Optimal Control of Complex Air-Path Systems for Advanced Diesel Engines

2009 ◽  
Author(s):  
Fengjun Yan ◽  
Benjamin Haber ◽  
Junmin Wang
Keyword(s):  
Optimal Control ◽  
Diesel Engines ◽  
Linear Quadratic Regulator ◽  
Mean Value ◽  
Diffusion Combustion ◽  
Linear Quadratic ◽  
Control Approach ◽  
Engine Model ◽  
Path System ◽  
Linearized Model

This paper describes a linear optimal control approach for advanced diesel engines equipped with complex air-path systems including a dual-loop exhaust gas recirculation (EGR) and a two-stage turbocharger. Such complex air-path systems are instrumental to achieve smooth and stable transient operation of diesel engines running advanced multiple combustion modes such as low temperature diffusion combustion (LTDC) and homogeneous charge compression ignition (HCCI). A mean-value engine model was developed to capture the main dynamics of the advanced air-path system. A linear quadratic regulator (LQR) optimal controller was designed based on a linearized model at a fixed operating point. Simulation results using a high-fidelity detailed GT-Power engine model show the effectiveness of the controller.

Active Control for Power-Train Vibration of Automotive Using Active Mounts

2013 ◽  
Vol 330 ◽  
pp. 598-601
Author(s):  
Guo Chun Sun ◽  
Li Meng He
Keyword(s):  
Optimal Control ◽  
Vibration Isolation ◽  
Linear Quadratic Regulator ◽  
Active System ◽  
Linear Quadratic ◽  
Isolation System ◽  
Vibration Isolation System ◽  
Engine Vibration ◽  
Active Vibration ◽  
Piezoelectric Stack Actuator

In this work, a new active mount featuring piezostack actuators and a rubber element is proposed and applied to a vibration control system. After describing the configuration and operating principle of the proposed mount, an appropriate rubber element and appropriate piezostacks are designed. Through the analysis of the property of the rubber and piezoelectric stack actuator, a mechanical model of the active vibration isolation system with the active mounts is established. An optimal control algorithm is presented for engine vibration isolation system. the controller is designed according to linear quadratic regulator (LQR) theory. Simulation shows the active system has a better consequence in reducing the vibration of the chassis significantly with respect to the ACM and the optimal control than that in the passive system.

scholarly journals Optimal Control for Single Inverted Pendulum Based on Linear Quadratic Regulator

2016 ◽  
Vol 44 ◽  
pp. 02064
Author(s):  
Huan Xin Cheng ◽  
Jun Xi Chen ◽  
Jing Li ◽  
Li Cheng
Keyword(s):  
Optimal Control ◽  
Inverted Pendulum ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic

scholarly journals Sub-Optimal Control of Autonomous Wheel Loader With Approximate Dynamic Programming

2019 ◽  
Author(s):  
Tohid Sardarmehni ◽  
Xingyong Song
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Approximate Dynamic Programming ◽  
A Priori ◽  
Optimal Path ◽  
Mean Value ◽  
Wheel Loader ◽  
Engine Model ◽  
Control Objective ◽  
Wheel Loaders

Abstract Optimal control of wheel loaders in short loading cycles is studied in this paper. For modeling the wheel loader, the data from a validated diesel engine model is used to find a control oriented mean value engine model. The driveline is modeled as a switched system with three constant gear ratios (modes) of −60 for backwarding, 60 for forwarding, and zero for stopping. With these three modes, the sequence of active modes in a short loading cycle is fixed as backwarding, stopping, forwarding, and stopping. For the control part, it is assumed that the optimal path is known a priori. Given the mode sequence, the control objective is finding the optimal switching time instants between the modes while the wheel loader tracks the optimal path. To solve the optimal control problem, approximate dynamic programming is used. Simulation results are provided to show the effectiveness of the solution.

scholarly journals Elevation, pitch and travel axis stabilization of 3DOF helicopter with hybrid control system by GA-LQR based PID controller

2020 ◽  
Vol 10 (2) ◽  
pp. 1868 ◽  
Author(s):  
Ibrahim K. Mohammed ◽  
Abdulla I. Abdulla
Keyword(s):  
Pid Controller ◽  
Hybrid Control ◽  
Research Work ◽  
Linear Quadratic Regulator ◽  
Optimization Method ◽  
Feedback Gain ◽  
Algorithm Optimization ◽  
Linear Quadratic ◽  
Control Approach ◽  
Lqr Controller

This research work presents an efficient hybrid control methodology through combining the traditional proportional-integral-derivative (PID) controller and linear quadratic regulator (LQR) optimal controlher. The proposed hybrid control approach is adopted to design three degree of freedom (3DOF) stabilizing system for helicopter. The gain parameters of the classic PID controller are determined using the elements of the LQR feedback gain matrix. The dynamic behaviour of the LQR based PID controller, is modeled and the formulated in state space form to enable utlizing state feedback controller technique. The performance of the proposed LQR based LQR controller is improved by using Genetic Algorithm optimization method which are adopted to obtain optimum values for LQR controller gain parameters. The LQR-PID hybrid controller is simulated using Matlab environment and its performance is evaluated based on rise time, settling time, overshoot and steady state error parameters to validate the proposed 3DOF helicopter balancing system. Based on GA tuning approach, the simulation results suggest that the hybrid LQR-PID controller can be effectively adopted to stabilize the 3DOF helicopter system.

Numeric Approach on Optimal Control for the Path Following System in Autonomous Vehicle

2019 ◽  
Author(s):  
Huyao Wu ◽  
Bin Ran
Keyword(s):  
Optimal Control ◽  
Control Strategies ◽  
Autonomous Vehicle ◽  
Path Following ◽  
Linear Quadratic Regulator ◽  
State Feedback Control ◽  
Linear Quadratic ◽  
The Road ◽  
Lqr Controller ◽  
Dynamic Errors

Abstract In this paper, the control strategies for Path Following System (PFS) in autonomous vehicle, which lets vehicle stay in the center of its lane is discussed, we will create a plant mechanical, mathematical and error dynamics model for the study of PFS, which is stabilized by the state-feedback control law, also considers the output where the sensor is made. We apply mainly an optimal control or configure a Linear-quadratic Regulator (LQR) for state space systems and compare it to that based on the Pole Assignment (PA). Combined with a typical operating scenario of the road, we mainly consider static and dynamic errors in the moving process, and how intensely the error fluctuates and how errors are related to the next time. Figures and data show that the LQR controller successfully adjusts and gives appropriate input to let the vehicle approach to centerline, errors and the steering angle required to negotiate a curved road are presented and analyzed, finally relevant conclusions are drawn.

Robust Control and Synchronization of Chaotic Systems with Actuator Constraints

2015 ◽  
pp. 1-43 ◽  
Author(s):  
Kouamana Bousson ◽  
Carlos Velosa
Keyword(s):  
Robust Control ◽  
Chaotic System ◽  
Chaotic Systems ◽  
Linear Part ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Aeroelastic System ◽  
Control Approach ◽  
Nonlinear Part ◽  
Actuator Constraints

This chapter proposes a robust control approach for the class of chaotic systems subject to magnitude and rate actuator constraints. The approach consists of decomposing the chaotic system into a linear part plus a nonlinear part to form an augmented system comprising the system itself and the integral of the output error. The resulting system is posteriorly seen as a linear system plus a bounded disturbance, and two robust controllers are applied: first, a controller based on a generalization of the Lyapunov function, then a Linear-Quadratic Regulator (LQR) with a prescribed degree of stability. Numerical simulations are performed to validate the approach applying it to the Lorenz chaotic system and to a chaotic aeroelastic system, and parameter uncertainties are also considered to prove its robustness. The results confirm the effectiveness of the approach, and the constraints are guaranteed as opposed to other control techniques which do not consider any kind of constraints.

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