Research on automobile four-wheel steering control system based on yaw angular velocity and centroid cornering angle

In order to improve the handling stability of four-wheel steering (4WS) cars, a two-degree-of-freedom 4WS vehicle dynamics model is constructed here, and the motion differential equation of the system model is established. Based on the quadratic optimal control theory, the optimal control of 4WS system is proposed in this paper. When running at low speed and high speed, through yaw rate feedback control, state feedback control, and optimal control, the 4WS cars are controlled based on yaw rate and centroid cornering angle with MATLAB/Simulink simulation. The result indicates that 4WS control based on the optimal control can improve the displacement of the cars. And, the optimal control of 4WS proposed in this paper can eliminate centroid cornering angle completely compared with other two traditional optimal control methods. Besides, the optimal control enjoys faster response speed and no overshoot happens. In conclusion, the optimal control method proposed in the paper represents better stability, moving track and stability, thereby further enhancing the handling property of cars.

A minimum transmitted load control method for shock isolation mounts with magnetorheological energy absorbers

This paper proposed a minimum transmitted load (MTL) control method for drop-induced shock isolation mounts (SIM) with magnetorheological energy absorbers (MREAs). MTL control method consists of two parts of maximum damping force (MDF) control and one part of constant acceleration (CA) control, which can make the payload stop after fully utilize MREA stroke (soft landing) with minimum transmitted load. The control algorithm of MTL control method is derived in a single-degree-of-freedom (SDOF) system. The relationship between the controllable velocity range of MTL control method and parameters of shock isolation mounts is also derived. An optimal control method selection criterion between Bingham number (BN) control method and MTL control method is developed. The performance of MTL control method and selection criterion are shown by applying to the SIM system with variable drop velocities and system parameters. Results shows that MTL control method has the minimum transmitted load and the selection criterion is feasible.

Comparative Study of Acoustic Parameter Reconstruction by using Optimal Control Method and Inverse Scattering Approach

Reconstruction of acoustic parameter such as acoustic velocity considers as part of inverse problems for mathematical physics and reasonable reconstruction of this parameter will assist solving interrelated problem such as inversion and imaging which are popular in the field of seismic imaging. In this work, we studied and conducted a comparative study between two methods; the optimal control method and inverse scattering approach. In optimal control method we are using conjugate gradient method for reconstructing the desired acoustic parameter while for inverse scattering approach, we are introducing the application of Marchenko integral equation. Furthermore, the numerical results for both approaches are presented for one dimensional problem along with the analysis from this comparative study.

Error propagation model and optimal control method for the quality of remanufacturing assembly

In order to improve the quality of remanufacturing assembly with uncertainty for the sustainability of remanufacturing industry, an error propagation model of the remanufacturing assembly process and its optimal control method are established. First, the state space model of error propagation is established by taking the work-in-process parameter errors of each process as the initial state of the procedure and the parameters of remanufactured parts and operation quantities as the input. Then, the quality control issue of remanufacturing assembly is transformed into a convex quadratic programming with constraints based on this model. Finally, the proposed method is used to control the remanufactured-crankshaft assembly quality. The experimental results show that the axial-clearance consistency and the crankshaft torque are improved, and the one-time assembly success rate of a remanufactured crankshaft is increased from 96.97%to 99.24%. This study provides a theoretical model and method support for the quality control of remanufacturing assembly and has a practical effect on improving the quality of remanufactured products.

Data-Driven Control for Proton Exchange Membrane Fuel Cells: Method and Application

A data-driven optimal control method for an air supply system in proton exchange membrane fuel cells (PEMFCs) is proposed with the aim of improving the PEMFC net output power and operational efficiency. Moreover, a marginal utility-based double-delay deep deterministic policy gradient (MU-4DPG) algorithm is proposed as a an offline tuner for the PID controller. The coefficients of the PID controller are rectified and optimized during training in order to enhance the controller’s performance. The design of the algorithm draws on the concept of marginal effects in Economics, in that the algorithm continuously switches between different forms of exploration noise during training so as to increase the diversity of samples, improve exploration efficiency and avoid Q-value overfitting, and ultimately improve the robustness of the algorithm. As detailed below, the effectiveness of the control method has been experimentally demonstrated.

Critic-observer-based decentralized force/position approximate optimal control for modular and reconfigurable manipulators with uncertain environmental constraints

A critic-observer decentralized force/position approximate optimal control method is presented to address the joint trajectory and contacted force tracking problem of modular and reconfigurable manipulators (MRMs) with uncertain environmental constraints. The dynamic model of the MRM systems is formulated as an integration of joint subsystems via extensive state observer (ESO) associated with the effect of interconnected dynamic coupling (IDC). A radial basis function neural network (RBF-NN) is developed to deal with the IDC effects among the independent joint subsystems. Based on adaptive dynamic programming (ADP) approach and policy iteration (PI) algorithm, the Hamilton–Jacobi–Bellman (HJB) equation is approximately solved by establishing critic NN structure and then the approximated optimal control policy can be derived. The closed-loop manipulator system is proved to be asymptotic stable by using the Lyapunov theory. Finally, simulation results are provided to demonstrate the effectiveness and advantages of the proposed control method.

Optimal Liquidation Behaviour Analysis for Stochastic Linear and Nonlinear Systems of Self-Exciting Model with Decay

When the market environment changes, we extend the self-exciting price impact model and further analysis of investors’ liquidation behaviour. It is assumed that the model is accompanied by an exponential decay factor when the temporary impact and its coefficient are linear and nonlinear. Using the optimal control method, we obtain that the optimal liquidation behaviours satisfy the second-order nonlinear ODEs with variable coefficients in the case of linear and nonlinear temporary impact. Next, we solve the ODEs and get the form of the investors’ optimal liquidation behaviour in four cases. Furthermore, we prove the decreasing properties of the optimal liquidation behaviour under the linear temporary impact. Through numerical simulation, we further explain the influence of the changed parameters ρ , a , b , x , and α on the investors’ liquidation strategy X t in twelve scenarios. Some interesting properties have been found.