一类非匹配不确定非线性系统的鲁棒跟踪控制制

针对一类半严格反馈型不确定非线性系统,提出一种鲁棒反演滑模变结构控制方法.采用反演控制方法设计了使前n-1阶子系统稳定的虚拟控制律,抑制非匹配不确定性的影响;在第n步设计了一种连续可导的滑模变结构控制律,消除控制抖振,实现了对存在未知不确定性及扰动系统的鲁棒输出跟踪.通过Lyapunov定理证明了闭环系统所有信号最终有界.仿真结果验证了该方法的有效性.     

基于RBF神经网络的块控制器设计

针对一类非匹配不确定性的线性系统,基于块控原理,提出了一种结合块控制、神经网络控制和backstepping控制技术的设计方法。利用反演设计方法来处理系统中的非匹配不确定性,利用神经网络来估计系统中的不确定性,再利用鲁棒控制方法来改善系统的性能,利用Lyapunov稳定性定理证明了系统的渐近稳定性。最后,给出的仿真实例证明了所提出的方法的正确性和有效性。     

非匹配不确定非线性系统的自适应反演滑模控制

针对一类具有非匹配不确定性的最小相位仿射非线性系统,研究其在未知扰动作用下的调节问题。基于自适应反演设计方法和变结构控制设计了控制方案,实现不确定系统的鲁棒调节。与经典反演设计相比,本方案允许非参数化不确定性,增强了控制系统的鲁棒性。     

非匹配不确定系统的显式反步变结构控制

研究非线性多输入多输出不确定系统在非匹配条件下的鲁棒输出跟踪问题.基于相对阶的假设,给出了多输入多输出不确定系统的标准型;进而结合反步设计方法和变结构控制方法,提出一种非匹配条件下非线性不确定系统鲁棒输出跟踪问题的显式反步变结构控制器设计方法.所给跟踪算法不仅只由不确定性的变化范围直接确定,而且有效地克服了现有反步设计算法因隐式递推关系和复杂偏导数计算给实时控制带来的困难.     

不匹配不确定线性时滞系统的鲁棒自适应控制

对一类同时具有匹配不确定性及结构确定不匹配不确定性的不确定时滞系统进行鲁棒自适应控制。首先,采用李雅谱诺夫函数方法,结合基于线性矩阵不等式的鲁棒控制器设计方法和变结构控制方法,设计鲁棒控制器,保证闭环系统的二次渐近稳定。利用自适应参数估计方法,设计具有匹配不确定性范数界估计能力的鲁棒自适应控制器,保证闭环系统的一致终结有界。结合算例,进行控制器的设计和仿真研究,验证所提出的设计方法的有效性。     

不确定非线性系统的自适应反推高阶终端滑模控制

针对一类非匹配不确定非线性系统,提出一种神经网络自适应反推高阶终端滑模控制方案.反推设计的前1步利用神经网络逼近未知非线性函数,结合动态面控制设计虚拟控制律,避免传统反推设计存在的计算复杂性问题,并抑制非匹配不确定性的影响;第步结合非奇异终端滑模设计高阶滑模控制律,去除控制抖振,使系统对于匹配和非匹配不确定性均具有鲁棒性.理论分析证明了闭环系统状态半全局一致终结有界,仿真结果表明了所提出方法的有效性.     

一类不确定线性切换系统的鲁棒H∞滑模控制

对一类含有非匹配不确定性且有外部干扰的线性切换系统,研究了H∞滑模变结构控制问题.利用LMI技术和单Lyapunov函数方法,设计单H∞滑模面,切换律以及子系统的变结构控制器,确保了切换系统的闭环系统为鲁棒稳定,且具有H∞扰动衰减度γ.系统状态到达滑模面后,该滑模面成为切换系统的全局鲁棒滑模面,可提高系统的暂态性能和鲁棒性.仿真例子说明所提出设计方法的有效性.     

一类非匹配不确定性系统的变结构控制

研究一类非匹配不确定性系统的变结构控制问题. 引入适当的状态变换, 将非匹配不确定系统描述成具有分级形式的两个子系统. 在保证第一个子系统二次渐近稳定的条件下, 利用线性矩阵不等式方法得到第二个状态变量的期望值, 并由第二个状态变量与其期望值之差构造滑动模, 并利用滑模运动的到达条件, 得到变结构控制律.     

一类非匹配不确定性系统的变结构控制

研究一类非匹配不确定性系统的变结构控制问题.引入适当的状态变换,将非匹配不确定系统描述成具有分级形式的两个子系统.在保证第一个子系统二次渐近稳定的条件下,利用线性矩阵不等式方法得到第二个状态变量的期望值,并由第二个状态变量与其期望值之差构造滑动模,并利用滑模运动的到达条件,得到变结构控制律.     

不确定非线性系统的多模反演滑模控制

对一般形式的仿射非匹配不确定非线性系统,研究了一种具有任意小跟踪误差的稳定控制器的新方法,结合反演（backstepping）设计和变结构控制,提出了反演变结构控制策略,对存在非匹配的不确定性和未知干扰的系统,设计的反演结构控制器实现了鲁棒输出跟踪,闭环系统在有限时间进入滑动模态,仿真算例证实了理论结果。     

Adaptive Variable Structure Control Design for Uncertain Time-Delayed Systems with Nonlinear Input

This paper presents a new robust control for uncertain dynamic time-delayed systems with series nonlinearities. It is implemented by using variable structure control. The proposed variable structure controller ensures the global reaching condition of the sliding mode of the uncertain time-delayed system. However, in the sliding mode, the investigated uncertain time-delayed system still bears the insensitivity to the uncertainties and disturbances as the systems with linear input. Furthermore, the proposed controller is achieved through adaptive variable structure control without the limitation of knowing the bounds of the uncertainties and perturbations in advance.     

VARIABLE STRUCTURE CONTROL OF TWO MANIPULATORS HANDLING A CONSTRAINED OBJECT

The control of two manipulators handling a constrained object involves the control of the position of the object, the internal force used to grasp the object, and the constraint force due to the constraint surface. The robustness of the controller must be guaranteed when the system faces parameter uncertainties and or external disturbances. In this paper, a variable structure control law is proposed. This controller guarantees the asymptotic convergence of the position of the object, internal force, and constraint force to their desired values when uncertainties on the parameters and external disturbances are present in the system. Simulation results for two planar robots moving an object along a horizontal plane illustrate the fact that the proposed controller achieves the desired asymptotic tracking.     

不确定关联大系统的分散变结构控制

针对不确定关联大系统,提出了一种变结构渐近稳定控制律.利用左特征向量设计了关联大系统的切换面,在理想切换面附近利用一个辅助函数,建立一个柔化带来柔化控制律.利用Lyapunov稳定性理论和对角占优性质,严格证明了所设计的不确定关联大系统的变结构控制系统的全局渐近稳定性.这种变结构控制律不仅提高了系统进入滑动模态的快速性,而且消除切换面附近的抖振.仿真算例验证了该方法的有效性.     

Robustness of multivariable discrete-time variable structure control

A robust stability criterion for multivariable discrete-time variable structure control is proposed. When a variable structure controller is designed and implemented as a sampled-data system, achieving robustness to uncertainties can become difficult. To ameliorate the situation, an uncertainty estimator, also formulated within the variable structure framework, can be used as an embedded discrete-time variable structure controller. This approach requires a bounded changing rate of the uncertainties to ensure robust stability. However, when uncertainties vary as a function of state variables, which occurs with parametric uncertainties, it is not practical to assume such a bound. In this paper, uncertainties are assumed to consist of external disturbances and parametric uncertainties. An uncertainty compensator is used to deal with the former, and a robust stability criterion is developed using the small gain theorem for the latter.     

BINARY CONTROL OF FERMENTATION PROCESSES

In this paper a binary control for nonlinear and uncertain systems is proposed. In particular, continuous fermentation processes are considered. The control design is carried out with the direct use of a nonlinear model, expert knowledge, and an on-line measurement of output variable only. Asymptotic stability of the closed-loop systems in the presence of parameter uncertainties and external disturbances is guaranteed. Simulation investigations are performed. The results obtained prove that the designed binary control is especially suitable for the stabilization of nonlinear and uncertain fermentation processes.     

Variable structure controller with neural compensator

This paper proposes a new variable structure controller combined with a multilayer neural network using an error back-propagation learning algorithm. The neural network acts as a compensator for a conventional variable structure controller in order to improve the control performance when the initial assumptions of the uncertainty bounds of the system parameters are violated. Also, the proposed controller can reduce the steady-state error of a conventional variable structure controller using the boundary layer technique. Computer simulation results show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.     

A Discrete‐Time VS Controller based on RBF Neural Networks for PMSM Drives

A method merging the features of variable structure control and neural network design is presented for speed control of a permanent magnet synchronous motor. The proposed control approach is based on a discrete‐time variable structure control and a robust digital differentiator for speed estimation. Radial basis function neural networks are used to learn about uncertainties affecting the system. A stability analysis is provided and the ultimate boundedness of the speed tracking error is proved. Control performance has been evaluated by simulations using the model of a commercial permanent magnet synchronous motor drive.     

Design of variable structure control for fuzzy nonlinear systems

In this paper, the variable structure control problem is presented for Takagi–Sugeno fuzzy systems with uncertainties and external disturbances. The sliding surfaces for the T–S fuzzy system are proposed by using a Lyapunov function and a fuzzy Lyapunov function, respectively. And we design the variable structure controllers such that the global T–S fuzzy system confined on the sliding surfaces is asymptotically stable. Two examples are given to illustrate the effectiveness of our proposed methods.     

A robust adaptive fuzzy variable structure tracking control for the wheeled mobile robot: Simulation and experimental results

In this paper an adaptive fuzzy variable structure control (kinematic control) integrated with a proportional plus derivative control (dynamic control) is proposed as a robust solution to the trajectory tracking control problem for a differential wheeled mobile robot. The variable structure controller, based on the sliding mode theory, is a well known, proven control method, fit to deal with uncertainties and disturbances (e.g., structural and parameter uncertainties, external disturbances and operating limitations). To minimize the problems found in practical implementations of the classical variable structure controllers, an adaptive fuzzy logic controller replaces the discontinuous portion of the control signals (avoiding the chattering), causing the loss of invariance, but still ensuring the robustness to uncertainties and disturbances without having any a priori knowledge of their boundaries. Moreover, the adaptive fuzzy logic controller is a feasible tool to approximate any real continuous nonlinear system to arbitrary accuracy, and has a simple structure by using triangular membership functions, a low number of rules that must be evaluated, resulting in a lower computational load for execution, making it feasible for real time implementation. Stability analysis and the convergence of tracking errors as well as the adaptation laws are guaranteed with basis on the Lyapunov theory. Simulation and experimental results are explored to show the verification and validation of the proposed control strategy.