A Review of Control Algorithms for Autonomous Quadrotors

The quadrotor unmanned aerial vehicle is a great platform for control systems research as its nonlinear nature and under-actuated configuration make it ideal to synthesize and analyze control algorithms. After a brief explanation of the system, several algorithms have been analyzed including their advantages and disadvantages: PID, Linear Quadratic Regulator (LQR), Sliding mode, Backstepping, Feedback linearization, Adaptive, Robust, Optimal, L1, H∞, Fuzzy logic and Artificial neutral networks. The conclusion of this work is a proposal of hybrid systems to be considered as they combine advantages from more than one control philosophy.

Modeling and Robust Backstepping Sliding Mode Control with Adaptive RBFNN for a Novel Coaxial Eight-rotor UAV

This paper focuses on the robust attitude control of a novel coaxial eight-rotor unmanned aerial vehicles(UAV) which has higher drive capability as well as greater robustness against disturbances than quad-rotor UAV. The dynamical and kinematical model for the coaxial eight-rotor UAV is developed, which has never been proposed before. A robust backstepping sliding mode controller(BSMC) with adaptive radial basis function neural network(RBFNN) is proposed to control the attitude of the eightrotor UAV in the presence of model uncertainties and external disturbances. The combinative method of backstepping control and sliding mode control has improved robustness and simplified design procedure benefiting from the advantages of both controllers. The adaptive RBFNN as the uncertainty observer can effectively estimate the lumped uncertainties without the knowledge of their bounds for the eight-rotor UAV. Additionally, the adaptive learning algorithm, which can learn the parameters of RBFNN online and compensate the approximation error, is derived using Lyapunov stability theorem. And then the uniformly ultimate stability of the eight-rotor system is proved. Finally, simulation results demonstrate the validity of the proposed robust control method adopted in the novel coaxial eight-rotor UAV in the case of model uncertainties and external disturbances.

Intelligent vehicle lateral controller design based on genetic algorithmand T-S fuzzy-neural network

Non-linearity and parameter time-variety are inherent properties of lateral motions of a vehicle. How to effectively control intelligent vehicle (IV) lateral motions is a challenging task. Controller design can be regarded as a process of searching optimal structure from controller structure space and searching optimal parameters from parameter space. Based on this view, an intelligent vehicle lateral motions controller was designed. The controller structure was constructed by T-S fuzzy-neural network (FNN). Its parameters were searched and selected with genetic algorithm (GA). The simulation results indicate that the controller designed has strong robustness, high precision and good ride quality, and it can effectively resolve IV lateral motion non-linearity and time-variant parameters problem.

Augmented Lagrangian Genetic Algorithm Based Decentralized Control Configuration Design for Fluid Catalytic Cracking Units

In this work, three decentralized control configuration designs—independent, sequential and simultaneous designs—were used in multivariable feedback configurations for PI control of the riser and regenerator temperatures of FCCU in order to compare their performances. Control design was formulated as optimization problem to minimize infinity norm of weighted sensitivity functions subject to μ-interaction measure bound on diagonal complementary functions of the closed loop system. The optimization problem was solved using augmented Lagrangian genetic algorithm. Simulation results show that simultaneous and independent designs give good response with less overshoot and with no oscillation. Bound on μ-interaction measure is satisfied for both designs meaning that their nominal stabilities are guaranteed;however, it is marginal for simultaneous design. Simultaneous design outperforms independent design in term of robust performance while independent design gives the best performance in terms of robust stability. Sequential design gives the worst performance out of the three designs.

金属弧焊机器人加工轨迹智能规划研究

针对金属弧焊机器人加工轨迹运动规划精度低的问题,提出一种基于改进梯形速度规划算法的弧焊加工轨迹智能规划方法。该方法首先根据D-H法构建机器人的运动学模型,根据拉格朗日法构建机器人的动力学模型;然后应用加速度规划改进后的梯形速度规划算法进行运动规划;最后基于稳健控制的运动修正方法对初步运动规划结果进行修正。仿真实验结果表明:相比于梯形速度规划算法,改进的梯形速度规划算法具有更高的运动规划精度;基于稳健控制的运动修正方法的加入,使运动规划精度、抗干扰性都得到提升,且提高了机器人关节的使用寿命;该方法具有较好的弧焊加工轨迹智能规划性能,基本满足研究需求。

基于鲁棒控制的自适应分数阶梯度优化算法设计

当目标函数是强凸函数时,一般的分数阶梯度下降法不能够使函数收敛到最小值点,只能收敛到一个包含最小值点的区域内或者是发散的.为了解决这个问题,本文提出了自适应分数阶梯度下降法(AFOGD)和自适应分数阶加速梯度下降法(AFOAGD)两种新的优化算法.受到鲁棒控制理论中二次约束和李雅普诺夫稳定性理论的启发,建立了一个线性矩阵不等式去分析所提出的算法的收敛性.当目标函数是L-光滑且m-强凸时,算法可以达到R线性收敛.最后几个数值仿真证明了算法的有效性和优越性.

孤网模式下水电机组智能鲁棒控制

针对孤网模式下水电机组PID控制策略鲁棒性差的缺点,设计了适用于非线性水轮机调节系统(hydraulic turbine regulating system,HTRS)的基于状态动态测量、反馈线性化和改进黏菌算法(improved slime mold algorithm,ISMA)的智能鲁棒控制器(intelligentrobustcontroller,IRC),以实现对水电站全工况实时最优控制。首先,充分考虑水轮机非线性、随动系统限速环节和高阶发电机特性,构建非线性、复杂HTRS数值仿真模型,并引入系统跟踪偏差的积分,推导出了孤网模式下考虑系统跟踪偏差的HTRS高阶状态空间方程模型。然后,结合高阶状态空间方程和H∞控制实现了HTRS鲁棒控制。进一步,基于ISMA对H∞控制器参数进行智能寻优。最后,结合某电站真实数据构建非线性、复杂HTRS仿真平台,验证了所提控制策略对系统参数和工况的敏感性、频率扰动下的强鲁棒性。

代理模型辅助的复杂网络能控性鲁棒性优化方法

近年来,复杂网络的鲁棒性优化问题引起人们广泛关注.复杂网络暴露在外会受到各种各样的攻击,因此如何设计抗击能力较好的网络结构成为了研究热点.虽然现有的方法在小规模复杂网络的鲁棒性方面已经取得了显著成果,但大规模复杂网络的能控性鲁棒性优化的计算成本非常大.而代理模型可以以较低的计算成本来代替优化过程中对复杂网络能控性鲁棒性的评估,但一个代理模型不可能适用于评估所有类型的复杂网络能控性鲁棒性.文中将Dempster-Shafer理论应用于代理模型选择及其混合,并把选择出的代理模型用来辅助进化算法搜索能控性鲁棒性更优的网络结构.此方法在SF、ER、SW、RR、RT和QS 6种合成网络上的实验结果表明:在不同类型的复杂网络中选择合适的代理模型能更好的辅助进化算法找到能控性鲁棒性更优的网络结构.

基于μ综合理论与遗传算法的并网逆变器鲁棒控制方法

在并网逆变器的实际运行中,电网阻抗和时间延迟系数的不确定性会影响到系统的稳定运行。为提高该含参数不确定性的系统的鲁棒性,提出采用μ综合理论来对系统进行鲁棒控制,通过对参数不确定性及其他性能约束的加权函数进行设计,从而使系统能够满足鲁棒稳定性与鲁棒性能要求。针对μ综合法设计的控制器阶数过高,难以工程实现的问题,可将其与遗传算法结合来对定结构控制器进行优化。首先采用μ综合理论搭建系统的摄动模型,然后用遗传算法对系统控制器的参数进行设计,使摄动系统能够实现结构化奇异值最小化的目标,从而达到对参数变化具有更强的鲁棒适应性的要求。实验结果表明,用该方法设计的控制器比用传统方法设计的控制器具有更好的鲁棒性。

基于ISSA-H_(∞)的水电机组鲁棒控制

随着风电、光伏等随机能源大量接入,电网结构变得复杂。在此背景下,水电机组将根据需要经常处于变工况运行,运行环境日趋恶劣,其传统的PID控制策略显然难以实现各种复杂工况下的最优控制。为此,将H_(∞)理论应用于水电机组,并基于改进樽海鞘算法(ISSA)和综合ITAE指标对其参数进行优化,实现了基于ISSA-H_(∞)的水电机组自适应鲁棒控制。仿真结果表明,相比传统的PID控制器,设计的基于ISSA-H_(∞)的自适应鲁棒控制器在不同工况下均有优异的调节性能,实现了水电机组多工况下最优控制。

基于Petri网的柔性装配系统的鲁棒控制

该文集中讨论了拥有不可靠柔性装配系统(Flexible Assembly System,FAS)的鲁棒死锁控制问题,确保当故障资源恢复后系统应恢复正常生产而不会出现堵塞现象,即不仅可以在无故障资源的情况下使系统远离死锁,而且还能在有不可靠资源时保证系统的顺利生产。首先,建立拥有不可靠资源FAS的Petri网模型,模拟整个系统的运行情况和资源的故障与恢复;其次,根据系统的控制目标,通过提出的进程可被推进算法和改进银行家算法设计了一种满足鲁棒性的死锁避免策略;然后,利用两个例子验证所设计的鲁棒控制策略的有效性;最后,对比实验结果表明,所提出的死锁控制策略所得到的可达标识数为30710个,显著多于其他论文的策略,在可允许性上具有一定优越性。

Boost型PFC变换器的自适应超螺旋全局滑模控制

针对Boost型功率因数校正(PFC)变换器,提出一种自适应超螺旋全局滑模控制(ASTTSMC)方法。该方法可以保证PFC变换器在理想工作条件下具备良好的性能,并且在面对负载突变、电网电压波动和电感参数变化等系统不确定性情况下时仍然能够保持全局鲁棒性。在控制律的设计中,采用全局滑模控制作为基准模型,从而确保系统在整个响应过程都具有鲁棒性;同时,切换项采用自适应超螺旋算法,考虑系统不确定项,通过自适应超螺旋算法中的切换增益参数来改善系统潜在的“抖振”现象;此外,基于李雅普诺夫稳定定理证明了所设计控制系统的稳定性。实验结果表明,在理想工作条件下和系统存在不确定性的情况下,该方法都能够有效改善网测电流的质量和提高变换器的功率因数,验证了该方法的有效性和所设计控制器的鲁棒性。

鲁棒参数自适应微振动控制算法

递推最小二乘(Recursive Least Squares,RLS)算法因其简单、快速的特点,在微振动自适应控制领域被广泛应用。由于微振动主动控制系统中扰动环境的特殊性及复杂性,需要重点考虑微振动控制中所采用的参数自适应算法在参数估计过程中的鲁棒性。针对多输入多输出(Multiple Input Multiple Output,MIMO)微振动主动控制系统,基于无限冲激响应(Infinite Impulse Response,IIR)滤波器,提出一种结合死区和归一化的MIMO鲁棒参数自适应算法,并给出其详细的算法推导与收敛性分析。在此基础上,通过构建三自由度微振动主动振动控制实验系统,针对单频窄带扰动、双频窄带扰动展开了对比实验分析,相关的实验结果验证了所提出鲁棒参数自适应算法的可行性和鲁棒性。

基于改进DDPG的变速抽蓄机组参与系统调频研究

在挖掘双馈型抽水蓄能(DFIM-PSH)机组调频能力的基础上,提出一种基于改进深度确定性策略梯度(DDPG)算法的系统频率控制方法。首先,基于所确定的DFIM-PSH机组在发电与抽水工况下的频率控制环节,构建考虑风电接入的含DFIMPSH单区域系统频率控制模型。其次,在考虑机组运行约束的基础上以最小化系统频率偏差及调频出力为目标,引入DDPG算法对各机组的AGC控制指令进行优化。通过在预学习中同时引入随机外部扰动与模型参数变化,提高AGC控制器在具有强不确定性环境中的适应性。最后,在仿真验证DFIM-PSH调频优势的基础上,在不同风电接入及扰动等多场景进行仿真分析,结果表明,所提频率控制方法能有效改善新型电力系统的频率特性且具有强鲁棒性。

Real-time Feed-forward Force Compensation for Active Magnetic Bearings System Based on H∞ Controller

There are two kinds of unbalance vibrations—force vibration and displacement vibration due to the existence of unbalance excitation in active magnetic bearings（AMB） system. And two unbalance compensation methods—closed-loop feedback and open loop feed-forward are presented to reduce the force vibration. The transfer function order of the control system directly influencing the system stability will be increased when the closed-loop method is adopted, which makes the real-time compensation not easily achieved. While the open loop method would not increase the primary transfer function order, it provides conditions for real-time compensation. But the real-time compensation signals are not easy to be obtained in the open loop method. To implement real-time force compensation, a new method is proposed to reduce the force vibration caused by the rotor unbalance on the basis of AMB active control. The method realizes real-time and on-line force auto-compensation based on H∞ controller and one novel feed-forward compensation controller, which makes the rotor rotate around its inertia axis. The time-variable feed-forward compensatory signal is provided by a modified adaptive variable step-size least mean square（VSLMS） algorithm. And the relevant least mean square（LMS） algorithm parameters are used to solve the H∞ controller weighting functions. The simulation of the new method to compensate some frequency-variable and sinusoidal signals is completed by MATLAB programming, and real-time compensation is implemented in the actual AMB experimental system. The simulation and experiment results show that the compensation scheme can improve the robust stability and the anti-interference ability of the whole AMB system by using H∞ controller to achieve close-loop control, and then real-time force unbalance compensation is implemented. The proposed research provides a new control strategy containing real-time algorithm and H∞ controller for the force compensation of AMB system. And the stability of the control system is finally improved.

Near time optimal control with perturbation estimation for flexible spacecraft slewing maneuvers

A feedforward approach for generating near time optimal controller for flexible spacecraft rest-to-rest maneuvers is presented with the objective insensitivity to modeling errors, parameter uncertainty and minimizing the residual energy of the flexible modes. The perturbation estimation of flexible appendages to the rigid-hub is accomplished simply via compare the output of real plant with the reference model, and the approach is based on combine this estimation with the bang-bang control for the rigid-hub modes through analysis the basic constraint and the additional constraint, i.e. zero coupling torque and zero coupling torque derivative for general two orders system and three orders system with considerate attitude acceleration mode near time optimal controls. These time optimal controls with control constraints and state constraints leads to forming a boundary-value problem, and resolved the problem using an iterative numerical algorithm. The near time optimal control with perturbation estimation shows a good robust to parameter uncertainty and can suppress the vibration and minimizing the residual energy. The capability of this approach is demonstrated through a numerical example in detail.

风电并网下的抽水蓄能鲁棒最优调频控制策略

针对短时风电功率波动及负荷波动给电力系统带来的一次调频问题,提出了风电并网系统下抽水蓄能机组的最优鲁棒调频控制方法。建立了以抽水蓄能机组为主要调频电源的电力系统整体状态空间方程,并以权重函数的形式将风电出力波动及负荷波动作为扰动变量将其一般鲁棒控制问题转换为最优鲁棒控制问题。运用混合秃鹫灰狼算法(mutation harris hawks optimization grey wolf optimizer,MHHOGWO)对反映风电出力波动特性和负荷波动特性加权矩阵参数选优,求解出以系统频率偏差最小的鲁棒控制器。仿真结果表明:所提方法能有效减小外部扰动对整个电力系统造成的频率偏差,抽水蓄能机组对短时外部功率波动的调节响应性能也得到提高。

计及分布式电源集群不确定性的配电网分散鲁棒电压控制

随着光伏、储能等分布式电源广泛接入,导致配电网区域电压波动频繁。针对分布式电源分散接入带来的不确定性问题,提出了一种计及分布式电源集群不确定性的配电网分散鲁棒电压控制方法。将大规模分布式电源聚合成相互关联的集群,对电压控制进行分区域调节。首先,针对配电网结构复杂和分布式电源点多面广的问题,设计一种基于改进Louvain算法的配电网集群划分方案,利用模块度函数并兼顾了集群的电压灵敏度和分布式电源调控容量。然后,由于分布式电源接入后配电网潮流更加复杂多变,在划分集群的基础上提出一种考虑不确定性的分散鲁棒控制方法,协同各分布式电源集群的调控能力,抑制由于模型参数及功率波动等不确定性导致的配电网电压波动。最后,通过算例分析验证了所提方法的可行性和有效性。

基于遗传算法优化的永磁同步电机模糊PI控制

针对传统模糊PI控制调节精度不高、调节周期较长的问题,提出一种基于优化模糊PI的永磁同步电机(PMSM)控制方法,利用遗传算法对模糊控制器进行优化,降低控制性能和精度不足的不利因素影响,达到模糊规则的合理优化。利用MATLAB/Simulink软件对优化模糊PI的PMSM矢量控制系统进行模型搭建,并与传统模糊PI控制系统进行了比较,仿真结果表明,优化模糊PI控制系统较传统模糊PI控制系统反应速度更快,振动幅度更小,鲁棒性更好,系统的动态响应特性更好。

基于SSA的微电网负荷频率鲁棒H_(2)/H_(∞)控制研究

针对微电网中由可再生能源出力波动和负荷变化引起的频率振荡问题,提出了一种混合H_(2)/H_(∞)鲁棒策略的负荷频率控制器。首先建立了具有功率扰动信号的风光柴微电网负荷频率控制模型。然后基于鲁棒H_(∞)理论设计了鲁棒H_(2)/H_(∞)控制器。在控制器的设计中,使用了樽海鞘群算法(SSA)对有关加权矩阵参数和范数权重进行寻优求解,使控制器的调整性能达到最好。最后仿真结果表明,相比于传统的鲁棒H_(∞)控制和PID控制,提出的H_(2)/H_(∞)鲁棒控制器在微电网的外界功率干扰以及系统的参数扰动工况下均有较好的控制效果。