Variable structure control with sliding mode prediction for discrete-time nonlinear systems

A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining feedback correction and receding horizon optimization methods which are extensively applied on predictive control strategy, a discrete-time variable structure control law is constructed. The closed-loop systems are proved to have robustness to uncertainties with unspecified boundaries. Numerical simulation and pendulum experiment results illustrate that the closed-loop systems possess desired performance, such as strong robustness, fast convergence and chattering elimination.     

SLIDING MODE PREDICTION TRACKING CONTROL DESIGN FOR UNCERTAIN SYSTEMS

In this paper, a tracking control algorithm based on sliding mode prediction for a class of discrete‐time uncertain systems is presented. By creating a special model to predict the future sliding mode function value and by combining feedback correction and receding horizon optimization approaches, which are extensively applied in predictive control strategy, a discrete‐time sliding mode control law for tracking problem is constructed. With the designed control law, closed‐loop systems have strong robustness to matched or unmatched uncertainties as they eliminate chattering. Besides, in the robustness analysis, the boundary condition for uncertainties, which is a universal presupposition in sliding mode control method, is not required. Numerical simulation and cart‐pendulum experiment results illustrate the validity of the proposed algorithm.     

带有内动态的离散不确定非线性系统的滑模预测跟踪控制

对于一类带有内动态的单输入-单输出不确定离散非线性系统,基于滑模预测控制技术设计了一个控制器.通过反馈校正和滚动优化技术,可以及时补偿不确定性的影响,提高了匹配和不匹配不确定项的鲁棒性.然后,通过滚动优化技术得到期望的滑模控制律.特别地,通过预测控制,滑模控制的抖振现象可以消除.最后,在不确定项的界未知的情况下,得到闭环系统的所有信号都是有界的,并且跟踪误差是鲁棒稳定的.仿真例子说明所提出控制方法的有效性.     

基于离散滑模预测的欠驱动AUV三维航迹跟踪控制

针对欠驱动自主水下航行器（AUV）的模型不确定和外界海流干扰问题,为了实现欠驱动AUV的三维航迹跟踪控制,采用虚拟向导法建立空间运动误差离散化模型.基于递归滑模思想设计离散滑模预测控制器,利用滚动优化和反馈校正方法补偿了不确定项对滑模预测模型的影响.最后针对某欠驱动AUV进行了空间曲线跟踪控制仿真实验.结果表明,所设计的控制器可以较好地克服时变非线性水动力阻尼对系统的影响,并对外界海流干扰有较好的抑制作用,保证了欠驱动AUV三维航迹跟踪系统的鲁棒性,实现了三维航迹的精确跟踪.     

非线性系统RBF神经网络多步预测控制

针对较强非线性的控制问题, 提出一种以RBF 神经网络为模型的多步预测控制方法. 构建多步预测模型, 并给出预测误差关于控制序列的雅可比矩阵的计算方法. 利用Levenberg-Marquardt(L-M) 算法设计滚动优化策略, 过误差修正参考输入的方法实现了反馈校正, 证明了控制系统的稳定性. 仿真结果表明所提出的控制方法效果较好.

     

单值预估离散滑模控制及其应用

提出基于单值预估离散滑模控制算法的离散交结构控制系统设计新思路．根据不确定系统的名义模型设计理想滑模面,以名义模型作为预测模型,利用当前及过去时刻的滑模信息预测未来时刻的滑模动态,并将滚动优化和反馈校正引入离散滑模控制系统的设计．该方法不仅较好地消除了抖振现象,而且能保证闭环系统的鲁棒稳定性．将该算法应用于船舶航向控制器的设计,试验结果表明了它的有效性．     

基于幂次函数的预测滑模控制算法

针对一类不满足匹配条件的不确定离散系统,利用全程滑模面设计了切换函数的预测模型,利用幂次函数趋近律设计了参考轨迹,结合反馈校正和滚动优化技术得到了一种离散滑模控制算法。理论分析和数值仿真均表明,该算法不需要知道系统的不确定性上界,对于不满足匹配条件的不确定性系统具有强鲁棒性,可以使切换函数无抖振的收敛于与外干扰相关的某一稳态值,而且被控系统表现出了良好的动态品质。     

基于RBF神经网络的改进多变量预测控制

针对一类多输入多输出非线性被控对象,提出一种基于单神经网络的预测控制算法,应用RBF神经网络对非线性系统进行辨识,并计算被控系统多步预测输出值.该方法通过对传统预测目标函数加以改进,给出一种带微分项的多步预测目标函数,通过迭代寻优实时给出优化控制量.该方法实时性好,简化了传统预测控制算法,加快了滚动寻优的速度,有效地抑制了系统惯性和输入时滞所带来的超调,减小了模型误差、干扰及不确定性对控制器的影响.仿真及应用结果表明了该方法的有效性.     

带终端滑模约束的非线性模型预测控制方法

将预测控制和滑模控制结合起来,提出一种非线性性模型预测控制方法。给出一种可行的双模控制方案,系统状态位于终端区外时采用提出的预测控制,在终端区内部采用高线设计的滑模控制。对系统终端滑模附加不等式约束,使得系统状态在预测时域的末端位于高线设计的滑动模态区内,从而使预测时域减小。仿真结果表明了算法的有效性。     

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。     

分片线性逼近及其在预测控制中的应用

介绍了分片线性逼近的相关理论并将其应用于预测控制。自适应链接超平面模型(AHH)是一种具有应用潜力的分片线性模型。采用AHH模型对被控制系统进行建模,由于AHH模型的辨识算法是自适应的,整个过程简单易实现。随后,在线解一个开环优化问题得到最优控制序列并应用滚动优化控制策略对系统进行控制。并且证明此开环优化问题实质上可以看成一系列子问题,每个子问题都是二次规划问题,因此,全局最优解的存在性得以保证。对于实际问题,提出了一个下降算法用以搜索局部最优解,仿真结果表明,基于AHH模型的预测控制具有一定的应用前景。     

神经网络预测控制局部优化初值确定方法

为解决局部优化算法初值选取不当造成神经网络预测控制性能下降的问题,本文提出了一种动态确定初值的方法.在每次优化时通过逆网络将初值选在输出误差最小点,通过修正目标性能函数中的权重因子来确保初值与当前控制量之间存在极值,并在理论上进行了证明.以BP神经网络预测控制为例,采用牛顿拉夫逊算法实现滚动优化,对所提方法进行了仿真实验,结果表明能够解决初值问题,提高控制系统的可靠性.     

GBF-CMAC和滑模控制的柔性结构系统控制

针对一类不确定系统的跟踪控制,设计了一种将GBF-CMAC（cerebellar model articulation controller with Gauss basis function）与滑模控制相结合的控制系统。利用符号距离和分层结构减少了神经网络所需存储器的数量,并提出了一种神经网络参数的自适应学习律。将设计的控制器用于含有不确定性和欠驱动结构的高阶柔性直线结构系统的跟踪控制,并与一般滑模控制和积分滑模控制进行了比较。实验结果表明,所设计的控制器不仅具有较好的鲁棒性,而且改善了滑模控制存在的抖振问题。同时通过调整神经网络的参数对抖振进行控制,实现了抖振和跟踪性能之间的最优选择。     

面向数据包丢失补偿的网络化广义预测控制

针对网络化控制系统中存在的数据包丢失,考虑了基于状态空间模型的网络化广义预测控制问题;在假设反馈通道和控制通道的数据包丢失过程确定可知的情况下,提出了一种采用预测器和预测控制器分别补偿反馈通道和控制通道的数据包丢失对系统性能影响的方法,通过把广义预测控制问题转化为滚动线性二次型最优跟踪问题,基于动态规划给出了网络化广义预测控制器的设计方法,并基于Ricoati差分方程非负定解的单调性,给出了末端加权矩阵保证系统稳定性的充分条件,最后通过仿真验证了所提出方法的有效性.     

Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism

It is well known that the major cause of instability in industrial cement ball mills is the so-called plugging phenomenon. A novel neural network adaptive control scheme for cement milling circuits that is able to fully prevent the mill from plugging is presented. Estimates of the one-step-ahead errors in control signals are calculated through a neural predictive model and used for controller tuning. A robust on-line learning algorithm, based on sliding mode control (SMC) theory is applied to both: to the controller and to the model as well. The proposed approach allows handling of mismatches, uncertainties and parameter changes in the model of the mill. The simulation results from indicate that both the neural model and the controller inherit the major advantages of SMC, i.e. robustness. Furthermore, learning is achieved in a rapid manner.     

Optimization over state feedback policies for robust control with constraints

This paper is concerned with the optimal control of linear discrete-time systems subject to unknown but bounded state disturbances and mixed polytopic constraints on the state and input. It is shown that the class of admissible affine state feedback control policies with knowledge of prior states is equivalent to the class of admissible feedback policies that are affine functions of the past disturbance sequence. This implies that a broad class of constrained finite horizon robust and optimal control problems, where the optimization is over affine state feedback policies, can be solved in a computationally efficient fashion using convex optimization methods. This equivalence result is used to design a robust receding horizon control (RHC) state feedback policy such that the closed-loop system is input-to-state stable (ISS) and the constraints are satisfied for all time and all allowable disturbance sequences. The cost to be minimized in the associated finite horizon optimal control problem is quadratic in the disturbance-free state and input sequences. The value of the receding horizon control law can be calculated at each sample instant using a single, tractable and convex quadratic program (QP) if the disturbance set is polytopic, or a tractable second-order cone program (SOCP) if the disturbance set is given by a 2-norm bound.     

Receding horizon output feedback control for constrained uncertain systems using periodic invariance

In this article, we consider a receding horizon output feedback control (RHOC) method for linear discrete-time systems with polytopic model uncertainties and input constraints. First, we derive a set of estimator gains and then we obtain, on the basis of the periodic invariance, a series of state feedback gains stabilising the augmented output feedback system with these estimator gains. These procedures are formulated as linear matrix inequalities. An RHOC strategy is proposed based on these state feedback and state estimator gains in conjunction with their corresponding periodically invariant sets. The proposed RHOC strategy enhances the performance in comparison with the case in which static periodic gains are used, and increases the size of the stabilisable region by introducing a degree of freedom to steer the augmented state into periodically invariant sets.     

Fault-tolerant control strategy for multicylinder hydraulic press machine based on dynamic control allocation and adjustable multiobjective optimization

A hierarchical controller is proposed for achieving high-accuracy control and the dynamic balance with the presence of multiple faults of actuator, the external disturbance, and the model uncertainties in multicylinder hydraulic press machine (MCHPM). The method divides the controller design into three steps: Virtual fault-tolerant control law, control allocation algorithm, and actuator control law, which are progressive. First, to precisely compensate the lumped disturbances including the multiple faults of actuator, the external disturbance, and the model uncertainties, a disturbance observer (DO) is developed. By combining the observer with the sliding mode control (SMC), a virtual fault-tolerant control law is designed. Second, a highly integrated control allocation algorithm for the virtual fault-tolerant control law is proposed to get the desired driving force, taking into account dynamic control allocation (DCA), multiobjective optimization (MOO) and Analytic Hierarchy Process (AHP) simultaneously. Third, taking the driving force obtained from above control allocation algorithm as the desired target, the control law of each cylinder is calculated. The global stability for the whole system is proved by the Lyapunov theory. Lastly, results of simulation and experiment show that the proposed controller can effectively handle different faults and have more superior control performance.     

基于终端不变集的 Markov 跳变系统约束预测控制

针对离散 Markov 跳变系统, 研究带输入输出约束的有限时域预测控制问题. 对于给定预测时域内的每条模态轨迹, 设计控制输入序列, 驱动系统状态到达相应的终端不变集内, 在预测时域外, 则寻求一个虚拟的状态反馈控制器以保证系统的随机稳定性, 在此基础上, 分别给出了以线性矩阵不等式 (LMI) 描述的带输入、输出约束预测控制器的设计方法.     

Online learning in adaptive neurocontrol schemes with a slidingmode algorithm

The novel features of an adaptive PID-like neurocontrol scheme for nonlinear plants are presented. The controller tuning is based on an estimate of the command-error on its output by using a neural predictive model. A robust online learning algorithm, based on the direct use of sliding mode control (SMC) theory is applied. The proposed approach allows handling of the plant-model mismatches, uncertainties and parameters changes. The results show that both the plant model and the controller inherit some of the advantages of SMC, such as high speed of learning and robustness.