Real Time Modeling and Control of Three Tank Hybrid System

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
K. Sathishkumar ◽  
V. Kirubakaran ◽  
T. K. Radhakrishnan
Keyword(s):  
Real Time ◽  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Estimation Model ◽  
Linear Quadratic ◽  
Loop Control ◽  
Modeling And Control ◽  
Time Modeling ◽  
Lqr Controller ◽  
Servo Tracking

AbstractThis study discusses the modeling and linear quadratic regulator (LQR) controller based closed loop control of a three tank hybrid (TTH) process. A pseudo random binary signal (PRBS) based excitation data obtained from a real time TTH setup is utilized in validating its first principle model (FPM). Based on top and bottom interactions, various modes prevalent are considered based on steady state physical reachability analysis of the TTH for a given input range for controller design. The FPM is linearized using nominal values of process parameters using Jacobians from each existing mode. LQR controllers are designed for each mode. A supervisory structure is designed for selecting estimation model and controller for each appropriate mode. Results from real time servo tracking and disturbance rejection experiments are discussed.

scholarly journals Simulation of a Steam Injected Gas Turbine Plant Control System

1997 ◽  
Author(s):  
Shusheng Zang ◽  
Jaqiang Pan
Keyword(s):  
Gas Turbine ◽  
Flow Rate ◽  
Controller Design ◽  
Dynamic Performance ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Turbine Plant ◽  
Fuel Flow ◽  
Lqr Controller ◽  
Gas Turbine Plant

The design of a modern Linear Quadratic Regulator (LQR) is described for a test steam injected gas turbine (STIG) unit. The LQR controller is obtained by using the fuel flow rate and the injected steam flow rate as the output parameters. To meet the goal of the shaft speed control, a classical Proportional Differential (PD) controller is compared to the LQR controller design. The control performance of the dynamic response of the STIG plant in the case of rejection of load is evaluated. The results of the computer simulation show a remarkable improvement on the dynamic performance of the STIG unit.

scholarly journals System Identification and LQR Controller Design with Incomplete State Observation for Aircraft Trajectory Tracking

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5354
Author(s):  
Piotr Lichota ◽  
Franciszek Dul ◽  
Andrzej Karbowski
Keyword(s):  
Simulation Model ◽  
Trajectory Tracking ◽  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Atmospheric Conditions ◽  
Nonlinear Transport ◽  
Linear Quadratic ◽  
Likelihood Principle ◽  
Tracking Errors ◽  
Lqr Controller

This paper presents a controller design process for an aircraft tracking problem when not all states are available. In the study, a nonlinear-transport aircraft simulation model was used and identified through Maximum Likelihood Principle and Extended Kalman Filter. The obtained mathematical model was used to design a Linear–Quadratic Regulator (LQR) with optimal weighting matrices when not all states are measured. The nonlinear aircraft simulation model with LQR controller tracking abilities were analyzed for multiple experiments with various noise levels. It was shown that the designed controller is robust and allows for accurate trajectory tracking. It was found that, in ideal atmospheric conditions, the tracking errors are small, even for unmeasured variables. In wind presence, the tracking errors were proportional to the wind velocity and acceptable for small and moderate disturbances. When turbulence was present in the experiment, state variable oscillations occurred that were proportional to the turbulence intensity and acceptable for small and moderate disturbances.

scholarly journals Rotor-Flying Manipulator: Modeling, Analysis, and Control

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Bin Yang ◽  
Yuqing He ◽  
Jianda Han ◽  
Guangjun Liu
Keyword(s):  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Combined System ◽  
Underwater Robots ◽  
Modeling Analysis ◽  
Full State Feedback ◽  
Lqr Controller ◽  
Linearized Model ◽  
And Control

Equipping multijoint manipulators on a mobile robot is a typical redesign scheme to make the latter be able to actively influence the surroundings and has been extensively used for many ground robots, underwater robots, and space robotic systems. However, the rotor-flying robot (RFR) is difficult to be made such redesign. This is mainly because the motion of the manipulator will bring heavy coupling between itself and the RFR system, which makes the system model highly complicated and the controller design difficult. Thus, in this paper, the modeling, analysis, and control of the combined system, called rotor-flying multijoint manipulator (RF-MJM), are conducted. Firstly, the detailed dynamics model is constructed and analyzed. Subsequently, a full-state feedback linear quadratic regulator (LQR) controller is designed through obtaining linearized model near steady state. Finally, simulations are conducted and the results are analyzed to show the basic control performance.

Modeling and Control Design for Active Trailer Steering of Heavy Vehicles

2017 ◽  
Author(s):  
Jesse Brown ◽  
Yuping He ◽  
Haoxiang Lang
Keyword(s):  
Degrees Of Freedom ◽  
Load Transfer ◽  
Linear Quadratic Regulator ◽  
Heavy Vehicles ◽  
Linear Quadratic ◽  
Modeling And Control ◽  
Software Packages ◽  
Lqr Controller ◽  
Control Evaluation ◽  
And Control

This paper presents a linear quadratic regulator (LQR) controller for active trailer steering (ATS) of a tractor-semitrailer. The tractor-semitrailer is modelled as a linear yaw/roll model with 5 Degrees-Of-Freedom (DOF). The linear yaw/roll model is validated with a nonlinear tractor-semitrailer model developed with TruckSim under a simulated single lane-change maneuver. Then, the validated linear yaw/roll model is used to design the LQR controller for ATS. The TruckSim model and the LQR controller are integrated by means of an interface between the software packages of TruckSim and Matlab/Simulink. The LQR controller is assessed using numerical simulation of the TruckSim model with and without the ATS control. Evaluation of the controller is based on the performance measures of the trailer in terms of rearward amplification (RA), peak roll angle, and load transfer ratio (LTR). It is demonstrated that the LQR controller leads to the decrease the peak values of the aforementioned measures by 4.81%, 20.7%, and 33%, respectively.

Modeling and Control of a Construction Machine With a Human-in-the-Loop Assessment in Virtual Reality

2003 ◽  
Author(s):  
Roger C. Fales ◽  
Atul G. Kelkar ◽  
Erik Spencer ◽  
Kurt Chipperfield ◽  
Francis Wagner
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Controller Design ◽  
Dynamic Modelling ◽  
Nonlinear Dynamic Model ◽  
Linear Quadratic ◽  
Modeling And Control ◽  
Human In The Loop ◽  
Load Sensing ◽  
And Control

This paper presents dynamic modelling, control design, simulation results, and real time Virtual Reality (VR)-based human-in-the-loop testing for a wheel loader control system. In particular, a loader with electro-hydraulic actuation is considered. A detailed nonlinear dynamic model is developed for the hydraulic system and the loader linkage. The hydraulic model includes a load sensing pump, valves, and cylinders. The linkage model represents a two degree of freedom loader with lift and tilt functions. An LQG-based (Linear Quadratic Gaussian) robust controller is designed for automatic bucket levelling to assist the operator during the boom motion. The controller design is tested with a nonlinear model in a real-time VR simulation. In this VR simulation, the operator interacts with the model using a joystick input. The loader linkage geometry is displayed to the operator in real time using a VR display.

Design Optimization of Car-Trailer Combinations With Electronic Stability Systems

2014 ◽  
Author(s):  
Tao Sun ◽  
Yuping He
Keyword(s):  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Lateral Stability ◽  
Trial And Error ◽  
Linear Quadratic ◽  
Weighting Factors ◽  
Lqr Controller ◽  
Ct System ◽  
Trial And Error Method ◽  
Error Method

To date, Linear Quadratic Regulator (LQR) controllers based on linear vehicle models have been researched and developed for improving the lateral stability of car-trailer (CT) combinations. However, in the LQR controller design, there is no a systematic way to determine the weighting factors of the performance index. Generally, the weighting factors are selected using trial and error based on designer’s experience. In order to facilitate the LQR controller design, a new method based on a genetic algorithm (GA) is presented to determine the appropriate weighting factors in the LQR controller design. To examine the proposed method, a controller for an active trailer differential braking (ATDB) system of a car-trailer (CT) system is designed and examined. The simulation results indicate that compared with the LQR controller based on the weighting factors derived from the conventional trial and error method, the controller developed using the proposed method exhibits better performance.

scholarly journals Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5538
Author(s):  
Bảo-Huy Nguyễn ◽  
João Pedro F. Trovão ◽  
Ronan German ◽  
Alain Bouscayrol
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Engine Fuel ◽  
Linear Quadratic ◽  
Loop Control ◽  
Parallel Hybrid ◽  
Linear Quadratic Regulation ◽  
Hybrid Electric

Optimization-based methods are of interest for developing energy management strategies due to their high performance for hybrid electric vehicles. However, these methods are often complicated and may require strong computational efforts, which can prevent them from real-world applications. This paper proposes a novel real-time optimization-based torque distribution strategy for a parallel hybrid truck. The strategy aims to minimize the engine fuel consumption while ensuring battery charge-sustaining by using linear quadratic regulation in a closed-loop control scheme. Furthermore, by reformulating the problem, the obtained strategy does not require the information of the engine efficiency map like the previous works in literature. The obtained strategy is simple, straightforward, and therefore easy to be implemented in real-time platforms. The proposed method is evaluated via simulation by comparison to dynamic programming as a benchmark. Furthermore, the real-time ability of the proposed strategy is experimentally validated by using power hardware-in-the-loop simulation.

Robust stabilization control of a spatial inverted pendulum using integral sliding mode controller

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ishan Chawla ◽  
Vikram Chopra ◽  
Ashish Singla
Keyword(s):  
Inverted Pendulum ◽  
Sliding Mode ◽  
Robust Stabilization ◽  
Linear Quadratic Regulator ◽  
Humanoid Robots ◽  
Sliding Mode Controller ◽  
Linear Quadratic ◽  
Integral Sliding Mode ◽  
Wide Range ◽  
Lqr Controller

AbstractFrom the last few decades, inverted pendulums have become a benchmark problem in dynamics and control theory. Due to their inherit nature of nonlinearity, instability and underactuation, these are widely used to verify and implement emerging control techniques. Moreover, the dynamics of inverted pendulum systems resemble many real-world systems such as segways, humanoid robots etc. In the literature, a wide range of controllers had been tested on this problem, out of which, the most robust being the sliding mode controller while the most optimal being the linear quadratic regulator (LQR) controller. The former has a problem of non-robust reachability phase while the later lacks the property of robustness. To address these issues in both the controllers, this paper presents the novel implementation of integral sliding mode controller (ISMC) for stabilization of a spatial inverted pendulum (SIP), also known as an x-y-z inverted pendulum. The structure has three control inputs and five controlled outputs. Mathematical modeling of the system is done using Euler Lagrange approach. ISMC has an advantage of eliminating non-robust reachability phase along with enhancing the robustness of the nominal controller (LQR Controller). To validate the robustness of ISMC to matched uncertainties, an input disturbance is added to the nonlinear model of the system. Simulation results on two different case studies demonstrate that the proposed controller is more robust as compared to conventional LQR controller. Furthermore, the problem of chattering in the controller is dealt by smoothening the controller inputs to the system with insignificant loss in robustness.

Comparison of Control Methods for Two-Link Planar Flexible Manipulator

2017 ◽  
Author(s):  
Joseph Bowkett ◽  
Rudranarayan Mukherjee
Keyword(s):  
Position Control ◽  
Linear Quadratic Regulator ◽  
Flexible Manipulator ◽  
Linear Quadratic ◽  
Large Space ◽  
Loop Control ◽  
Command Shaping ◽  
Aerospace Applications ◽  
Control Command ◽  
Low Stiffness

While the majority of terrestrial multi-link manipulators can be considered in a purely kinematic sense due to their high stiffness, the launch mass restrictions of aerospace applications such as in-orbit assembly of large space structures result in low stiffness links being employed, meaning dynamics can no longer be ignored. This paper seeks to investigate the suitability of several different open and closed loop control techniques for application to the problem of end effector position control with minimal vibration for a low stiffness space based manipulator. Simulations of a representative planar problem with two flexible links are used to measure performance and sensitivity to parameter variation of: model predictive control, command shaping, and command shaping with linear quadratic regulator (LQR) feedback. An experimental testbed is then used to validate simulation results for the recommended command shaped controller.

scholarly journals FULL CAR ACTIVE DAMPING SYSTEM FOR VIBRATION CONTROL

2020 ◽  
Vol 6 (4) ◽  
pp. 1-17
Author(s):  
G. Yakubu ◽  
G. Sani ◽  
S. B. Abdulkadir ◽  
A. A.Jimoh ◽  
M. Francis
Keyword(s):  
Mathematical Model ◽  
Linear Quadratic Regulator ◽  
Active Damping ◽  
Settling Time ◽  
Linear Quadratic ◽  
Body Acceleration ◽  
The Road ◽  
Road Profile ◽  
Lqr Controller ◽  
Mass Displacement

Full car passive and active damping system mathematical model was developed. Computer simulation using MATLAB was performed and analyzed. Two different road profile were used to check the performance of the passive and active damping using Linear Quadratic Regulator controller (LQR)Road profile 1 has three bumps with amplitude of 0.05m, 0.025 m and 0.05 m. Road profile 2 has a bump with amplitude of 0.05 m and a hole of -0.025 m. For all the road profiles, there were 100% amplitude reduction in Wheel displacement, Wheel deflection, Suspension travel and body displacement, and 97.5% amplitude reduction in body acceleration for active damping with LQR controller as compared to the road profile and 54.0% amplitude reduction in body acceleration as compared to the passive damping system. For the two road profiles, the settling time for all the observed parameters was less than two (2) seconds. The present work gave faster settling time for mass displacement, body acceleration and wheel displacement.

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