In this paper, a hybrid predictive controller is proposed for a class of uncertain switched nonlinear systems based on high-order differential state observers and Lyapunov functions. The main idea is to design an outp...In this paper, a hybrid predictive controller is proposed for a class of uncertain switched nonlinear systems based on high-order differential state observers and Lyapunov functions. The main idea is to design an output feedback bounded controller and a predictive controller for each subsystem using high-order differential state observers and Lyapunov functions, to derive a suitable switched law to stabilize the closed-loop subsystem, and to provide an explicitly characterized set of initial conditions. For the whole switched system, based on the high-order differentiator, a suitable switched law is designed to ensure the whole closed-loop’s stability. The simulation results for a chemical process show the validity of the controller proposed in this paper.展开更多
A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which i...A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.展开更多
In this paper, a neural network based adaptive prescribed performance control scheme is proposed for the altitude and attitude tracking system of the unmanned helicopter in the presence of state and output constraints...In this paper, a neural network based adaptive prescribed performance control scheme is proposed for the altitude and attitude tracking system of the unmanned helicopter in the presence of state and output constraints. For handling the state constraints, the barrier Lyapunov function and the saturation function are employed. And, the prescribed performance method is used to deal with the flapping angle constraints for the unmanned helicopter. It is proved that the proposed control approach can ensure that all the signals of the resulting closed-loop system are bounded, and the tracking errors are within the prescribed performance bounds for all time. The numerical simulation is given to illustrate the performance of the proposed scheme.展开更多
This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turb...This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.展开更多
针对机械臂在运行过程中因输入输出受限、外界未知干扰以及自身动力学参数不确定性导致控制精度低和抗干扰能力差的问题,提出基于时变正切型障碍李雅普诺夫函数的自适应反步滑模控制方法(adaptive backstepping sliding mode control fo...针对机械臂在运行过程中因输入输出受限、外界未知干扰以及自身动力学参数不确定性导致控制精度低和抗干扰能力差的问题,提出基于时变正切型障碍李雅普诺夫函数的自适应反步滑模控制方法(adaptive backstepping sliding mode control for time-varying tangent barrier Lyapunov,TTBLF-ABSMC)。首先,通过设计饱和补偿系统解决输入受限问题,提高控制系统的稳定性;其次,将外界未知干扰与自身动力学参数不确定性视为复合干扰,设计反馈自适应律对其做出准确估计;同时,采用时变正切型障碍李雅普诺夫函数将位置误差和速度误差限制在时变范围内;最后,通过李雅普诺夫理论分析证明闭环控制系统是有界稳定的。仿真实验结果表明:与采取时变对数型障碍李雅普诺夫函数的控制方法相比,该控制方法使机械臂关节1、2的跟踪误差分别降低了58%、33%;相比于未考虑输入受限方法,该控制方法在关节1、2的轨迹跟踪响应速度分别提升了69%、50%,有效的提高了系统的控制精度和抗干扰能力。展开更多
文摘In this paper, a hybrid predictive controller is proposed for a class of uncertain switched nonlinear systems based on high-order differential state observers and Lyapunov functions. The main idea is to design an output feedback bounded controller and a predictive controller for each subsystem using high-order differential state observers and Lyapunov functions, to derive a suitable switched law to stabilize the closed-loop subsystem, and to provide an explicitly characterized set of initial conditions. For the whole switched system, based on the high-order differentiator, a suitable switched law is designed to ensure the whole closed-loop’s stability. The simulation results for a chemical process show the validity of the controller proposed in this paper.
文摘A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.
基金supported by the National Natural Science Foundation of China (Nos. 61573184, 61751210)Aeronautical Science Foundation of China (No. 20165752049)the Fundamental Research Funds for the Central Universities of China (No. NE2016101)
文摘In this paper, a neural network based adaptive prescribed performance control scheme is proposed for the altitude and attitude tracking system of the unmanned helicopter in the presence of state and output constraints. For handling the state constraints, the barrier Lyapunov function and the saturation function are employed. And, the prescribed performance method is used to deal with the flapping angle constraints for the unmanned helicopter. It is proved that the proposed control approach can ensure that all the signals of the resulting closed-loop system are bounded, and the tracking errors are within the prescribed performance bounds for all time. The numerical simulation is given to illustrate the performance of the proposed scheme.
基金supported by National Natural Science Foundation of China(60904008,61273336)the Fundamental Research Funds for the Central Universities(2018MS025)the National Basic Research Program of China(973 Program)(B1320133020)
文摘This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.
文摘针对机械臂在运行过程中因输入输出受限、外界未知干扰以及自身动力学参数不确定性导致控制精度低和抗干扰能力差的问题,提出基于时变正切型障碍李雅普诺夫函数的自适应反步滑模控制方法(adaptive backstepping sliding mode control for time-varying tangent barrier Lyapunov,TTBLF-ABSMC)。首先,通过设计饱和补偿系统解决输入受限问题,提高控制系统的稳定性;其次,将外界未知干扰与自身动力学参数不确定性视为复合干扰,设计反馈自适应律对其做出准确估计;同时,采用时变正切型障碍李雅普诺夫函数将位置误差和速度误差限制在时变范围内;最后,通过李雅普诺夫理论分析证明闭环控制系统是有界稳定的。仿真实验结果表明:与采取时变对数型障碍李雅普诺夫函数的控制方法相比,该控制方法使机械臂关节1、2的跟踪误差分别降低了58%、33%;相比于未考虑输入受限方法,该控制方法在关节1、2的轨迹跟踪响应速度分别提升了69%、50%,有效的提高了系统的控制精度和抗干扰能力。
