scholarly journals OPTIMAL CONTROLLER DESIGN FOR ACTIVE SUSPENSION SYSTEM ON CARS

2020 ◽  
Vol 225 (13) ◽  
pp. 107-113
Author(s):  
Vũ Văn Tấn
Keyword(s):  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Suspension System ◽  
Optimal Controller ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian

Hệ thống treo là một trong những bộ phận quan trọng nhất trong thiết kế ô tô và là yếu tố quyết định đến sự thoải mái của lái xe, hành khách (độ êm dịu) và giữ được bám giữa lốp và mặt đường (độ an toàn). Bài báo này giới thiệu một mô hình ¼ ô tô có 2 bậc tự do sử dụng hệ thống treo chủ động với hai bộ điều khiển tối ưu: linear quadratic regulator và linear quadratic gaussian (linear quadratic regulator kết hợp với bộ quan sát Kalman-Bucy). Bằng cách sử dụng bộ quan sát Kalman-Bucy, số lượng cảm biến dùng để đo đạc các tín hiệu đầu vào của bộ điều khiển linear quadratic regulator đã được giảm thiểu tối đa chỉ còn các cảm biến thông thường như gia tốc của khối lượng được treo. Độ êm dịu và an toàn chuyển động khi ô tô sử dụng hệ thống treo chủ động được so sánh với ô tô sử dụng hệ thống treo bị động thông thường thông qua dịch chuyển của khối lượng được treo và gia tốc của nó. Kết quả mô phỏng đã thể hiện rõ giá trị sai lệch bình phương trung bình của gia tốc dịch chuyển thân xe với hệ thống treo tích cực điều khiển tối ưu linear quadratic regulator, linear quadratic gaussian đã giảm khoảng 20% so với ô tô sử dụng hệ thống treo bị động.

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.

Stability Robustness of LQ and LQG Active Suspensions

1994 ◽  
Vol 116 (1) ◽  
pp. 123-131 ◽  
Author(s):  
A. G. Ulsoy ◽  
D. Hrovat ◽  
T. Tseng
Keyword(s):  
Controller Design ◽  
Active Suspension ◽  
Point Of View ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Active Suspensions ◽  
Quarter Car Model ◽  
Stability Robustness ◽  
Trade Offs ◽  
Two Degree Of Freedom

A two-degree-of-freedom quarter-car model is used as the basis for linear quadratic (LQ) and linear quadratic Gaussian (LQG) controller design for an active suspension. The LQ controller results in the best rms performance trade-offs (as defined by the performance index) between ride, handling and packaging requirements. In practice, however, all suspension states are not directly measured, and a Kalman filter can be introduced for state estimation to yield an LQG controller. This paper (i) quantifies the rms performance losses for LQG control as compared to LQ control, and (ii) compares the LQ and LQG active suspension designs from the point of view of stability robustness. The robustness of the LQ active suspensions is not necessarily good, and depends strongly on the design of a backup passive suspension in parallel with the active one. The robustness properties of the LQG active suspension controller are also investigated for several distinct measurement sets.

scholarly journals Optimal Controller Design for the System of Ball-on-sphere: The Linear Quadratic Gaussian (LQG) Case

2021 ◽  
Vol 11 (2) ◽  
pp. 14-30
Author(s):  
Usman Mohammed ◽  
◽  
Tologon Karataev ◽  
Omotayo O. Oshiga ◽  
Suleiman U. Hussein
Keyword(s):  
Controller Design ◽  
Optimal Controller ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian

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

Intelligent optimal controller design applied to quarter car model based on non-asymptotic observer for improved vehicle dynamics

2020 ◽  
pp. 095965182095883
Author(s):  
Maroua Haddar ◽  
Riadh Chaari ◽  
S Caglar Baslamisli ◽  
Fakher Chaari ◽  
Mohamed Haddar
Keyword(s):  
Control Method ◽  
Controller Design ◽  
Operational Calculus ◽  
Algebraic Theory ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Linear Quadratic ◽  
Model Based ◽  
The Stability ◽  
Proportional Derivative Controller

A novel active suspension control strategy is introduced to improve dynamic response of vehicle suspension systems. The proposed algorithm is a fusion of classical controller design methods together with an online observer and is based on the cancelation of system disturbances. The operational calculus method and the differential algebraic theory are applied to build the observer/compensator that is appended to the classical linear quadratic regulator. An ultra-local model based on linear algebraic rules is presented avoiding the use of a precise mathematical model while guaranteeing the stability of the overall system. Simplicity of implementation, low power demand and significant enhancement of active suspension performance are the observed features of the proposed controller. The numerical simulations illustrate the effectiveness and the robustness against sprung mass variation of the proposed control method compared to proportional–integral–derivative controller, intelligent proportional–derivative controller, linear quadratic regulator and active disturbance rejection—linear quadratic regulator.

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