基于HOD的无模型四旋翼RBF滑模控制

为了探索解决在无模型控制算法中如何对系统的未知模型和扰动进行准确估计,提出一种基于高阶微分器(HOD)的无模型RBF神经网络滑模控制器(HODRBFSMC).引入HOD估计系统模型的各阶状态变量,并将系统模型的未知项和外界干扰统一归为总扰动,通过RBF神经网络对总扰动进行估计,并根据Lyapunov定理证明所设计控制器的闭环稳定性.为验证控制器的有效性,所设计的控制器被应用于四旋翼飞行器的轨迹控制,解决其模型参数复杂且飞行过程中易受外界干扰的问题.仿真实验表明,所提出方法能够有效估计并补偿总扰动,其轨迹跟踪能力和抗干扰性能相比PID和高阶微分反馈控制(HODFC)具有一定的优越性,能够很好地满足四旋翼飞行器控制的需求.     

低比特率视频编码中一种有效的码率分配算法

引入能较好地反映视频图像运动特性的差分帧直方图（HOD）来预分配码率.对于运动较为平稳的序,提出了基于HOD的分段常数码率预分配算法.帧级码率预分配根据视频原始序列HOD的统计特性来决定;对于运动程度变化较大的序列,采用修正后的线性HOD模型来预分配码率.实验结果表明,相对于H.264 JM76中的分配算法,该算法使得R-D性能平均提高0.35/dB,视频主观质量的下降也得到了有效的改善.     

改进滑模模型参考自适应的PMSM无传感控制

针对MRAS（模型参考自适应）观测器对PMSM（永磁同步电机）参数变化和外部干扰敏感的缺点,设计了一种用于PMSM无传感控制的软开关滑模模型参考自适应观测器。该观测器将滑模控制与MRAS相结合,并构造了边界层可变的正弦饱和函数,以抑制由于滑模控制引起的系统抖动,增强系统鲁棒性。同时引入sigmoid函数,以提高滑模软切换速度控制器的稳定性。实验仿真结果表明,软开关滑模模型参考自适应控制不仅可以实现边界层内的快速收敛、弱化抖动,而且跟踪能力强。     

不确定离散时间系统积分滑模保性能控制

针对不确定离散时间系统,研究其积分滑模（ＩＳＭ）保性能控制的设计问题．将最优保性能积分滑模面设计问题,转化为一个具有线性矩阵不等式（ＬＭＩ）约束的目标函数凸优化问题,给出了最优保性能积分滑模面存在的充分条件,并结合干扰估计器设计相应的保性能控制器．与传统滑模控制相比较,积分滑模保性能控制系统具有全阶滑动模态,系统的鲁棒性得到加强,消除了控制抖振和稳态抖振．仿真结果验证了该方法的有效性．     

永磁同步电动机混沌系统光滑二阶滑模控制

提出基于二阶滑模的控制方法控制永磁同步电动机（PMSM）中的混沌现象。利用Lyapunov函数构造了一种新的滑模面,能保证在滑模面上系统状态在有限时间内稳定到原点。控制器的设计采用了光滑二阶滑模方法,控制输入为光滑的函数,能有效消除抖振现象。仿真的结果也验证了控制方法的有效性。     

基于滑模预测控制的海底采矿车轨迹跟踪算法

海底采矿车多工作于稀软底质,其面临的外部扰动较大,难以快速收敛跟踪误差,精准地跟踪预设轨迹。为此,本文提出了一种海底采矿车的滑模预测控制（sliding model predictive control,SMPC）轨迹跟踪算法。基于海底采矿车的运动学模型,首先设计滑模控制率实现轨迹跟踪误差快速收敛,其次利用少预测时域的线性时变模型预测控制算法（linear time varying model predictive control,LTV-MPC）优化该滑模控制率。而后,通过证明滑模控制率收敛和模型预测控制稳定,保证了闭环控制系统的稳定性。RecurDyn&Simulink联合仿真结果表明,与单一的滑模控制（sliding mode control,SMC）和线性时变模型预测控制算法相比,所提出的SMPC轨迹跟踪算法提高了轨迹跟踪精度,且算法具有较好的实时性。     

一类非线性不确定系统的非奇异Terminal滑模控制

针对一类二阶非线性系统提出新的Terminal滑模控制面以克服传统的Terminal滑模控制的奇异问题，同时确保系统从任何初始状态能在有限时间内收敛至平衡点．进一步考虑系统参数摄动和外界扰动等不确定性因素上界的未知性，用Lyapunov稳定性方法给出了一个带有未知性上界参数估计的自适应非奇异Terminal滑模控制（NTSM）控制．最后通过实例比较三种滑模控制方法，仿真结果验证了非奇异Terminal滑模控制能克服传统的Terminal滑模控制的奇异问题，并说明了自适应非奇异Terminal滑模控制的有效性和可行性．     

无刷直流电机的自适应模糊滑模控制策略研究

为了提高无刷直流电机（BLDCM,brushless dC motor）控制系统的动态响应速度和干扰抑制能力,提出了一种新的自适应模糊滑模控制（AFSMC,adaptive fuzzy sliding mode control）策略。控制系统根据滑模开关函数的取值范围,可以切换滑模控制器的输出,能够改进滑模观测器的抖振现象和系统稳定性。控制器的控制律由自适应模糊控制算法调节,滑模控制器的输出减少了系统不确定时延的影响。根据所提出控制策略建立了仿真模型,并进行了仿真。仿真结果表明,所提出的控制策略能提高系统的动态性能和鲁棒性。该方法用于无刷直流电机的控制是可行的、有效的。     

基于RBF神经网络的作业型AUV自适应终端滑模控制方法及实验研究

研究了作业型AUV （自主水下机器人）的轨迹跟踪控制问题.实际作业中,水下机械手展开作业过程将引起AUV动力学性能变化,进而影响AUV轨迹跟踪控制;并且水流环境干扰亦将影响AUV轨迹跟踪控制.针对上述AUV轨迹跟踪控制问题,提出一种基于RBF （径向基函数）神经网络的AUV自适应终端滑模运动控制方法.该方法在李亚普诺夫稳定性理论框架下,采用RBF网络对机械手展开引起的AUV动力学性能变化和水流环境干扰进行在线逼近,并结合自适应终端滑模控制器对神经网络权值和AUV控制参数进行自适应在线调节.通过李亚普诺夫稳定性理论,证明AUV系统轨迹跟踪误差一致稳定有界.针对滑模控制项引起的控制量抖振问题,提出一种变滑模增益的饱和连续函数滑模抖振降低方法,以降低滑模控制量抖振.通过AUV实验样机的艏向和垂向的轨迹跟踪实验,验证了本文AUV系统控制方法和滑模降抖振方法的有效性.     

不确定离散时间系统积分滑模保性能控制

针对不确定离散时间系统,研究其积分滑模（ＩＳＭ）保性能控制的设计问题．将最优保性能积分滑模面设计问题,转化为一个具有线性矩阵不等式（ＬＭＩ）约束的目标函数凸优化问题,给出了最优保性能积分滑模面存在的充分条件,并结合干扰估计器设计相应的保性能控制器．与传统滑模控制相比较,积分滑模保性能控制系统具有全阶滑动模态,系统的鲁棒性得到加强,消除了控制抖振和稳态抖振．仿真结果验证了该方法的有效性．

     

Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

This paper deals with the design of a novel fuzzy proportional–integral–derivative (PID) controller for automatic generation control (AGC) of a two unequal area interconnected thermal system. For the first time teaching–learning based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed fuzzy-PID controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the fuzzy-PID controller. The superiority of proposed approach is demonstrated by comparing the results with some of the recently published approaches such as Lozi map based chaotic optimization algorithm (LCOA), genetic algorithm (GA), pattern search (PS) and simulated algorithm (SA) based PID controller for the same system under study employing the same objective function. It is observed that TLBO optimized fuzzy-PID controller gives better dynamic performance in terms of settling time, overshoot and undershoot in frequency and tie-line power deviation as compared to LCOA, GA, PS and SA based PID controllers. Further, robustness of the system is studied by varying all the system parameters from −50% to +50% in step of 25%. Analysis also reveals that TLBO optimized fuzzy-PID controller gains are quite robust and need not be reset for wide variation in system parameters.     

Adaptive RCMAC sliding mode control for uncertain nonlinear systems

An adaptive recurrent cerebellar-model-articulation-controller (RCMAC) sliding-mode control (SMC) system is developed for the uncertain nonlinear systems. This adaptive RCMAC sliding-model control (ARCSMC) system is composed of two systems. One is an adaptive RCMAC system utilized as the main controller, in which an RCMAC is designed to identify the system models. Another is a robust controller utilized to achieve system’s robust characteristics, in which an uncertainty bound estimator is developed to estimate the uncertainty bound so that the chattering phenomenon of control effort can be eliminated. The on-line adaptive laws of the ARCSMC system are derived in the sense of Lyapunov so that the system stability can be guaranteed. Finally, a comparison between SMC and ARCSMC for a chaotic system and a car-following system are presented to illustrate the effectiveness of the proposed ARCSMC system. Simulation results demonstrate that the proposed control scheme can achieve favorable control performances for the chaotic system and car-following systems without the knowledge of system dynamic functions.     

Robust Finite-time Synchronization of Non-identical Fractional-order Hyperchaotic Systems and Its Application in Secure Communication

This paper proposes a novel adaptive sliding mode control (SMC) method for synchronization of non-identical fractional-order (FO) chaotic and hyper-chaotic systems. Under the existence of system uncertainties and external disturbances, finite-time synchronization between two FO chaotic and hyperchaotic systems is achieved by introducing a novel adaptive sliding mode controller (ASMC). Here in this paper, a fractional sliding surface is proposed. A stability criterion for FO nonlinear dynamic systems is introduced. Sufficient conditions to guarantee stable synchronization are given in the sense of the Lyapunov stability theorem. To tackle the uncertainties and external disturbances, appropriate adaptation laws are introduced. Particle swarm optimization (PSO) is used for estimating the controller parameters. Finally, finite-time synchronization of the FO chaotic and hyper-chaotic systems is applied to secure communication.      

一种带有无模型外环补偿的PID控制

提出了一种新的控制器来解决传统的PID控制器不能很好地控制的系统,如非线性系统、变结构系统等。使用无模型控制作为补偿,用来解决由时变参数和参数估计误差引起的系统跟踪误差。当把外环去掉时,就是PID控制器。如果PID控制效果已经达到了理想状态,即受控对象的输出已经跟踪了期望输出,无模型控制的输出控制信号不起作用。证明了无模型控制的收敛性,仿真结果表明了该方法的有效性。     

基于粒子群优化算法的PID控制器参数整定

PID控制器的性能完全依赖于其参数的整定和优化,但参数的整定及在线自适应调整对常规的PID控制器是难以解决的问题。根据粒子群算法具有对整个参数空间进行高效并行搜索的特点,提出了一种基于粒子群优化算法整定PID控制器参数的设计方法,并定义了一种新的性能指标函数来评价PID控制器的性能。现以二阶的船舶控制装置为研究对象,运用粒子群优化方法对PID控制器参数进行了寻优研究。仿真结果表明,该方法比一般PID参数整定方法具有更好的控制性能指标,有着一定的工程应用价值。     

一种非线性PID控制算法的仿真研究

研究PID控制系统优化问题,工业控制被控对象均具有非线性、时变和大时滞性,引起系统的品质性能差,传统的线性控制难以达到所要求精度。为了提高系统控制精度,利用PID控制器各增益参数与偏差信号间的非线性关系,提出一种非线性PID控制算法。首先将PID参数转化为优化问题,然后采用粒子群算法的全局、并行搜索能力对非线性控制参数进行求解,得到一组最优的PID控制参数。仿真结果表明,相对于传统线性PID控制,非线性PID控制器超调小,调节时间短,并提高了控制精度,有效解决了传统PID难以准确控制非线性对象的难题。     

Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is mea sured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control, automatic generation control (AGC) plays a crucial role. In this paper, multi-area (Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative (PID) controller as a supplemen tary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm (FFA). The experimental results demonstrated the comparison of the proposed system performance (FFA-PID) with optimized PID controller based genetic algorithm (GA PID) and particle swarm optimization (PSO) technique (PSO PID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error (ITAE) cost function with one percent step load perturbation (1% SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.      

Optimum design of fractional order PID controller for AVR system using chaotic ant swarm

Fractional-order PID (FOPID) controller is a generalization of standard PID controller using fractional calculus. Compared to PID controller, the tuning of FOPID is more complex and remains a challenge problem. This paper focuses on the design of FOPID controller using chaotic ant swarm (CAS) optimization method. The tuning of FOPID controller is formulated as a nonlinear optimization problem, in which the objective function is composed of overshoot, steady-state error, raising time and settling time. CAS algorithm, a newly developed evolutionary algorithm inspired by the chaotic behavior of individual ant and the self-organization of ant swarm, is used as the optimizer to search the best parameters of FOPID controller. The designed CAS-FOPID controller is applied to an automatic regulator voltage (AVR) system. Numerous numerical simulations and comparisons with other FOPID/PID controllers show that the CAS-FOPID controller can not only ensure good control performance with respect to reference input but also improve the system robustness with respect to model uncertainties.     

基于布谷鸟搜索算法优化的BLDCM角度控制

当前,经典比例积分微分(PID)控制在无刷直流电机(BLDCM)控制领域仍然占据十分重要的地位。为了解决传统PID控制器参数优化费时、最佳控制性能难以保证的问题,提出使用布谷鸟搜索(CS)算法优化PID控制器(CS-PID)构成电机的角度位置控制。其次,选用时间乘绝对误差积分(ITAE)函数作为CS算法的适应性函数,为PID控制器参数优化的合理性提供参考。最后,以粒子群算法优化PID(PSO-PID)控制器为基准,利用MATLAB仿真软件在恒定阶跃函数下分别对CS-PID控制器和PSO-PID控制器进行了实验测试。仿真试验结果表明:CS-PID控制器具有较好的控制性能指标;相对于PSO-PID控制器,CS-PID控制器优化算法具有优越性和有效性。