位置伺服系统量化效应引起的振荡问题

针对位置传感器量化非线性造成的位置伺服系统极限环振荡问题,研究了在量化非线性和死区非线性共同作用下系统趋于稳态时的特性。采用单回路多非线性系统的描述函数方法给出了振荡时力矩死区同位置传感器量化阶之间的关系,并求得了能使系统镇定的死区范围,提出了以伺服误差的方差作为评价指标的寻优判据,采用数值方法解得使系统镇定的最小死区。仿真结果表明,在量化非线性和所求得的最小死区共同作用下,原有位置伺服系统在稳态时可以避免振荡。     

Robust adaptive control of continuous system with unknown deadzone

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Anti-windup strategies for discrete-time switched systems subject to input saturation

This paper deals with the design of anti-windup compensator for discrete-time switched systems subject to input saturation. The cases of static and dynamic anti-windup controllers are addressed aiming at maximising the estimate of the basin of attraction of the origin for the closed-loop system. Two aspects of the switching law are taken into account during the design: either it is arbitrary or it is a part of the complete control law. Theoretical conditions allowing to synthesise the anti-windup compensator are mainly described through linear matrix inequalities. Computational oriented conditions are then provided to solve convex optimisation problems that are able to give a constructive solution.     

基于C8051F020海水预处理模块测控系统设计

根据对海水预处理模块控制要求的分析研究,提出设计一套能同时调节海水温度和流量的预处理模块控制器,该控制器使用Cygnal公司8位微处理器C8051F020作为主芯片,并给出了测控模块软、硬件设计方案,该控制器已经在海水预处理模块上试验成功。试验结果表明,该控制器设计稳定,具有很好的工程应用价值。     

时变负载比例阀控马达的精确点位控制

目的针对负载特性是以静扭矩为主的阀控马达电液比例伺服系统的点位控制,研究在变死区、变增益非线性因素影响下的定位控制性能问题.方法研究了死区的动态补偿方法,解决了由于变死区引起的大定位误差问题;提出了三级智能控制策略,解决了系统由于变增益引起的大超调问题.结果实验结果表明,在负载扭矩大范围内变化下,取得了快响应、高精度、小超调甚至无超调的定位控制.结论三级智能控制结合死区动态补偿的综合控制方法解决了阀控马达电液比例伺服系统由于变死区、变增益引起的定位控制性能问题.     

Robust Adaptive Identification of Nonlinear System Using Neural Network

It is well known that disturbance can cause divergence of neural networks in the identification of nonlinear systems. Sufficient conditions using so‐called modified algorithms are available to provide guaranteed convergence for adaptive system. They are dead zone scheme, adaptive law modification, and σ‐modification. These schemes normally require knowledge of the upper bound of the disturbance. In this paper, a robust weighttuning algorithm is used to train the multi‐layered neural network with an adaptive dead zone scheme. The proposed robust adaptive algorithm does not require knowledge of either the upper bound of the disturbance or the bound on the norm of the estimate parameter. A complete convergence proof is provided based on Lyapunov theorem to deal with the nonlinear system. Simulation results are presented to show good perfor‐mance of the algorithm.     

Finite-time sliding surface constrained control for a robot manipulator with an unknown deadzone and disturbance

This paper presents finite-time sliding mode control (FSMC) with predefined constraints for the tracking error and sliding surface in order to obtain robust positioning of a robot manipulator with input nonlinearity due to an unknown deadzone and external disturbance. An assumed model feedforward FSMC was designed to avoid tedious identification procedures for the manipulator parameters and to obtain a fast response time. Two constraint switching control functions based on the tracking error and finite-time sliding surface were added to the FSMC to guarantee the predefined tracking performance despite the presence of an unknown deadzone and disturbance. The tracking error due to the deadzone and disturbance can be suppressed within the predefined error boundary simply by tuning the gain value of the constraint switching function and without the addition of an extra compensator. Therefore, the designed constraint controller has a simpler structure than conventional transformed error constraint methods and the sliding surface constraint scheme can also indirectly guarantee the tracking error constraint while being more stable than the tracking error constraint control. A simulation and experiment were performed on an articulated robot manipulator to validate the proposed control schemes.     

Decentralized Sliding Mode Control for Multi‐Input Complex Interconnected Systems Subject to non‐smooth Nonlinearities

Many modern control systems become gradually more complicated and, consequently, the approach to control design approaches is both difficulty and complex. Moreover, if such a complex interconnected system is subjected to non‐smooth nonlinearities in the actuator, then unexpected difficulties, degradation or, even worse, instability will arise in the system performance. Therefore, a new decentralized sliding mode control (DSMC) approach for such a class of complex interconnected systems subjected to non‐smooth (deadzone) nonlinearities is proposed in this paper. Based on sliding mode control (SMC) theory, the proposed DSMC laws guarantee the global reaching condition of the sliding mode in uncertain complex interconnected systems with deadzone nonlinearities, that is, they can ensure that the sliding mode is reached in finite time and with prescribed transient behavior. In the sliding mode, the investigated uncertain complex interconnected system with deadzone nonlinearities in the actuator still are insensitive to system uncertainties and external disturbances. The proposed DSMC laws can work effectively for uncertain complex interconnected systems either with or without deadzone nonlinearities in the actuator. However, this cannot be guaranteed by the traditional DSMC design for systems without input deadzone nonlinearities. Furthermore, the sliding motion can be controlled to converge within a specified exponential speed. Finally, two illustrative examples with a series of computer simulations are presented to demonstrate the effectiveness of the proposed DSMC laws.     

Adaptive neural network control of unknown nonlinear affine systems with input deadzone and output constraint

In this paper, we aim to solve the control problem of nonlinear affine systems, under the condition of the input deadzone and output constraint with the external unknown disturbance. To eliminate the effects of the input deadzone, a Radial Basis Function Neural Network (RBFNN) is introduced to compensate for the negative impact of input deadzone. Meanwhile, we design a barrier Lyapunov function to ensure that the output parameters are restricted. In support of the barrier Lyapunov method, we build an adaptive neural network controller based on state feedback and output feedback methods. The stability of the closed-loop system is proven via the Lyapunov method and the performance of the expected effects is verified in simulation.     

基于自适应模糊逻辑的死区补偿控制研究

采用自适应模糊控制方法研究了死区补偿问题。通过引入系统当前状态与理想状态的归一化距离，构造了调节权函数，实现对模糊控制输入变量量化因子的自动调节，使得当系统处于死区时量化因子显著增加，使控制器的输出摆脱死区范围，形成对死区的自适应补偿。仿真结果表明该方法能够有效地控制系统稳态性能，同时不会引起系统的震荡。