The use of a methodology of nonlinear continuous predictive control to design the guidance control law for the aircraft TF/TA2 trajectory tracking problem is emptojed. For the derivation of the predictive control law,...The use of a methodology of nonlinear continuous predictive control to design the guidance control law for the aircraft TF/TA2 trajectory tracking problem is emptojed. For the derivation of the predictive control law, by using Taylor series expansion, and based on optimizing a performance index which is a quadratic function of both the predictive value of the state variables and the control inputs, a state variable feedback controller for nonlinear systems is obtained, and it provides a tradeoff between satisfactory tracking performance and the control magnitude requirements. Numerical simulation results for a supersonic fighter aircraft model show the viability of this approach.展开更多
This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,...This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,path following dynamics,and system input dynamics.The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics,as well as nonlinear tire forces.The tracking error dynamics are derived based on the curvilinear coordinates.The cost function is designed to minimize path tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits.An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed.The length of the preview path is adaptively adjusted based on the vehicle speed,heading error,and path curvature.We validate the controller performance in a simulation environment with the autonomous racing scenario.The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors.The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.展开更多
A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established...A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.展开更多
Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was...Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.展开更多
Proton exchange membrane fuel cell (PEMFC) stack temperature and cathode stoichiometric oxygen are very important control parameters. The performance and lifespan of PEMFC stack are greatly dependent on the parameters...Proton exchange membrane fuel cell (PEMFC) stack temperature and cathode stoichiometric oxygen are very important control parameters. The performance and lifespan of PEMFC stack are greatly dependent on the parameters. So, in order to improve the performance index, tight control of two parameters within a given range and reducing their fluctuation are indispensable. However, control-oriented models and control strategies are very weak junctures in the PEMFC development. A predictive control algorithm was presented based on their model established by input-output data and operating experiences. It adjusts the operating temperature to 80 ℃. At the same time, the optimized region of stoichiometric oxygen is kept between 1.8?2.2. Furthermore, the control algorithm adjusts the variants quickly to the destination value and makes the fluctuation of the variants the least. According to the test results, compared with traditional fuzzy and PID controllers, the designed controller shows much better performance.展开更多
文摘The use of a methodology of nonlinear continuous predictive control to design the guidance control law for the aircraft TF/TA2 trajectory tracking problem is emptojed. For the derivation of the predictive control law, by using Taylor series expansion, and based on optimizing a performance index which is a quadratic function of both the predictive value of the state variables and the control inputs, a state variable feedback controller for nonlinear systems is obtained, and it provides a tradeoff between satisfactory tracking performance and the control magnitude requirements. Numerical simulation results for a supersonic fighter aircraft model show the viability of this approach.
基金“National Science and Technology Council”(NSTC 111-2221-E-027-088)。
文摘This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,path following dynamics,and system input dynamics.The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics,as well as nonlinear tire forces.The tracking error dynamics are derived based on the curvilinear coordinates.The cost function is designed to minimize path tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits.An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed.The length of the preview path is adaptively adjusted based on the vehicle speed,heading error,and path curvature.We validate the controller performance in a simulation environment with the autonomous racing scenario.The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors.The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.
文摘A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.
文摘Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.
基金Project (2003AA517020) supported by the National High-Tech Research and Development Program of China
文摘Proton exchange membrane fuel cell (PEMFC) stack temperature and cathode stoichiometric oxygen are very important control parameters. The performance and lifespan of PEMFC stack are greatly dependent on the parameters. So, in order to improve the performance index, tight control of two parameters within a given range and reducing their fluctuation are indispensable. However, control-oriented models and control strategies are very weak junctures in the PEMFC development. A predictive control algorithm was presented based on their model established by input-output data and operating experiences. It adjusts the operating temperature to 80 ℃. At the same time, the optimized region of stoichiometric oxygen is kept between 1.8?2.2. Furthermore, the control algorithm adjusts the variants quickly to the destination value and makes the fluctuation of the variants the least. According to the test results, compared with traditional fuzzy and PID controllers, the designed controller shows much better performance.
