基于模糊逻辑的机械手智能力/位控制

提出了一种面向机器人参与机械加工的智能柔顺控制策略,通过一个模糊阻抗控制器和一个速度控制器,该策略被应用于机器人切除毛刺操作中。模糊阻抗控制器可以根据机器人末端实际位置和期望位置的误差调整阻抗参数,从而提高控制性能和加工效果。而模糊速度控制器被用来调节工具进给速度,从而彻底清除较大的毛刺,避免损害工件和刀具,得到理想的加工表面。为了验证控制策略的柔顺控制能力,以一个三关节机器人进行了计算机仿真,仿真结果表明提出的策略能够很好提高控制性能,获得理想的加工效果。     

Decentralized robust guaranteed cost control for a class of interconnected singular large-scale systems with time-delay and parameter uncertainty

The decentralized robust guaranteed cost control problem is studied for a class of interconnected singular large-scale systems with time-delay and norm-bounded time-invariant parameter uncertainty under a given quadratic cost performance function. The problem that is addressed in this study is to design a decentralized robust guaranteed cost state feedback controller such that the closed-loop system is not only regular, impulse-free and stable, but also guarantees an adequate level of performance for all admissible uncertainties. A sufficient condition for the existence of the decentralized robust guaranteed cost state feedback controllers is proposed in terms of a linear matrix inequality (LMI) via LMI approach. When this condition is feasible, the desired state feedback decentralized robust guaranteed cost controller gain matrices can be obtained. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed approach.     

飞行器抗饱和鲁棒自适应非线性模型预测控制

针对巡航飞行器同时存在较大外部干扰和模型参数不确定性时自适应预测控制性能下降的问题,设计了带有模糊干扰观测器(fuzzy disturbance observer, FDO)补偿的鲁棒自适应非线性模型预测控制(robust adaptive nonlinear model predictive control, RANMPC)方法(简记为FDO-RANMPC方法)。首先,利用具有未知参数限制条件的递推最小二乘方法在线辨识模型参数;其次,利用模糊干扰观测器输出值抵消幅值较大的复合干扰,再利用Tube鲁棒预测控制策略设计了飞行器底层姿态系统具有稳定保障的FDO-RANMPC控制器;最后,在考虑复合不确定性情况下对指令姿态角跟踪的仿真中验证了控制器的有效性及其渐进稳定性。     

Prediction-based protocol for mobile target tracking in wireless sensor networks

Remote tracking for mobile targets is one of the most important applications in wireless sensor networks (WSNs). A target tracking protoco–exponential distributed predictive tracking (EDPT) is proposed. To reduce energy waste and response time, an improved predictive algorithm–exponential smoothing predictive algorithm (ESPA) is presented. With the aid of an additive proportion and differential (PD) controller, ESPA decreases the system predictive delay effectively. As a recovery mechanism, an optimal searching radius (OSR) algorithm is applied to calculate the optimal radius of the recovery zone. The simulation results validate that the proposed EDPT protocol performes better in terms of track failed ratio, energy waste ratio and enlarged sensing nodes ratio, respectively.     

Predictive Control with Velocity Observer for Cushion Robot Based on PSO for Path Planning

This paper proposes a novel model predictive control method with velocity estimation simultaneously constraining trajectory and velocity tracking errors for a cushion robot. The authors investigated a path planning method using improved particle swarm optimization(PSO) combined with Dijkstra's algorithm and obtained a real-time desired optimal motion path for obstacle avoidance.The authors designed a velocity observer to estimate the unmeasurable speed, while the asymptotic stability of the observer error system was established. A predictive controller with error-constrained performance was derived by solving a quadratic programming problem with incremental control. Simulation and experimental results confirm the effectiveness of the proposed method and verify that the error constraints adopted in the cushion robot provide safe motion while avoiding obstacles.     

Robust H∞ controller design for sampled-data systems with parametric uncertainties

This article investigates the problem of robust H∞ controller design for sampled-data systems with time-varying norm-bounded parameter uncertainties in the state matrices. Attention is focused on the design of a causal sampled-data controller, which guarantees the asymptotical stability of the closed-loop system and reduces the effect of the disturbance input on the controlled output to a prescribed H∞ performance bound for all admissible uncertainties. Sufficient condition for the solvability of the problem is established in terms of linear matrix inequalities (LMIs). It is shown that the desired H∞ controller can be constructed by solving certain LMIs. An illustrative example is given to demonstrate the effectiveness of the proposed method.     

Robust H∞ controller design for sampled-data systems with parametric uncertainties

This article investigates the problem of robust H∞ controller design for sampled-data systems with time-varying norm-bounded parameter uncertainties in the state matrices. Attention is focused on the design of a causal sampled-data controller, which guarantees the asymptotical stability of the closed-loop system and reduces the effect of the disturbance input on the controlled output to a prescribed H∞ performance bound for all admissible uncertainties. Sufficient condition for the solvability of the problem is established in terms of linear matrix inequalities (LMIs). It is shown that the desired H∞ controller can be constructed by solving certain LMIs. An illustrative example is given to demonstrate the effectiveness of the proposed method.     

参数不确定的广义T-S模糊系统的最优保成本控制

研究参数不确定的广义T-S模糊系统的最优保成本控制问题.对给定系统所容许的所有不确定参数,设计了状态反馈最优保成本控制器,使得闭环系统不仅渐近稳定而且具有最小性能指标上界.基于LMI处理方法,给出了该控制器存在的充分条件.通过矩阵分解把广义系统的非严格矩阵不等式约束转化为严格矩阵不等式,从而可以像正常系统那样使用LMI工具箱求解控制器.算例显示了所给方法的简洁、有效;所得的性能指标的不同,也说明了最优保成本控制器的良好性能.     

时变非线性系统直接自适应迭代学习控制

针对一类含有非周期时变参数化不确定性的非线性系统,设计了一种新的迭代神经网络估计器,解决了非周期时变不确定性带来的设计难题。用迭代神经网络直接对期望控制量进行整体逼近,利用Lyapunov稳定性理论和自适应迭代学习控制技术设计了控制器,并进行稳定性分析,证明了系统所有状态量有界,且输出量将收敛至期望轨迹的一个邻域内。仿真结果验证了控制器设计方案的正确性。     

QFT control based on zero phase error compensation for flight simulator

To improve the robustness of high-precision servo systems, quantitative feedback theory (QFT) which aims to achieve a desired robust design over a specified region of plant uncertainty is proposed. The robust design problem can be solved using QFT but it fails to guarantee a high precision tracking. This problem is solved by a robust digital QFT control scheme based on zero phase error (ZPE) feed forward compensation. This scheme consists of two parts: a QFT controller in the closed-loop system and a ZPE feed-forward compensator. Digital QFT controller is designed to overcome the uncertainties in the system. Digital ZPE feed forward controller is used to improve the tracking precision. Simulation and real-time examples for flight simulator servo system indicate that this control scheme can guarantee both high robust performance and high position tracking precision.     

具有饱和执行器跳变系统的鲁棒模型预测控制

针对一类具有饱和执行器的不确定离散Markov跳变系统,提出鲁棒模型预测控制器设计方法。为便于工程应用,考虑被控跳变系统各模态下的动态系统参数以及各模态间的跳变转移模态均存在不确定性的情形,这些不确定性均以凸多面体的形式给出。预测控制器的求解通过在每一个采样时刻优化无穷时域的最坏二次性能指标实现,该预测控制序列在每个采样时刻表现为具有饱和特性的状态反馈控制律,从而使得所形成的闭环系统鲁棒均方稳定。为方便求解,将控制器的求解转化为以线性矩阵不等式形式给出的正半定规划(SDP)问题。数值示例验证了所提方法的有效性。     

时滞时变AUV的模糊变结构控制

针对自主水下航行器(autonomous underwater vehicle,AUV)具有时变的水动力学系数的现象,为了实现其鲁棒控制,设计了一个模糊变结构控制器.该控制器以变结构控制的切换函数及其变化率为模糊控制器的输入,以变结构控制律的变化率为模糊控制器的输出.为了提高控制器的性能,引入了压缩扩张隶属度函数.通过将该控制器和均分隶属度函数模糊变结构控制器、准滑模控制器的仿真对比,证明了设计的控制器具有更好的性能.数值仿真表明,该控制器能很好地实现对具有控制输入时滞和时变水动力系数AUV的深度控制,且稳定后基本无抖振现象.     

无人机鲁棒轨迹线性化控制航迹跟踪设计

研究一种鲁棒轨迹线性化控制方法并将其应用于无人机(unmanned aerial vehicle, UAV)航迹跟踪控制设计。通过理论分析指明传统轨迹线性化控制方法对系统中的不确定性存在鲁棒性不足的问题,采用改进隐层自适应神经网络对不确定性进行补偿,并利用Lyapunov理论证明了跟踪误差的有界性,最后将该方法应用到无人机三维航迹跟踪控制中。仿真结果表明,当参数摄动在20%时,该控制方法仍能使UAV很好地跟踪理想航迹,从而验证了该方法的有效性。     

Adaptive functional link network control of near-space vehicles with dynamical uncertainties

The control law design for a near-space hypersonic vehicle (NHV) is highly challenging due to its inherent nonlinearity, plant uncertainties and sensitivity to disturbances. This paper presents a novel functional link network (FLN) control method for an NHV with dynamical thrust and parameter uncertainties. The approach devises a new partially-feedback-functional-link-network (PFFLN) adaptive law and combines it with the nonlinear generalized predictive control (NGPC) algorithm. The PFFLN is employed for approximating uncertainties in flight. Its weights are online tuned based on Lyapunov stability theorem for the first time. The learning process does not need any offline training phase. Additionally, a robust controller with an adaptive gain is designed to offset the approximation error. Finally, simulation results show a satisfactory performance for the NHV attitude tracking, and also illustrate the controller's robustness.     

基于L1自适应律的尾坐式飞行器悬停位置控制

针对一款飞翼布局的尾坐式垂直起降飞行器,研究了悬停阶段的位置控制问题。小型飞行器质量、惯量小,具有纵向横向耦合严重、难以精确建模以及对扰动敏感等特点。首先,建立包含不确定性的非线性动力学模型,通过整合将其改写为一个不含不确定的标称系统和一个非线性非匹配的不确定性两部分,使用级联形式的非线性动态逆架构进行标称系统控制器的设计,显式的期望动力学表达使其更易与L1自适应控制器结合。接着使用L1自适应控制器对非线性非匹配的不确定性进行补偿,以便提高系统鲁棒性,改善瞬态性能。为减小运算负荷,便于工程实现,使用计算更轻量的比例型自适应律进行L1自适应控制器设计。数值仿真验证了所设计控制器具有良好的位置和高度指令跟踪能力。     

机械手鲁棒输出跟踪控制与仿真

研究了两自由度机械手终端夹持不定载荷时的输出跟踪问题。应用输入 /输出反馈线性化法和李亚普诺夫方法 ,提出了一种基于不确定项上界的机械手鲁棒输出跟踪控制器设计算法。所述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。该方法计算简单 ,容易实现。仿真结果证明了其可行性及有效性。     

基于非线性干扰观测器的转台反演鲁棒控制研究

针对转台系统三框耦合干扰和不平衡负载干扰等非线性因素的影响,提出了基于非线性干扰观测器的转台鲁棒Backstepping控制器.首先,阐述了鲁棒反演控制器的设计及稳定性证明;其次,通过设计并选择合适参数的NDO控制器,并进一步保证NDO控制器的全局指数稳定性及其有效性.实验仿真表明,这种新型鲁棒控制器能够很好地克服了转台现存的非线性因素影响,非常适合高精度飞行仿真转台系统的鲁棒控制.     

基于LMI的非线性系统PDC控制器设计方法

针对T-S模糊模型描述的非线性系统，提出了一种PDC控制器的直接设计方法。将闭环控制系统的稳定要求、性能指标及控制量约束条件统一到线性矩阵不等式的框架内，通过求解线性矩阵不等式族获得控制器参数。以倒立摆系统为例，对提出的设计方法进行了验证，仿真结果表明设计方法是有效的。     

Predictive control for satellite formation keeping

Based on a Hill equation and a nonlinear equation describing the desired and real dynamics of relative motion separately, a predictive controller is brought forward, which makes the real state track the desired ones to keep satellite formation. The stability and robustness of the controller are analyzed. Finally, comparing the simulation results of the proposed controller with that of the traditional, proportional-differential controller shows that the former one is capable of keeping the satellite formation more favorably, considering the disturbances such as the J2 perturbations.     

基于能量最优的无人驾驶汽车路径跟踪控制

动力总成控制对无人驾驶汽车的动态性能和经济性起着重要的作用。为了提高车辆在路径跟踪过程中的动力性和经济性,提出了一种基于能量最优化的路径跟踪控制策略。控制策略分为两部分,在上层控制器中使用非线性模型预测控制来计算所需要的动力参数和前轮转角。下层控制器采用基于电机能耗数值最优的方法进行设计。该方法可以确保电机一直运行在效率最优状态,并可根据电机状态动态调节无级变速器以满足车辆动力需求。仿真结果表明,控制方案具有良好的跟踪性能和节能潜力。