,设计了反演非奇异终端模糊滑模控制。并设计了模糊控制器在线估计不确定性上界值,削弱了抖动。利用李亚普诺夫定理证明了系统的稳定性,仿真结果表明方法的有效性。     

A class of adaptive regulators for robot manipulators

In this paper we propose a framework to adaptive regulator design of robot manipulators. A class of global regulators for robot is characterized for which we provide guidelines to derive adaptive versions. The class of regulators is described by control laws comprising the gradient of an artificial potential energy depending in a linear manner on the robot and payload unknown parameters plus a linear velocity feedback. We provide explicit sufficient conditions on the artificial potential energy which allow to obtain an adaptive regulator which yields a stable closed-loop system and global positioning. Using this framework we revise two previously proposed adaptive regulators and we suggest two new ones. © 1998 John Wiley & Sons, Ltd.     

A reinforcement learning approach to automatic generation control

This paper formulates the automatic generation control (AGC) problem as a stochastic multistage decision problem. A strategy for solving this new AGC problem formulation is presented by using a reinforcement learning (RL) approach. This method of obtaining an AGC controller does not depend on any knowledge of the system model and more importantly it admits considerable flexibility in defining the control objective. Two specific RL based AGC algorithms are presented. The first algorithm uses the traditional control objective of limiting area control error (ACE) excursions, where as, in the second algorithm, the controller can restore the load-generation balance by only monitoring deviation in tie line flows and system frequency and it does not need to know or estimate the composite ACE signal as is done by all current approaches. The effectiveness and versatility of the approaches has been demonstrated using a two area AGC model.     

基于模糊神经网络的机器人自学习控制

研究了一种模糊神经网络与传统PD控制相结合的机器人学习控制系统,该控制系统具有自学习、自适应、控制精度高等特点。     

多关节机器人的反演自适应模糊滑模控制

针对二关节机器人轨迹跟踪问题,设计了一种新的反演自适应模糊滑模控制器.该方法设计了反演滑模控制器和自适应模糊控制器,通过设计合适的自适应律,采用模糊控制器在线估计不确定性上界值,实现了对建模误差和干扰的自动跟踪,削弱了抖振.利用李亚普诺夫定理证明了系统的稳定性.仿真结果表明该方法的有效性.     

一种基于PSO-PID算法的分布式机器人实时控制

分析了粒子群优化(PSO)算法的原理、算法参数及其对算法性能的影响。以PSO算法为基础,提出了一种新的粒子群优化不完全微分PID算法。根据多关节机器人系统的特点,介绍了一种新的分布式机器人实时控制系统。系统采用双速率控制策略和分布式控制方式,机器人运动控制运用粒子群优化算法定时寻优PID参数,使其随着系统参数的变化而实时更新,实现最优不完全微分PID控制。实验结果表明,该系统设计科学、性能优越,新算法寻优能力强、控制效果好。     

Speed gradient adaptive control algorithms for mechanical systems

A number of recent and some new adaptive control algorithms for robot manipulators are considered from the viewpoint of the speed gradient method. Various types of algorithms are considered in a unified framework that allows useful comparisons to be made.     

带有神经网络补偿的机械手PD控制

提出了一种带有神经网络补偿的机械手PD控制策略,该方法结合PD控制器和神经网络的优势,解决了在机械手工业应用中,常规的控制策略在处理机械手耦合和非线性特性时控制效果差的问题。该方法基于常规的PD控制策略,采用径向基(radial basis function,RBF)神经网络动态补偿机械手系统的非线性,改善系统的控制性能。该文的控制策略是基于离散时间模型的,可以直接应用到控制系统中。为实现该文控制方法,开发了基于半实物仿真技术的开放式机械手平台,并且在该平台上对该方法进行了实验研究,实验结果表明：该文所提的控制策略实现简单,同时具有较高的控制精度。     

机器人的神经网络鲁棒轨迹跟踪控制

考虑了一类具有外界扰动和参数不确定性机器人系统的轨迹跟踪鲁棒控制问题。提出了两种控制方法：第一种应用输入输出线性化方法以及Lyapunov函数法,推导出鲁棒输出跟踪控制器。所获得的控制器可确保系统输出按指数规律跟踪期望输出,同时相应闭环系统的状态一致最终有界。第二种方法在第一种控制方法的基础上,利用一个RBF神经网络自适应学习系统不确定性的未知上界,有效的克服了系统不确定性的影响,提高了控制精度。     

Load levelling by control of air conditioner operation

The recent drastic increase in the number of air conditioners has caused sharp and narrow peaks in summer seasons due to the inherent temperature-sensitive characteristics. The authors propose a method to reduce the peak power demand by controlling air conditioner operations, and verify its effectiveness on clipping peak demand. However, former study has shortcomings in that no qualitative treatment of room temperature or comfort was attempted, and it did not provide a way of assessing peak demand clipping in a power system as a whole. In this paper, the authors propose a new control method that can deal with the maintaining of comfort and the reduction in power demand. Although air conditioners are used for “comfort,” this contradicts the reduction of power consumption and furthermore the concept of “comfort” is very vague. Hence, Weber-Fechner's law is utilized to quantify the pleasant feeling which is treated as a fuzzy quantity. A fuzzy coordination method is used to resolve this difference between power demand curtailment and comfort. The second part of this paper prepares an approach for assessing the amount of peak load clipping when the newly proposed control strategy is adopted in a real-size power system. A decrease in the required generation capacity is estimated provided that the loss of load probability (LOLP) is maintained at the same level before and after the application of the new control strategy. The reduction can be regarded as a dividend of load management.     

不确定机器人轨迹跟踪的自适应神经网络控制

提出了一种新的基于Lyapunov理论的自适应神经鲁棒控制方案 ,用于不确定性机器人的轨迹跟踪控制。这种控制器是一个基于神经网络动态补偿的PD反馈控制 ,可以保证机器人跟踪误差的渐近收敛性。仿真试验结果进一步证明了这种控制算法的有效性。。     

Motion of control of a multijoint robot based on a robust velocity controller

This paper proposes a simple and robust robot motion control method using a robust velocity controller. The robust velocity controller is based on H^∞ control theory, and is called H^∞ velocity controller. The H^∞ velocity controller based motion control method is completely equivalent to the robust acceleration control method using the H^∞ acceleration controller, but it has simpler structure. Therefore, the proposed system can realize a fine robot motion control easily. To confirm the validity of the proposed method, this paper realizes the hybrid control of position and force for a multijoint robot manipulator. Further, the simple realization of hybrid control is proposed considering the attitude of the robot manipulator. This system achieves hybrid control of position and force of the robot manipulator while maintaining a perpendicular attitude to the target environment. The experimental results in this paper show that the proposed system has the desired force and position response to the target environment. © 1997 Scripta Technica, Inc. Electr Eng Jpn, 118 (4): 58–69, 1997     

多端柔性直流输电系统的改进下垂控制策略

电压源型变流器技术发展日臻成熟,电压等级及容量不断提高,已成功用于柔性直流输电系统,并展现出高电压、大容量和多端柔性直流输电系统应用的发展态势。本文结合世界首个多端柔性直流输电工程,针对多端柔性直流输电系统应用中存在的强非线性和多端协调等技术难点,提出采用改进下垂控制方法,即通过多个换流站同时采用定电压控制方式运行,暂态过程中,采用定电压控制的换流站退出时系统仍能正常运行,保持直流网络功率平衡。通过检测直流网络电压和动态改变平滑切换控制器输出值,即由切换控制器选择定功率控制输出有效或定电压控制器输出有效,在无需站间通信情况下,实现定电压控制换流站和定功率控制换流站之间的相互转换,相对于传统采用单点电压控制的多端系统,具有较好的灵活性。此外,针对采用定电压控制的换流站完全退出的极端工况,设计反步电压控制器,有效地减小了暂态过电压程度,与下垂控制策略结合,能较好地实现系统直流电压的稳定控制,充分发挥其多端协调功能,提高系统抗干扰性能。仿真结果证明了所采用方法的正确性和有效性。     

Iterative learning control for robotic manipulators: A bounded‐error algorithm

This paper presents a model‐based nonlinear iterative learning control (NILC) for nonlinear multiple‐input and multiple‐output mechanical systems of robotic manipulators. An algorithm of a new strategy for the NILC implementation is proposed. This algorithm ensures that trajectory‐tracking errors of the proposed NILC, when implemented, are bounded by a given error norm bound. Both standard and bounded‐error learning control laws with feedback controllers attached are considered. The NILC synthesis is based on a dynamic model of a six degrees of freedom robotic manipulator. The dynamic model includes viscous and Coulomb friction and input generalized torques are bounded. With respect to the bounded‐error and standard learning processes applied to a virtual PUMA 560 robot (Unimation, Inc. Danburry, CT, USA), simulation results are presented in order to verify maximal tracking errors, convergence and applicability of the proposed learning control. Copyright © 2013 John Wiley & Sons, Ltd.     

基于双电压环补偿的直流微网下垂控制

针对直流微网传统下垂控制中分布式电源(distributed generation,DG)输出功率分配精度和电压维持额定值间的矛盾,提出了一种基于双电压环补偿的直流微网下垂控制方法.该方法基于集中式二级控制和对等下垂控制策略,在二级控制中引入双电压环控制,其中,第一电压环保证负载功率的高精度分配,第二电压环抬高系统电压...     

Biped robot control strategy and open-closed-loop iterative learning control

Master-slaver dual-leg coordination control was proposed for the biped robot with heterogeneous legs (BRHL), in order to reduce gait planning and to get a good tracking performance. The key to coordination is gait trajectory tracking control. Bionic knee joint with closed-chain 4links makes robot walking more humanlike, but the model is complex and tracking of the bionic leg to the artificial leg is more difficult. P-type open-closed-loop iterative learning control (ILC) is not based on model parameters and has advantages in both open-loop and closed-loop ILC; so this paper proposes a complex robot gait trajectory tracking. The convergence is proved by using functional analysis and the stability condition is given. A tracking simulation based on the virtual prototype pe was done. The results show that this control strategy and algorithm are effective and robust, and the convergence speed is better than separate open-loop or closed-loop ILC. Translated from Journal of Northeastern University (Natural Science), 2005, 26(8): 722–725 [译自: 东北大学学报(自然科学版)]