基于模糊自适应的智能车驱动与转向协同控制研究

为改善智能车驱动电机调速与舵机转向的协调性,简化调参适配步骤,提出了基于MK60FN1(MK60)芯片的驱动与转向协同控制的模糊自适应控制方案。MK60计算出摄像头拍摄图像中车体与车道中线的位置偏差和角度偏差,根据位置偏差与舵机角度、角度偏差与测量到的车速,采用局部参数优化理论设计模糊自适应控制算法实时调整驱动电机和舵机的可调增益实现协同控制。与驱动、转向分开独立控制的策略相比较,本方案减小了稳态误差,智能车能够更快完成自主循迹,稳定性更好。     

Adaptive fuzzy neural network control for motor-toggle servomechanism

In this study, the dynamic responses of an adaptive fuzzy neural network (FNN) controlled toggle mechanism is described. The toggle mechanism is driven by a permanent magnet (PM) synchronous servo motor. First, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the motor-toggle servomechanism. Since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the motor-toggle servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Moreover, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed adaptive and adaptive FNN controllers are robust with regard to uncertainties.     

Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms

A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance     

Development of new training algorithms for neuro-wavelet systems on the robust control of induction servo motor drive

A robust wavelet neural network control (RWNNC) system is proposed to control the rotor position of an induction servo motor drive in this paper. In the proposed RWNNC system, a wavelet neural network controller is the main tracking controller that is used to mimic a computed torque control law, and a robust controller is designed to recover the residual approximation for ensuring the stable control performance. Moreover, to relax the requirement for a known bound on lumped uncertainty, which comprises a minimum approximation error, optimal network parameters and higher order terms in a Taylor series expansion of the wavelet functions, an RWNNC system with adaptive bound estimation was investigated for the control of an induction servo motor drive. In this control system, a simple adaptive algorithm was utilized to estimate the bound on lumped uncertainty. In addition, numerical simulation and experimental results due to periodic commands show that the dynamic behaviors of the proposed control systems are robust with regard to parameter variations and external load disturbance.     

Total sliding-mode controller for PM synchronous servo motor driveusing recurrent fuzzy neural network

In this paper, the dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode-controlled permanent-magnet (PM) synchronous servo motor are described. First, a newly designed total sliding-mode control system, which is insensitive to uncertainties, including parameter variations and external disturbance in the whole control process, is introduced. The total sliding-mode control comprises the baseline model design and the curbing controller design. In the baseline model design, a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design, an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system, the controlled system has a total sliding motion without a reaching phase. Then, to overcome the two main problems with sliding-mode control, i.e., the assumption of known uncertainty bounds and the chattering phenomena in the control effort, an RFNN sliding-mode control system is investigated to control the PM synchronous servo motor. In the RFNN sliding-mode control system, an RFNN bound observer is utilized to adjust the uncertainty bounds in real time. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties     

A hybrid computed torque controller using fuzzy neural network formotor-quick-return servo mechanism

The dynamic response of a hybrid computed torque controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this paper. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque control, a position controller is designed to control the position of a slider of the motor-quick-return servo mechanism. In addition, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a fuzzy neural network (FNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Moreover, a hybrid control system, which combines the computed torque controller, the FNN uncertainty observer, and a compensated controller, is developed based on Lyapunov stability to control the motor-quick-return servo mechanism. The computed torque controller with FNN uncertainty observer is the main tracking controller, and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rule numbers of the FNN. Finally, simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed hybrid computed torque control system are robust with regard to parametric variations and external disturbances     

某型运动控制平台三环控制系统设计与仿真

为了提高舰载某型运动控制平台伺服控制系统的运行效率和响应能力,以计算机建模和虚拟样机技术为基础,应用ADAMS和Matlab/Simulink协同仿真技术,建立了三环永磁同步电机矢量控制模型,并设计了一种同步电机驱动下的三环伺服控制器。仿真结果表明,该三环伺服控制器能够有效提高系统的动态响应性能和跟随定位能力,并且满足工程控制的相应稳定性要求。     

自适应逆控制策略在IC材料切片设备中的应用

 针对数字PID控制系统在高速同步伺服控制系统应用中存在的跟随误差和控制精度问题,提出了一种自适应逆控制策略,该方法以主电机为参考模型,通过LMS自适应控制算法调整控制器,使放线电机与主电机具有类似的动态特性.仿真结果与产品的检测结果表明系统能有效减小随动误差,提高控制系统的精度.     

Development of robust motor servo control for rear steering actuator based on two-degree-of-freedom control system

A rear steering system can improve vehicle stability by actively controlling the rear wheel angles. For designing the rear steering system, environmental conditions, performance deterioration due to aging and component variation as a result of manufacturing tolerance under mass production must be taken into consideration. We have applied two-degree-of-freedom (2DOF) feedback control with feed-forward control for the motor servo control so that the rear steering actuator can track the target rear steering angle accurately and stably. The control system is designed based upon a nominal mathematical model and its variation range.As a result, the rear steering actuator can be controlled with excellent performance and high reliability. This paper describes the mathematical model construction in the frequency domain and a robust motor servo controller design based on 2DOF feedback control with feed-forward control.     

Mechatronics and control of a long-range nanometer positioning servomechanism

Since the manufacturing accuracy of semiconductor and biochemical products is increased to nanometer level gradually, the development of long-range positioning servomechanism has become a hot research topic. The piezoelectric actuator is widely chosen to design precise positioning system for achieving the sub-micro or nano-position. However, their motion range is only 100 μm. Here, a long-range nanometer positioning servo system with AC servo motor and ball screw mechanism is built for the precision manufacturing purpose. It needs a special design control algorithm to obtain quick transient response and precise steady state accuracy. A new model-free fuzzy controller is designed to control this low-cost motion stage for achieving nanometer step response positioning. It has a novel gain auto-tuning strategy in response to the transient or steady state responses requirement. The experimental results show that this approach can reach more than 30 cm stroke and the step response error is less than 60 nm.     

Recurrent-fuzzy-neural-network sliding-mode controlled motor-toggleservomechanism

The dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode controlled motor-toggle servomechanism are described. The servomechanism is a toggle mechanism actuated by a permanent magnet (PM) synchronous servo motor. First, a total sliding-mode control system, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process, is introduced. In the baseline model design a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system the controlled system has a total sliding motion without a reaching phase. Then, to overcome the two main problems with sliding-mode control, a RFNN sliding-mode control system is investigated to control the motor-toggle servomechanism. In the RFNN sliding-mode control system a RFNN bound observer is utilized to adjust the uncertainty bounds in real time. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Simulated and experimental results due to periodic sinusoidal command show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties     

液压伺服系统自适应模糊变结构控制

结合变结构控制、自适应控制和模糊技术等特点,提出一种自适应模糊变结构控制方法.首先,设计一个带积分开关平面函数的变结构控制器,并构造一个二维模糊边界层宽度调节器以削弱抖振.其次,基于Lyapunov稳定性理论,引入一自适应算法,自适应调节变结构控制参数.应用于液压伺服系统的控制实验结果表明,所提出的控制方法能削弱抖振,改善液压伺服系统稳态控制精度,具有较强的鲁棒自适应综合性能.     

基于DSP的舰载搜索雷达稳定平台交流伺服系统的设计

给出了一种基于DSP的舰载搜索雷达交流伺服控制系统的设计方法。采用高速数字信号处理芯片TMS320LF2407A作控制主体,交流变频器和交流无刷电机作驱动,采用先进的数字PID控制算法,组成全数字交流伺服系统,保证系统具有很高的精度、可靠的稳定性和良好的可扩展性。仿真试验表明该设计满足舰载雷达伺服控制系统的要求。     

基于跟踪微分器的模型参考自适应控制

为了处理二阶系统模型参数的大范围不确定性,提出了基于跟踪微分器的模型参考自适应控制,利用两个非线性跟踪微分器分别得到系统输出的微分信号和误差的微分信号,同时抑制了高频噪声放大效应。根据被控对象的数学模型,自适应调节律能自动实时调节控制律中的参数。实验结果表明,当雷达伺服系统被控对象模型的参数在较大范围内变化时,该新型控制器有效补偿了二阶系统参数的不确定性,提高了伺服系统稳态和动态跟踪精度,保证了系统的全局渐近稳定。     

Intelligent optimal control of single-link flexible robot arm

This paper addresses the design and properties of an intelligent optimal control for a nonlinear flexible robot arm that is driven by a permanent-magnet synchronous servo motor. First, the dynamic model of a flexible robot arm system with a tip mass is introduced. When the tip mass of the flexible robot arm is a rigid body, not only bending vibration but also torsional vibration are occurred. In this paper, the vibration states of the nonlinear system are assumed to he unmeasurable, i.e., only the actuator position can be acquired to feed into a suitable control system for stabilizing the vibration states indirectly. Then, an intelligent optimal control system is proposed to control the motor-mechanism coupling system for periodic motion. In the intelligent optimal control system a fuzzy neural network controller is used to learn a nonlinear function in the optimal control law, and a robust controller is designed to compensate the approximation error. Moreover, a simple adaptive algorithm is proposed to adjust the uncertain bound in the robust controller avoiding the chattering phenomena. The control laws of the intelligent optimal control system are derived in the sense of optimal control technique and Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. In addition, numerical simulation and experimental results are given to verify the effectiveness of the proposed control scheme.     

High performance ultra-low speed servo system based on doubly coprime factorization and instantaneous speed observer

This paper proposes a new speed servo system for a wide speed range based on doubly coprime factorization and instantaneous speed observer. The proposed speed controller is a new two-degrees-of-freedom speed servo system considering the robust stability according to inertia variation, which is designed on the basis of doubly coprime factorization and disturbance observer. In an ultra-low speed range, a speed sensor based on a rotary encoder cannot detect a motor speed. In this paper, a speed information in a very low speed range is estimated by the proposed instantaneous speed observer which mounts the parameter identification algorithm to cancel out the estimation error caused by parameter variation. Therefore, the two-degrees-of-freedom speed servo system connecting the instantaneous speed observer regulates a motor speed robustly in a wide speed range including an ultra-low speed range, and it keeps the desired speed response for all speed reference including an ultra-low speed range.     

自适应变阻尼控制算法在随动系统中的应用

为了提高电动舵机系统的动态响应性能,文中提出了一种自适应变阻尼控制方法。首先,在分析舵机传递函数的基础上对其进行了简化;其次,分析了无阻尼自然频率和阻尼比对系统动态性能的影响;再次,结合常规PID控制结构简单、易于实现和遗传算法自适应能力强的优点,设计了基于自适应变阻尼控制算法的新型变阻尼控制器;最后,通过仿真结果对比,表明自适应变阻尼控制方法可以优化随动系统的动态响应性能,系统单位阶跃响应速度快,无超调。     

Desktop rapid prototyping system with supervisory control andmonitoring through Internet

Rapid prototyping (RP) can enhance design and manufacturing productivity by taking advantage of the Internet. Via the Internet, most small and medium size companies should be able to share the access of the RP machine remotely without owning the expensive machines. This study combines RP preprocessing, RP machine, and the Internet into a telecontrolled manufacturing system. Also presented is the software and hardware for a new LCD-based photosensitive resin RP machine that uses only visible light. This visible light can expose and solidify an entire layer at once, layer-by-layer, until the whole part is finished. The user sends a three-dimensional (3-D) CAD model (STL file) via the Internet to a telecontrol server, which transforms the CAD model into a RP machine LCD photomask display. The user can then direct the RP machine to build the RP part while watching a live image of the part via the World Wide Web. An online visual system allows inspection of the RP part quality during manufacturing. Part building is monitored by a pattern matching algorithm which compares a grabbed image with the photomask. If the grabbed image is not adequately similar to the photomask, the program stops manufacture and notifies the user. The experimental results show that RP using the Internet is promised, but the surface roughness should be further improved     

High-gain observer-based pump/valve combined control for heavy vehicle electro-hydraulic servo steering system

Electro-hydraulic servo steering system (EHSSS) has been widely used in multi-axle heavy vehicles. Noteworthy, the traditional EHSSS controlled only by servo solenoid valve has amounts of energy loss in throttling orifice. Although the steering control accuracy is ensured, it leads to low energy efficiency. In this paper, a novel pump/valve combined control (PVCC) EHSSS is proposed to increase the energy efficiency, which only uses one servo motor pump and one servo solenoid valve to drive the steering trapezoid mechanism. Based on the control objectives of low pressure difference in valve orifice and high steering tracking performance, a dual-input-dual-output control strategy is proposed. To guarantee the high steering tracking performance of PVCC steering system, a high-gain observer based sliding mode controller (HGO-SMC) is designed for controlling the spool displacement of servo solenoid valve. During the steering process, the servo motor pump is controlled by a simple speed feedforward and PID controller, so that the pressure difference in throttling orifice is kept at a low value to reduce the energy wasted. The experimental comparison results show that the proposed method can achieve the same tracking performance as valve control EHSSS with less energy consumption.