空间飞行器姿态控制律设计

空间飞行器姿态系统具有非线性、强耦合、多输入多输出的特点。针对飞行器姿态模型的非线性和不确定性,提出了分散模糊变结构控制方法。利用模糊系统对不确定性函数进行逼近,将获得的模糊系统函数作为系统不确定性界函数。对模糊逼近所带来的误差以及外部干扰项,采用控制补偿方法。理论分析和仿真研究表明,提出的方法具有控制精度高,实时计算量小,便于工程实现等优点。     

超机动飞行快回路的自适应模糊滑模控制

针对超机动飞行快回路的不确定非线性模型,提出了一种利用自适应模糊滑模控制器算法。在所得的最终控制信号中,采用模糊逻辑系统来逼近未知系统函数和开关项;所设计的鲁棒自适应律用来减小逼近误差,从而有效降低抖振。仿真结果表明,所设计的控制律能在过失速机动条件下控制飞机跟踪指令飞行,确保系统具有良好的动态和稳态性能,而且控制器具有很强的鲁棒性。     

空天飞行器姿态运动的模糊滑模鲁棒控制研究

针对空天飞行器(ASV)的姿态系统的非线性、强耦合和不确定性,提出了一种分散模糊滑模变结构控制方法.首先基于反馈线性化方法将ASV姿态系统解耦成3个独立的子系统;然后应用模糊滑模变结构控制方法分别设计了各子系统的姿态控制器.通过结合模糊控制与滑模变结构控制,有效抑制了ASV姿态系统的建模误差以及外部干扰.理论分析和仿真研究表明,所提方法具有控制精度高、鲁棒性强、便于工程实现等优点.     

无人机模糊小波神经网络轨迹线性化控制

针对系统存在不确定和有界干扰的情况,提出了一种基于模糊小波神经网络的轨迹线性化控制方法。利用模糊小波神经网络对非线性函数的逼近能力,减小不确定干扰对系统的影响,并与轨迹线性化方法结合设计了无人机飞控系统控制器。采用Lyapunov稳定性理论,证明了在所设计的控制器下,闭环系统所有信号一致最终有界。最后对系统存在不确定的情况下进行了仿真,并与没有加模糊小波神经网络的轨迹线性化控制器进行了对比,仿真结果证明了所提方法的有效性和鲁棒性。     

基于NDO的单向辅助面滑模飞行控制

针对存在建模误差和外界干扰等不确定因素的飞行姿态系统,使用单向辅助面滑模控制与非线性干扰观测器(NDO)相结合的方法设计控制器。采用单向辅助面滑模控制方法设计标称系统控制器,而对于系统中的不确定性和扰动则利用NDO进行逼近,并将NDO的输出用于设计鲁棒补偿控制项,所设计的控制器可以有效削弱抖振且能够显著提高收敛速度。最后通过Lyapunov理论证明了闭环系统的稳定性,仿真结果也体现了良好的控制效果。     

量化因子自整定的四旋翼飞行器模糊PID控制

针对四旋翼飞行器的非线性飞行控制模型,提出了一种新改进的量化因子自整定二维直接控制量型模糊PID控制器。通过分析量化因子与模糊控制器输入输出的模糊逻辑关系,对量化因子分别设计模糊整定器并确定了相应的模糊输入量以及模糊规则表,然后利用自整定算法实现量化因子在线实时调整。通过对四旋翼飞行器进行动力学建模以及控制通道分析,进行了姿态飞行控制仿真实验。与传统PID型模糊控制器相比,本文设计的控制器在动态响应速度及抗外界干扰性能方面效果更优。     

导弹电液伺服机构的自适应模糊滑模跟踪控制

针对导弹电液伺服机构的跟踪控制问题,提出了一种自适应模糊滑模的设计方案.使用具有参数在线调节的自适应模糊控制,逼近滑模控制中的等效控制部分,并确定非线性控制项以保证系统的稳定性.根据滑模控制原理给出四条模糊规则,以平滑不连续控制,达到削弱抖振的目的.仿真结果表明了该方案的有效性.     

参数不确定高超声速飞行器自适应模糊H_∞控制

针对具有参数不确定性特点的高超声速飞行器输出跟踪问题,提出了一种自适应模糊H∞控制器设计方法。考虑系统存在的参数不确定性,利用自适应模糊系统在线逼近动态逆控制器中的非线性项,同时引入鲁棒补偿项,减轻模糊系统逼近误差和系统外部干扰对控制系统稳定性造成的影响,提高控制器的H∞性能。利用Lyapunov理论对整个系统的稳定性进行证明。对比仿真结果表明该方法能够保证高超声速飞行器具有良好的跟踪性能和很强的鲁棒性。     

基于模糊逼近的非线性多时延系统的自适应跟踪控制

针对一类多输入多输出非线性多时延系统,提出了基于模糊逼近的自适应跟踪控制方案.该方案构建了基于模糊T-S模型的自适应时延模糊逻辑系统,用来逼近未知非线性时延函数.从而实现了对非线性系统的建模.根据跟踪误差给出了时延模糊逻辑系统的参数自适应律.设计了H_∞补偿器来抵消模糊逼近误差和外部扰动.基于Lyapunov稳定性理论,提出的控制方案保证了闭环系统的稳定性并获得了期望的H_∞跟踪性能.机械臂的仿真结果表明了该方案的有效性.     

扩张状态观测器下的自适应输出反馈控制

针对一类不确定非线性系统,研究了一种结合反步法和自抗扰控制的新的自适应输出反馈控制方法.通过引入扩张状态观测器(ESO)对被控系统的未知状态进行实时估计,同时利用扩张状态观测器实现对系统中的不确定项在线逼近及补偿.通过非线性滤波器对反步法设计过程中的虚拟控制信号进行求导,避免了传统反步法设计控制中复杂性爆炸的问题,并由...     

薄膜材料的绕组处理的适应失真压力的控制系统

薄膜材料的绕组处理是在一个高度非线性的动态系统中维持应力不变.提出一个为薄膜材料的绕组处理在不同摩擦锟供料速度下的适应模糊应力的控制系统.该提出的适应模糊应力的控制系统包括一个模糊应力控制器和一个适应调谐器.模糊应力控制器是主进度控制器,一个平移宽度的概念和变化模式技术被包括在模糊推论中以矫正模糊现象,而且只有一个参数因素需要被调整.为了对抗在实际应用中的不确定,一个失真压力的控制系统占据着简单控制框架,无震颤的,稳定跟踪性能和对不确定性的鲁棒的优势.与传统的比例积分应力控制方法相比较可提出的这种控制方法有显著的优势.     

模糊自适应PID控制在高炉TRT系统中的仿真研究

针对高炉TRT顶压控制系统存在的复杂性,高度非线性,时变不确定性等特点,提出一种模糊自适应PID优化控制算法,应用模糊推理的方法实现对TRT顶压控制系统中PID参数的自整定,达到对高炉顶压的稳定控制。实验结果表明,采用该优化算法可使控制效果得到明显改善,系统运行速度及可靠性等得到进一步提高,达到优化系统控制性能的目的,满足TRT系统的控制要求。     

Robust fuzzy neural network sliding mode control scheme for IPMSM drives

This article proposes a robust fuzzy neural network sliding mode control (FNNSMC) law for interior permanent magnet synchronous motor (IPMSM) drives. The proposed control strategy not only guarantees accurate and fast command speed tracking but also it ensures the robustness to system uncertainties and sudden speed and load changes. The proposed speed controller encompasses three control terms: a decoupling control term which compensates for nonlinear coupling factors using nominal parameters, a fuzzy neural network (FNN) control term which approximates the ideal control components and a sliding mode control (SMC) term which is proposed to compensate for the errors of that approximation. Next, an online FNN training methodology, which is developed using the Lyapunov stability theorem and the gradient descent method, is proposed to enhance the learning capability of the FNN. Moreover, the maximum torque per ampere (MTPA) control is incorporated to maximise the torque generation in the constant torque region and increase the efficiency of the IPMSM drives. To verify the effectiveness of the proposed robust FNNSMC, simulations and experiments are performed by using MATLAB/Simulink platform and a TI TMS320F28335 DSP on a prototype IPMSM drive setup, respectively. Finally, the simulated and experimental results indicate that the proposed design scheme can achieve much better control performances (e.g. more rapid transient response and smaller steady-state error) when compared to the conventional SMC method, especially in the case that there exist system uncertainties.     

Fuzzy supervisory sliding-mode and neural-network control for robotic manipulators

Highly nonlinear, highly coupled, and time-varying robotic manipulators suffer from structured and unstructured uncertainties. Sliding-mode control (SMC) is effective in overcoming uncertainties and has a fast transient response, while the control effort is discontinuous and creates chattering. The neural network has an inherent ability to learn and approximate a nonlinear function to arbitrary accuracy, which is used in the controllers to model complex processes and compensate for unstructured uncertainties. However, the unavoidable learning procedure degrades its transient performance in the presence of disturbance. A novel approach is presented to overcome their demerits and take advantage of their attractive features of robust and intelligent control. The proposed control scheme combines the SMC and the neural-network control (NNC) with different weights, which are determined by a fuzzy supervisory controller. This novel scheme is named fuzzy supervisory sliding-mode and neural-network control (FSSNC). The convergence and stability of the proposed control system are proved by using Lyapunov's direct method. Simulations for different situations demonstrate its robustness with satisfactory performance.     

Design and implementation of a robust and nonlinear flight control system for an unmanned helicopter

In this work, we focus on the design and implementation of a robust flight control system for an unmanned helicopter. A comprehensive nonlinear model for an unmanned helicopter system, which is built by our research team at the National University of Singapore, is first presented. A three-layer control architecture is then adopted to construct an automatic flight control system for the aircraft, which includes (1) an inner-loop controller designed using the H_∞ control technique to internally stabilize the aircraft and at the same time yield good robustness properties with respect to external disturbances, (2) a nonlinear outer-loop controller to effectively control the helicopter position and yaw angle in the overall flight envelope, and lastly, (3) a flight-scheduling layer for coordinating flight missions. Design specifications for military rotorcraft set for the US army aviation are utilized throughout the whole process to guarantee a top level performance. The result of actual flight tests shows our design is very successful. The unmanned helicopter system is capable of achieving the desired performance in accordance with the military standard under examination.     

基于自适应模糊理论的某型无人机起飞控制方法

基于合理简化的无人机纵向模型,设计了一种自适应模糊控制器,该控制器将Takagi-Sugeno模糊系统与等效控制器相结合,以增强系统的鲁棒性.只要求预先知道系统的相对阶以及未知函数的上下界即可,不需要精确的数学模型.Lyapunov合成方法证明了跟踪误差能趋近于零且其余的控制信号均有界.最后,结合优先级按比例分配的控制分配器,给出了存在扰动情况下飞行控制系统的仿真结果,表明即使在模型部分未知的情况下,该系统仍然能够达到飞行控制的指标性能和品质要求,验证了该方法的有效性.     

Adaptive Fuzzy Sliding Mode Control Algorithm for a Non-Affine Nonlinear System

This paper concerns the design of robust controller for a nonlinear system that can be represented or approximated in a non-affine form. The control algorithm is based on sliding mode control that incorporates a fuzzy tuning technique, and it superposes equivalent control, switching control, and fuzzy control. An equivalent control law is firstly designed based on a nominal system model that was obtained by using curve fitting techniques under MATLAB. Switching control is then added to guarantee that the state reaches the sliding surface in the presence of parameter and disturbance uncertainties. Also, fuzzy tuning schemes, which can be supported by learning techniques derived from neural networks, are employed to improve control performance and to reduce chattering in the sliding mode. To verify the performance of this controller, an experimental platform of a pneumatically actuated top-guided single-seated control valve, which belongs to a classical complex nonlinear system, was constructed. Also, the experimental results show that high performance and attenuated chatter are achieved and thus verify the validity of the proposed control approach to dynamic systems characterized by severe uncertainties.     

Auto-tuning of PID controller according to fractional-order reference model approximation for DC rotor control

This paper presents a stochastic, multi-parameters, divergence optimization method for the auto-tuning of proportional–integral–derivative (PID) controllers according to a fractional-order reference model. The study aimed to approximate the step response of the real closed-loop flight control system to the response of a theoretical reference model for a smoother and more precise flight control experience. The proposed heuristic optimization method can auto-tune a PID controller without a precise plant model. This is very advantageous when dealing with model and parameter uncertainties in real control application and practice. Experimental study confirms the reference model driven auto-tuning of the DC rotor prototype.     

Nonlinear adaptive torque control of electro-hydraulic load system with external active motion disturbance

This paper develops a high performance nonlinear adaptive control method for electro-hydraulic load simulator (EHLS). The tracking performance of EHLS is mainly affected by the following factors: actuator’s active motion disturbance, flow nonlinear and parametric uncertainties, etc. Most previous studies on EHLS pay too much attention on actuator’s active motion disturbance, while deemphasize the other two factors. This paper concerns EHLS as a motion loading system. Besides actuator’s motion disturbance, both the nonlinear characteristics and parametric uncertainties of the loading system are addressed by the present controller. First, the nonlinear model of EHLS is developed, and then a Lyapunov-based control algorithm augmented with parameters update law is developed using back-stepping design method. The stability of the developed control algorithm is proven via Lyapunov analysis. Both the co-simulation and experiment are performed to validate the effectiveness of the developed algorithm.