The energy management strategy is an important part of a hybrid electrical vehicle design. It is used to improve fuel economy and to sustain a proper battery state of charge by controlling the power components while s...The energy management strategy is an important part of a hybrid electrical vehicle design. It is used to improve fuel economy and to sustain a proper battery state of charge by controlling the power components while satisfying various constraints and driving demands. However, achieving an optimal control performance is challenging due to the nonlinearities of the hybrid powertrain, the time varying constraints, and the dilemma in which controller complexity and real-time capability are generally conflicting objectives. In this paper, a real-time capable cascaded control strategy is proposed for a dual-mode hybrid electric vehicle that considers nonlinearities of the system and complies with all time-varying constraints. The strategy consists of a supervisory controller based on a non-linear model predictive control （MPC） with a long sampling time interval and a coordinating controller based on linear model predictive control with a short sampling time interval to deal with different dynamics of the system. Additionally, a novel data based methodology using adaptive Markov chains to predict future load demand is introduced. The predictive future information is used to improve controller performance. The proposed strategy is implemented on a real test-bed and experimental trials using unknown driving cycles are conducted. The results demonstrate the validity of the proposed approach and show that fuel economy is significantly improved compared with other methods.展开更多
The paper presents a new dual-mode nonlinear model predictive control(NMPC) scheme for continuous-time nonlinear systems subject to constraints on the state and control.The idea of control Lyapunov functions for nonli...The paper presents a new dual-mode nonlinear model predictive control(NMPC) scheme for continuous-time nonlinear systems subject to constraints on the state and control.The idea of control Lyapunov functions for nonlinear systems is used to compute the terminal regions and terminal control laws with some free-parameters in the dual-mode NMPC framework.The parameters of the terminal controller are selected offline to estimate the terminal region as large as possible;and the parameters are optimized online to gain optimality of the terminal controller with respect to given cost functions.Then a dual-mode NMPC algorithm with varying time-horizon is formulated for the constrained system.Recursive feasibility and closed-loop stability of this NMPC are established.The example of a spring-cart is used to demonstrate the advantages of the presented scheme by comparing to the dual-mode NMPC via the linear quadratic regulator(LQR) method.展开更多
Due to the non-standardization and complexity of the farmland environment,it is always a huge challenge for tractors to achieve fully autonomy(work at Self-driving mode)all the time in agricultural industry.Whereas,wh...Due to the non-standardization and complexity of the farmland environment,it is always a huge challenge for tractors to achieve fully autonomy(work at Self-driving mode)all the time in agricultural industry.Whereas,when tractors work in the Tele-driving(or Remote driving)mode,the operators are prone to fatigue because they need to concentrate for long periods of time.In response to these,a dual-mode control strategy was proposed to integrate the advantages of both approaches,i.e.,by combing Self-driving at most of the time with Tele-driving under special(complex and hazardous)conditions through switching control method.First,the state switcher was proposed,which is used for smooth switching the driving modes according to different working states of a tractor.Then,the state switching control law and the corresponding subsystem tracking controllers were designed.Finally,the effectiveness and superiority of the dualmode control method were evaluated via actual experimental testing of a tractor whose results show that the proposed control method can switch smoothly,stably,and efficiently between the two driving modes automatically.The average control accuracy has been improved by 20%and 15%respectively,compared to the conventional Tele-driving control and Self-driving control with low-precision navigation.In conclusion,the proposed dualmode control method can not only satisfy the operation in the complex and changeable farmland environment,but also free drivers from high-intensity and fatiguing work.This provides a perfect application solution and theoretical support for the intelligentization of unmanned farm agricultural machinery with high safety and reliability.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.51005017,51575043&U1564210)
文摘The energy management strategy is an important part of a hybrid electrical vehicle design. It is used to improve fuel economy and to sustain a proper battery state of charge by controlling the power components while satisfying various constraints and driving demands. However, achieving an optimal control performance is challenging due to the nonlinearities of the hybrid powertrain, the time varying constraints, and the dilemma in which controller complexity and real-time capability are generally conflicting objectives. In this paper, a real-time capable cascaded control strategy is proposed for a dual-mode hybrid electric vehicle that considers nonlinearities of the system and complies with all time-varying constraints. The strategy consists of a supervisory controller based on a non-linear model predictive control （MPC） with a long sampling time interval and a coordinating controller based on linear model predictive control with a short sampling time interval to deal with different dynamics of the system. Additionally, a novel data based methodology using adaptive Markov chains to predict future load demand is introduced. The predictive future information is used to improve controller performance. The proposed strategy is implemented on a real test-bed and experimental trials using unknown driving cycles are conducted. The results demonstrate the validity of the proposed approach and show that fuel economy is significantly improved compared with other methods.
基金supported by the National Natural Science Foundation of China(613741 11)Zhejiang Provincial Natural Science Foundation of China(LR17F030004)
文摘The paper presents a new dual-mode nonlinear model predictive control(NMPC) scheme for continuous-time nonlinear systems subject to constraints on the state and control.The idea of control Lyapunov functions for nonlinear systems is used to compute the terminal regions and terminal control laws with some free-parameters in the dual-mode NMPC framework.The parameters of the terminal controller are selected offline to estimate the terminal region as large as possible;and the parameters are optimized online to gain optimality of the terminal controller with respect to given cost functions.Then a dual-mode NMPC algorithm with varying time-horizon is formulated for the constrained system.Recursive feasibility and closed-loop stability of this NMPC are established.The example of a spring-cart is used to demonstrate the advantages of the presented scheme by comparing to the dual-mode NMPC via the linear quadratic regulator(LQR) method.
基金supported in part by the Independent Innovation Project of Agricultural Science and Technology of Jiangsu Province(CX(20)3068)Modern Agricultural Machinery Equipment and Technology Demonstration and Promotion Project of Jiangsu Province(NJ2021-37)+1 种基金National Foreign Experts Program of China(G2021145010L)Science and Technology Project of Suzhou City(SNG2020039)。
文摘Due to the non-standardization and complexity of the farmland environment,it is always a huge challenge for tractors to achieve fully autonomy(work at Self-driving mode)all the time in agricultural industry.Whereas,when tractors work in the Tele-driving(or Remote driving)mode,the operators are prone to fatigue because they need to concentrate for long periods of time.In response to these,a dual-mode control strategy was proposed to integrate the advantages of both approaches,i.e.,by combing Self-driving at most of the time with Tele-driving under special(complex and hazardous)conditions through switching control method.First,the state switcher was proposed,which is used for smooth switching the driving modes according to different working states of a tractor.Then,the state switching control law and the corresponding subsystem tracking controllers were designed.Finally,the effectiveness and superiority of the dualmode control method were evaluated via actual experimental testing of a tractor whose results show that the proposed control method can switch smoothly,stably,and efficiently between the two driving modes automatically.The average control accuracy has been improved by 20%and 15%respectively,compared to the conventional Tele-driving control and Self-driving control with low-precision navigation.In conclusion,the proposed dualmode control method can not only satisfy the operation in the complex and changeable farmland environment,but also free drivers from high-intensity and fatiguing work.This provides a perfect application solution and theoretical support for the intelligentization of unmanned farm agricultural machinery with high safety and reliability.
