scholarly journals Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

2021 ◽  
Vol 11 (21) ◽  
pp. 10493
Author(s):  
Kun Wu ◽  
Jiang Liu ◽  
Min Li ◽  
Jianze Liu ◽  
Yushun Wang
Keyword(s):  
Clustering Algorithm ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Vehicle Model ◽  
Control Effect ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Suspension Control ◽  
Random Weighting ◽  
Weighting Coefficients

The traditional Linear quadratic regulator (LQR) control algorithm depends too much on expert experience during the selection of weighting coefficients. To solve this problem, we proposed a Genetic K-means clustering Linear quadratic (GKL) algorithm. Firstly, a 2-DOF 1/4 vehicle model and road input model are established. The weights of an LQR controller are optimized using a genetic algorithm. Then, a possible weighting space is constructed based on this optimal solution. Random weighting coefficients of each performance index are generated in this space. Next, LQR control for the 1/4 vehicle model is performed, and the simulation data are recorded automatically, with these random weighting values, different road classes, and driving speed. A machine learning dataset is built from these simulations. Finally, a K-means clustering algorithm is used to classify the LQR control active suspension into three performance modes: safety mode, comprehensive mode, and comfort mode. The optimal weighting matrix of each performance mode is determined to satisfy requirements for different types of drivers. The results show that the new GKL algorithm not only improves the suspension control effect but also realizes different performance modes. It can better adapt to the changes in driving conditions and drivers.

scholarly journals Design of Static Output Feedback and Structured Controllers for Active Suspension with Quarter-Car Model

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8231
Author(s):  
Manbok Park ◽  
Seongjin Yim
Keyword(s):  
Output Feedback ◽  
Ride Comfort ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Static Output Feedback ◽  
State Variables ◽  
Linear Quadratic ◽  
Suspension Control ◽  
Quarter Car ◽  
Static Output

This paper presents a method to design active suspension controllers for a 7-Degree-of-Freedom (DOF) full-car (FC) model from controllers designed with a 2-DOF quarter-car (QC) one. A linear quadratic regulator (LQR) with 7-DOF FC model has been widely used for active suspension control. However, it is too hard to implement the LQR in real vehicles because it requires so many state variables to be precisely measured and has so many elements to be implemented in the gain matrix of the LQR. To cope with the problem, a 2-DOF QC model describing vertical motions of sprung and unsprung masses is adopted for controller design. LQR designed with the QC model has a simpler structure and much smaller number of gain elements than that designed with the FC one. In this paper, several controllers for the FC model are derived from LQR designed with the QC model. These controllers can give equivalent or better performance than that designed with the FC model in terms of ride comfort. In order to use available sensor signals instead of using full-state feedback for active suspension control, LQ static output feedback (SOF) and linear quadratic Gaussian (LQG) controllers are designed with the QC model. From these controllers, observer-based controllers for the FC model are also derived. To verify the performance of the controllers for the FC model derived from LQR and LQ SOF ones designed with the QC model, frequency domain analysis is undertaken. From the analysis, it is confirmed that the controllers for the FC model derived from LQ and LQ SOF ones designed with the QC model can give equivalent performance to those designed with the FC one in terms of ride comfort.

Optimal Controller Design For A Railway Vehicle Suspension System Using Particle Swarm Optimization

2012 ◽  
Author(s):  
Arfah Syahida Mohd Nor ◽  
Hazlina Selamat ◽  
Ahmad Jais Alimin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Suspension System ◽  
Railway Vehicle ◽  
Linear Quadratic ◽  
Swarm Optimization ◽  
Suspension Control ◽  
Trial And Error Method

This paper presents the design of an active suspension control of a two–axle railway vehicle using an optimized linear quadratic regulator. The control objective is to minimize the lateral displacement and yaw angle of the wheelsets when the vehicle travels on straight and curved tracks with lateral irregularities. In choosing the optimum weighting matrices for the LQR, the Particle Swarm Optimization (PSO) method has been applied and the results of the controller performance with weighting matrices chosen using this method is compared with the commonly used, trial and error method. The performance of the passive and active suspension has also been compared. The results show that the active suspension system performs better than the passive suspension system. For the active suspension, the LQR employing the PSO method in choosing the weighting matrices provides a better control performance and a more systematic approach compared to the trial and error method. Key words: active suspension control, two–axle railway vehicle, linear quadratic regulator, particle swarm optimization

Lateral Stability Analysis for Car-Trailer Combinations

2016 ◽  
Author(s):  
Eungkil Lee ◽  
Tao Sun ◽  
Yuping He
Keyword(s):  
Stability Analysis ◽  
Linear Stability ◽  
Linear Stability Analysis ◽  
Parametric Study ◽  
Degrees Of Freedom ◽  
Linear Quadratic Regulator ◽  
Lateral Stability ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Ct System

This paper presents a parametric study of linear lateral stability of a car-trailer (CT) combination in order to examine the fidelity, complexity, and applicability for control algorithm development for CT systems. Using MATLAB software, a linear yaw-roll model with 5 degrees of freedom (DOF) is developed to represent the CT combination. In the case of linear stability analysis, a parametric study was carried out using eigenvalue analysis based on a linear yaw-roll CT model with varying parameters. Built upon the linear stability analysis, an active trailer differential braking (ATDB) controller was designed for the CT system using the linear quadratic regulator (LQR) technique. The simulation study presented in this paper shows the effectiveness of the proposed LQR control design and the influence of different trailer parameters.

scholarly journals Signature PSO: A novel inertia weight adjustment using fuzzy signature for LQR tuning

2021 ◽  
Vol 10 (1) ◽  
pp. 308-318
Author(s):  
Achmad Komarudin ◽  
Novendra Setyawan ◽  
Leonardo Kamajaya ◽  
Mas Nurul Achmadiah ◽  
Zulfatman Zulfatman
Keyword(s):  
Linear Quadratic Regulator ◽  
Absolute Error ◽  
Control Parameters ◽  
Optimum Control ◽  
Exploration And Exploitation ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Inertia Weight ◽  
Adjustment Strategy ◽  
Complete Optimization

Particle swarm optimization (PSO) is an optimization algorithm that is simple and reliable to complete optimization. The balance between exploration and exploitation of PSO searching characteristics is maintained by inertia weight. Since this parameter has been introduced, there have been several different strategies to determine the inertia weight during a train of the run. This paper describes the method of adjusting the inertia weights using fuzzy signatures called signature PSO. Some parameters were used as a fuzzy signature variable to represent the particle situation in a run. The implementation to solve the tuning problem of linear quadratic regulator (LQR) control parameters is also presented in this paper. Another weight adjustment strategy is also used as a comparison in performance evaluation using an integral time absolute error (ITAE). Experimental results show that signature PSO was able to give a good approximation to the optimum control parameters of LQR in this case.

scholarly journals DYNAMICS BASED CONTROL OF A SKID STEERING MOBILE ROBOT

2016 ◽  
Vol 9 (2) ◽  
pp. 70 ◽  
Author(s):  
Osama Elshazly ◽  
Hossam Abbas ◽  
Zakarya Zyada
Keyword(s):  
Mobile Robot ◽  
Inverse Dynamics ◽  
Linear Quadratic Regulator ◽  
Nonlinear Controller ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Lqr Control ◽  
Control Schemes ◽  
Skid Steering ◽  
Drive Model

In this paper, development of a reduced order, augmented dynamics-drive model that combines both the dynamics and drive subsystems of the skid steering mobile robot (SSMR) is presented. A Linear Quadratic Regulator (LQR) control algorithm with feed-forward compensation of the disturbances part included in the reduced order augmented dynamics-drive model is designed. The proposed controller has many advantages such as its simplicity in terms of design and implementation in comparison with complex nonlinear control schemes that are usually designed for this system. Moreover, the good performance is also provided by the controller for the SSMR comparable with a nonlinear controller based on the inverse dynamics which depends on the availability of an accurate model describing the system. Simulation results illustrate the effectiveness and enhancement provided by the proposed controller.

Improvement on Ride Comfort of Quarter-Car Active Suspension System Using Linear Quadratic Regulator

2018 ◽  
pp. 431-441
Author(s):  
Sharifah Munawwarah Syed Mohd Putra ◽  
Fitri Yakub ◽  
Mohamed Sukri Mat Ali ◽  
Noor Fawazi Mohd Noor Rudin ◽  
Zainudin A. Rasid ◽  
...  
Keyword(s):  
Ride Comfort ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Suspension System ◽  
Linear Quadratic ◽  
Quarter Car

scholarly journals Online Reinforcement Learning-Based Control of an Active Suspension System Using the Actor Critic Approach

2020 ◽  
Vol 10 (22) ◽  
pp. 8060
Author(s):  
Ahmad Fares ◽  
Ahmad Bani Younes
Keyword(s):  
Reinforcement Learning ◽  
Least Squares Method ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Suspension System ◽  
Recursive Least Squares ◽  
Linear Quadratic ◽  
Reward Function ◽  
Road Profile ◽  
The One

In this paper, a controller learns to adaptively control an active suspension system using reinforcement learning without prior knowledge of the environment. The Temporal Difference (TD) advantage actor critic algorithm is used with the appropriate reward function. The actor produces the actions, and the critic criticizes the actions taken based on the new state of the system. During the training process, a simple and uniform road profile is used while maintaining constant system parameters. The controller is tested using two road profiles: the first one is similar to the one used during the training, while the other one is bumpy with an extended range. The performance of the controller is compared with the Linear Quadratic Regulator (LQR) and optimum Proportional-Integral-Derivative (PID), and the adaptiveness is tested by estimating some of the system’s parameters using the Recursive Least Squares method (RLS). The results show that the controller outperforms the LQR in terms of the lower overshoot and the PID in terms of reducing the acceleration.

FULL-AND REDUCED- ORDER COMPENSATOR FOR INNOSAT ATTITUDE CONTROL

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
Fadzilah Hashim ◽  
Mohd Yusoff Mashor ◽  
Siti Maryam Sharun
Keyword(s):  
State Space ◽  
Attitude Control ◽  
Space Form ◽  
Transfer Functions ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Lqr Control ◽  
Best Value ◽  
Control Scheme

This paper presents a study on the estimator based on Linear Quadratic Regulator (LQR) control scheme for Innovative Satellite (InnoSAT). By using LQR control scheme, the controller and the estimator has been derived for state space form in all three axes to stabilize the system’s performance. This study starts by converting the transfer functions of attitude control into state space form.  Then, the step continues by finding the best value of weighting matrices of LQR in order to obtain the best value of controller gain, K. After that, the best value of L is obtained for the estimator gain. The value of K and L is combined in forming full order compensator and in the same time the reduced order compensator is also formed. Lastly, the performance of full order compensator is compared to reduced order compensator. From the simulation, results indicate that both types of estimators have presented good stability and tracking performance. However, reduced order estimator has simpler equation and faster convergence to zero than the full order estimator. This property is very important in developing a satellite attitude control for real-time implementation.

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