MODELING, VALIDATION AND OPTIMAL DESIGN OF THE CLAMPING FORCE CONTROL VALVE USED IN CONTINUOUSLY VARIABLE TRANSMISSION

Associated dynamic performance of the clamping force control valve used in continuously variable transmission （CVT） is optimized. Firstly, the structure and working principle of the valve are analyzed, and then a dynamic model is set up by means of mechanism analysis. For the purpose of checking the validity of the modeling method, a prototype workpiece of the valve is manufactured for comparison test, and its simulation result follows the experimental result quite well. An associated performance index is founded considering the response time, overshoot and saving energy, and five structural parameters are selected to adjust for deriving the optimal associated performance index. The optimization problem is solved by the genetic algorithm （GA） with necessary constraints. Finally, the properties of the optimized valve are compared with those of the prototype workpiece, and the results prove that the dynamic performance indexes of the optimized valve are much better than those of the prototype workpiece.

Active suspension with optimal control based on a full vehicle model

The 7-DOF model of a full vehicle with an active suspension is developed in this paper.The model is written into the state equation style.Actuator forces are treated as inputs in the state equations.Based on the basic optimal control theory,the optimal gains for the control system are figured out.So an optimal controller is developed and implemented using Matlab/Simulink,where the Riccati equation with coupling terms is deduced using the Hamilton equation.The all state feedback is chosen for the controller.The gains for all vehicle variables are traded off so that majority of indexes were up to optimal.The active suspension with optimal control is simulated in frequency domain and time domain separately,and compared with a passive suspension.Throughout all the simulation results,the optimal controller developed in this paper works well in the majority of instances.In all,the comfort and ride performance of the vehicle are improved under the active suspension with optimal control.

Generalized Internal Model Robust Control for Active Front Steering Intervention

Because of the tire nonlinearity and vehicle＇s parameters＇uncertainties,robust control methods based on the worst cases,such as H_∞,μsynthesis,have been widely used in active front steering control,however,in order to guarantee the stability of active front steering system（AFS）controller,the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control.In this paper,a generalized internal model robust control（GIMC）that can overcome the contradiction between performance and stability is used in the AFS control.In GIMC,the Youla parameterization is used in an improved way.And GIMC controller includes two sections：a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters＇uncertainties and some external disturbances.Simulations of double lane change（DLC）maneuver and that of braking on split-μroad are conducted to compare the performance and stability of the GIMC control,the nominal performance PID controller and the H_∞controller.Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle＇s parameters variations,H_∞controller is conservative so that the performance is a little low,and only the GIMC controller overcomes the contradiction between performance and robustness,which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller.Therefore,the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system,that is,can solve the instability of PID or LQP control methods and the low performance of the standard H_∞controller.

An optimal filter based MPC for systems with arbitrary disturbances

In this study, a linear model predictive control(MPC) approach with optimal filters is proposed for handling unmeasured disturbances with arbitrary statistics. Two types of optimal filters are introduced into the framework of MPC to relax the assumption of integrated white noise model in existing approaches. The introduced filters are globally optimal for linear systems with unmeasured disturbances that have unknown statistics. This enables the proposed MPC to better handle disturbances without access to disturbance statistics. As a result, the effort required for disturbance modeling can be alleviated. The proposed MPC can achieve offset-free control in the presence of asymptotically constant unmeasured disturbances. Simulation results demonstrate that the proposed approach can provide an improved disturbance ?rejection performance over conventional approaches when applied to the control of systems with unmeasured disturbances that have arbitrary statistics.

Optimal Active Power Dispatching of Microgrid and Distribution Network Based on Model Predictive Control

First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time scale optimal scheduling of the microgrid based on Model Predictive Control（MPC） is then studied, and the optimized genetic algorithm and the microgrid multi-time rolling optimization strategy are used to optimize the datahead scheduling phase and the intra-day optimization phase. Next, based on the three-tier coordinated scheduling architecture, the operation loss model of the distribution network is solved using the improved branch current forward-generation method and the genetic algorithm. The optimal scheduling of the distribution network layer is then completed. Finally, the simulation examples are used to compare and verify the validity of the method.

A novel two-degree-of-freedom spherical ultrasonic motor using three travelling-wave type annular stators

In order to promote the tolerance and controllability of the multi-degree-of-freedom(M-DOF) ultrasonic motor, a novel two-degree-of-freedom(2-DOF) spherical ultrasonic motor using three traveling-wave type annular stators was put forward. Firstly,the structure and working principle of this motor were introduced, especially a spiral spring as the preload applied component was designed for adaptive adjustment. Then, the friction drive model of 2-DOF spherical motor was built up from spatial geometric relation between three annular stators and the spherical rotor which was used to analyze the mechanical characteristics of the motor.The optimal control strategy for minimum norm solution of three stators' angular velocity was proposed, using Moore-Penrose generalized inverse matrix. Finally, a 2-DOF prototype was fabricated and tested, which ran stably and controllably. The maximum no-load velocity and stall torque are 92 r/min and 90 m N·m, respectively. The 2-DOF spherical ultrasonic motor has compact structure, easy assembly, good performance and stable operation.

Improved MPPT Control Strategy for PV Connected to Grid Using IncCond-PSO-MPC Approach

This paper proposes a new hybrid maximum power point tracking(MPPT)control strategy for grid-connected solar systems based on Incremental conductance—Particle Swarm Optimization and Model Predictive Controller(IncCond-PSOMPC).The purpose of the suggested method is to create as much power as feasible from a PV system during environmental changes,then transfer it to the power grid.To accomplish this,a hybrid combination of incremental conductance(IncCond)and particle swarm optimization(PSO)is proposed to locate maximum power,followed by model predictive control(MPC)to track maximum power and control the boost converter to achieve high performance regardless of parameter variations.A two-level inverter,likewise,controlled by Model Predictive Control,is employed to inject the PV power generated.In this application,the MPC is based on minimizing the difference between the reference and prediction powers,which is computed to select the switching state of the inverter.The proposed system is simulated and evaluated in a variety of dynamic conditions using Matlab/Simulink.Results reveal that the proposed control mechanism is effective at tracking the maximum power point(MPP)with fewer power oscillations.

Study of helicopter autorotation landing following engine failure based on a six-degree-of-freedom rigid-body dynamic model

This paper focuses on the prediction of the safe autorotation landing operations of a helicopter following engine failure.The autorotation landing procedure is formulated as a nonlinear optimal control problem based on an augmented six-degree-of-freedom rigid-body flight dynamic model.First,the cost function and constraints are properly selected.The direct transcription approach is then employed to solve the optimal control problem.For a UH-60 helicopter,the optimal solutions with the rigid-body model are compared with those obtained using a two-dimensional point-mass model.It is found that the optimal solutions using the two different models show reasonably good agreement,and furthermore the optimal solutions using the rigid-body model involve the time histories of angular rates and attitudes,lateral velocity and position,as well as pitch controls.Finally the optimal control formulations with different cost functions are proposed for taking account of 1-s time delay and minimum touchdown speed.The calculated control strategies and trajectories are realistic.