LQR Control Method Research Articles

Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension

In this paper, a hybrid particle swarm optimization genetic algorithm LQR controller is used on a quarter car model with an active suspension system. The proposed control algorithm is utilized to overcome the shortcoming that the weight matrix Q and matrix R determined by experience in the traditional LQR control method. The proposed hybrid control method makes it possible to achieve the optimal control effect. A full-order state observer is proposed to observe the state of active suspension. A quarter car active suspension model and road input model are presented at first, and the LQR controller based on the hybrid particle swarm optimization genetic algorithm is utilized in the active suspension system control. Sprung mass acceleration, suspension deflection, and tire dynamic load are selected as the control effect evaluation index. Next, simulation results are presented. According to the results, compared with the passive suspension and active suspension with a traditional LQR control, there is an obvious reduction in the sprung mass acceleration, deflection, and tire dynamic load with an optimized controller under case 1 and case 2. Simultaneously, the system state fed back by the full-order state observer can effectively reflect the true state of the active suspension system.

Research on path tracking control of unmanned vehicle

With the continuous progress of science and technology, driverless vehicles have become one of the development trends of automobiles in the future. Path tracking control is a key problem in the research of driverless vehicles. Based on one control theory or by combining several different theories, different control algorithms are proposed, and some achievements have been made. This paper mainly studies the tracking control research method, which is a key problem in driverless vehicles. By analyzing and comparing the LQR control method and MPC control method, we compare their advantages and disadvantages, understand the concepts and related algorithms of various path-tracking control methods, and analyze the advantages and disadvantages of various control methods.

Linear Quadratic Optimal Control with the Finite State for Suspension System

The control algorithm could greatly help the suspension system improve the comprehensive performance of the vehicle. Existing control methods need to obtain the intermediate states, which are difficult to obtain directly or accurately when estimated by filters or observers. Thus, this paper proposed a new practical finite state LQR control method to deal with this problem. By combining with the output state of the finite sensor of the vehicle suspension system and weakening the unknown state as the goal, an optimization model is established with the design variables as the LQR weight coefficients. Then, the direct relationship between the current control input and the finite sensor output is obtained, and the finite state LQR control is realized. Taking the quarter-car suspension model as an example, the corresponding noise is added considering sensor accuracy, and the control performance of the four control methods is studied considering the uncertainties of suspension system parameters. In addition, the acceleration of sprung mass and the dynamic travel coefficient of suspension have been separately calculated by methods of finite state LQR control, LQR control, and PID control. The results show that there is not much difference between them under shock excitation or random excitation. However, the finite state LQR control method has the best comprehensive control performance in that its dynamic tire load coefficient is better than other methods; it could take into account the suspension work stroke coefficient, dynamic tire load coefficient, and sprung mass’ acceleration of the vehicle suspension system at the same time. In order to realize the optimal control effect with limited sensor arrangement, the finite state LQR control method only needs to obtain the current sensor output and the current control input, without estimating the unknown intermediate state. By this means, the proposed control method greatly simplifies the design of the control system and has great advantages on practical value.

ACTIVE CONTROL OF VERTICAL ACCELERATION WITH T-FOIL AND TRIM TAB SYSTEMS IN A FAST FERRY

This study is concerned with the active T-foil placed near the bow on the keel line of the fast ferry and two active trim tab controls placed at the stern to improve the maritime performance of a fast ferry, whilst improving the comfort and safety of passengers and crew. In the scope of the study, the vertical direction of the fast ferry under the random head waves, heave and pitch motions were taken into account. For the control of T-foil and trim Tabs, PID and LQR control methods were used. The purpose of these controllers is to reduce the acceleration of the heave and pitch motions of the fast ferry by changing the operating angles of the T-foil and trim tab wings. A random wave model was created using the Pierson-Moskowitz model, and simulations were done assuming that the fast ferry was subjected to random head waves. Finally, in order to see the effect of vertical acceleration on passengers, the rate of seasickness (MSI) change of the fast ferry in uncontrolled and controlled states was examined. Mathematical models of fast ferry, T-foil and trim tab and their simulations were carried out in MATLAB / Simulink environment. The simulation results show that T-foil and trim tab Active systems can effectively reduce vertical acceleration by improving heave and pitch motions.

Pengendalian Sudut Pitch dan Roll Pesawat Flying Wing

The development of the aviation world in recent years has increased, one of which is the field of unmanned aerial vehicles. This development is in line with the increasing use of UAVs in various fields, including for military missions, natural disaster monitoring missions, and aerial photography missions. Many types of aircraft that can be used to complete these missions, one type of aircraft that is widely used is the flying wing type aircraft. This type of aircraft is triangular in shape and does not have a tail. This aircraft controller uses elevons on the right and left sides of its wings, and its form is relatively small, so this aircraft is susceptible to environmental disturbances and causes flight mission failures. To overcome this problem, a control system is needed that is able to make the aircraft maintain flight stability. In this study, the Linear Quadratic Regulator control method was applied to overcome this. The LQR control method is used to control the pitch and roll angles of the aircraft. Based on the test results, the interference given can be handled well by the aircraft. It was proven that when given a disturbance, the aircraft experienced an overshoot of 4.24º at the pitch angle and 4.26º at the roll angle, but could quickly return to the setpoint within 1.2 seconds at the pitch angle, and 1.3 seconds at the roll angle. Thus the aircraft is able to maintain flight stability.

Research on the Safety of Intelligent Vibration Control System for Electric Vehicle Suspension

Aiming at the problem of poor vertical vibration performance of electric vehicles, an integrated design scheme of electric vehicle suspension based on in-wheel vibration damping system is proposed, and the fuzziness between vehicle suspension and in-wheel vibration damping system is carried out for this scheme. control. Then, the LQR control method based on particle swarm optimization weight coefficient is adopted, and the LQR optimization controller is designed to comprehensively control the electric in-wheel vibration damping system and the vehicle suspension, and further optimize the vehicle ride comfort and electric wheel vibration performance. The simulation results show that the inversion control strategy based on the reference model can effectively suppress the vertical motion of the body, reduce the vertical acceleration of the body, and at the same time reduce the tire deformation to a certain extent, and improve the ride comfort and driving stability of the car.

LQR Control with the New Triple In-Loops Algorithm for Optimization of the Tuning Parameters

The suspension system plays a role in ensuring the stability of the vehicle when traveling on the road. On many modern vehicles, the active suspension system has been proposed to replace the conventional passive suspension system. The performance of the controller for the active suspension system depends on its control method. In this paper, a half dynamics model of the vehicle is established. Besides, the LQR control method is also used. The parameters of the control matrix are calculated through the triple in-loop optimization algorithm, which has been shown in the research. This is a completely novel algorithm. This algorithm helps to choose the most optimal parameters. Thus, it ensures the efficiency and stability of the controller. The calculation and comparison process are done automatically. When the loop ends, the optimal parameters are explicitly indicated. The simulation process is done in the MATLAB-Simulink environment. The results of the research showed that when the LQR controller, which was optimized through the triple in-loop algorithm used, the vehicle's oscillation was significantly reduced. In the three survey situations, the values of the roll angle and the angular acceleration of the sprung mass are guaranteed to be stable. Besides, when using this controller, the phenomenon of “chattering” after the excitation ends does not appear. This topic can be further developed in the future.

Active nonlinear vibration control of a membrane solar array structure

Membrane solar array has attracted a lot of attentions in recent years because of the advantage of light-weight, low-cost, and high-folding-ratio. Meanwhile, the membrane solar array structure also pose challenging large-amplitude vibration issue which will impact the performance of the spacecraft significantly. In this paper, active nonlinear vibration control of a membrane solar array structure is studied. Based on the nonlinear finite element method, a nonlinear dynamic model of the structure is established. The optimal positions of piezoelectric actuators are determined by optimizing a controllability optimization criterion with Particle Swarm Optimizer algorithm. An active controller is designed to suppress the undesired nonlinear vibration based on the linearized dynamic model by using the LQR control method. Simulation results show that the designed active controller can suppress the nonlinear vibration of the membrane solar array structure effectively, and the optimally placed actuators can produce better control effect with smaller control inputs.

Research on LQR controller of control torque gyro self-balancing vehicle based on QPSO optimization

Abstract The LQR control method based on quantum particle swarm optimization (QPSO) is used to solve the balance problem of the control torque gyro self-balancing vehicle. Firstly, the system dynamics model was established by Lagrange method, and the fitness function of mixed indexes was designed, and the system was simulated numerically. Finally, compared with the results of double closed-loop PID control and LQR control using empirical method, the performance of each algorithm in vehicle body balance control is analyzed. The results show that the LQR control algorithm based on PSO optimization can quickly adjust the roll Angle from the initial Angle to the balance position, and has a faster response time, smaller overshoot, and a stronger ability to resist external interference for the balance control of the torque gyro balancing vehicle.

Investigation and realisation of PID and LQR control methods in Parrot Mambo minidrone

Investigation and realisation of PID and LQR control methods in Parrot Mambo minidrone

Investigation and realisation of PID and LQR control methods in Parrot Mambo minidrone

Investigation and realisation of PID and LQR control methods in Parrot Mambo minidrone

Bir Quadcopterin PID, Adaptif Ve LQR Kontrol Yöntemleri Kullanılarak Yapılan Dinamik Analizi

In this study, different control methods for controlling the position and angle values for the quadcopter (UAV) that take off and land vertically are simulated with MATLAB/Simulink. First of all, a mathematical model was created with the Newton-Euler method, taking into account the dynamics of the quadcopter system. The focus of the study is to investigate the appropriate control method for the position control of the quadcopter. For the linear model of the quadcopter system, PID, LQR and Adaptive Control methods, and for the nonlinear model of the quadcopter system. PD Control simulation has been done. The mentioned control methods have been applied to the system and the control of the movement of the system in each axis has been examined. The obtained results are compared with each other to see the performance of the controllers and the most appropriate control method for the quadcopter was determined with comparisons and tracking scenarios.

Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control

This paper presents the simulation and calculation-based aspect of constructing a dynamically stable, self-balancing electric monowheel from first principles. It further goes on to formulate a reference model-based adaptive control structure in order to maintain balance as well as the desired output. First, a mathematical model of the nonlinear system analyzes the vehicle dynamics, followed by an appropriate linearization technique. Suitable parameters for real-time vehicle design are calculated based on specific constraints followed by a proper motor selection. Various control methods are tested and implemented on the state-space model of this system. Initially, classical pole placement control is carried out in MATLAB to observe the responses. The LQR control method is also implemented in MATLAB and Simulink, demonstrating the dynamic stability and self-balancing system property. Subsequently, the system considers an extensive range of rider masses and external disturbances by introducing white noise. The parameter estimation of rider position has been implemented using Kalman Filter estimation, followed by developing an LQG controller for the system, in order to mitigate the disturbances caused by factors such as wind. A comparison between LQR and LQG controllers has been conducted. Finally, a reference model-assisted adaptive control structure has been established for the system to account for sudden parameter changes such as rider mass. A reference model stabilizer has been established for the same purpose, and all results have been obtained by running simulations on MATLAB Simulink.

Designing LQR Controllers for an Active Anti-roll Bar System with a Flexible Frame Model of a Single Unit Heavy Vehicle

Rollover accidents of heavy vehicles often cause serious consequences both in terms of vehicle and environmental damage as well the loss or injury of drivers, passengers and ordinary civilians. Currently, the active anti-roll bar system is considered as the most effective solution in enhancing vehicle roll stability. In this paper, we firstly investigated the role of a flexible frame of a single unit heavy vehicle in the rollover process. This approach is an important step forward in the research of the active anti-roll bar system. Then, the LQR control method is applied in designing controllers for the active anti-roll bar control system with this frame model. The active torque of the anti-roll bar system is considered as the control signal. The simulation results in the frequency and time domains with a double lane change maneuver show that the vehicle’s roll stability is improved by over 30 % compared to a vehicle using a passive anti-roll bar system.

Genetic Algorithm Based LQR Control for AGV Path Tracking Problem

In order to study path tracking of AGV, the lateral dynamic model of AGV is established, and the state equation of the system is obtained. In order to make the state equation better adapt to the LQR controller, a lateral error integral is added to the controller. Then an improved state equation is obtained. The energy function is constructed and the LQR controller is designed. In order to find the optimal value of the weight Q and R in the LQR controller, Genetic Algorithm (GA) is introduced so as to realize the optimization of the LQR controller. The simulation results show that compared with the empirical method to determine the Q and R of LQR control, the LQR control method optimized by GA can effectively reduce the overshoot of the system, improve the convergence speed and stability of the system, and obtain a better path tracking effect.

Preventing rollover phenomenon with an active anti-roll bar system using electro-hydraulic actuators: A full car model

This paper discusses the role of the active anti-roll bar system in order to enhance the roll stability of cars, thereby preventing rollover phenomenon in high speed emergency situations. First, an integrated full car model is proposed including the longitudinal, lateral, vertical motions and an electro-hydraulic actuator model. In this full car model, the control signal being the input current which is supplied to the actuators to create the active torques to improve the car's stability. This is the most general model in theory to study this active roll control system and is a big step forward compared to previous related studies. The optimal LQR control method has then been used to synthesize the controller based on the integrated model with 26 degrees of freedom. The criteria used to assess the vehicle roll stability are the sprung mass roll angle and the interactive force between the wheels and the road surface. The simulation results in the frequency domain and the validation in the time domain through the CarSim software's nonlinear car model clearly show the advantages of this active system with an optimal LQR controller in preventing vehicle rollover.

Robust Control of Electric Tail Reduction System: Uncertainty and Performance Index

The electric tail reduction system of the unmanned helicopter contains uncertainty. To solve this problem, a constraint-following approach was applied to design a novel robust control for uncertain mechanical systems. The dynamic model of the uncertain electric tail reduction system was established by combining the load of the electric tail rotor and the flight state of the helicopter. Based on the Udwadia–Kalaba theory, a robust constraint following the control method was proposed to deal with the uncertainty of the system. In addition, to balance the steady-state performance and control cost of the system, a control parameter optimization design method was proposed to minimize the performance index. Furthermore, the unique solution of the optimal parameter can be obtained. Compared with the LQR control method, the effectiveness of the optimization method of robust constraint following control was verified.

Pengendalian Sikap Lateral Pesawat Flying wing Menggunakan Metode LQR

Unnamed Aerial Vehicle (UAV) technology of drone is widely used to carry out various mission such as photography, disaster monitoring, and area mapping. However, crash can be caused by environmental disturbances that hinders the completion of the mission. Moreover, an automatic control system is needed to handle this. In this study, the LQR control method is used to control the eleven and pitch angle so that the drone is able to maintain a stable lateral attitude. This control method has a fast and strong response in reaching a point of balance. Based on the test results, it show that the LQR control method applied is able to control the pitch angle and is able to make the drone maintain a lateral attitude, as evidenced by the disturbance at a pitch 20 ∘ the drone is able to maintain a lateral attitude with overshoot of 4.23 ∘ , risetime of 0,7 second, settling time of 1,2 second with a steady state error trend of 0,78

Çift Ters Sarkaç Sisteminin Kontrolü için PID ve LQR Kontrolcü Tasarımlarının Modellenmesi

Nowadays, stability of the inverted pendulum system is an up-to-date topic in which researchers working on control systems compare control theories and methods. The inverse pendulum systems are unstable and nonlinear systems in terms of controllability. Due to the complexity of the structure and the difficulty of the control process, many advanced control theories can be applied on these systems to improving the performance of the controllers. In this study, PID and LQR controller methods were applied on a double inverted pendulum modeled in MATLAB environment and their controller performances were compared. The results via experimental studies were evaluated on the applicability of PID and LQR control methods.

State Estimation with Reduced-Order Observer and Adaptive-LQR Control of Time Varying Linear System

In this study, a new controller design was created to increase the control performance of a variable loaded time varying linear system. For this purpose, a state estimation with reduced order observer and adaptive-LQR (Linear–Quadratic Regulator) control structure was offered. Initially, to estimate the states of the system, a reduced-order observer was designed and used with LQR control method that is one of the optimal control techniques in the servo system with initial load. Subsequently, a Lyapunov-based adaptation mechanism was added to the LQR control to provide optimal control for varying loads as a new approach in design. Thus, it was aimed to eliminate the variable load effects and to increase the stability of the system. In order to demonstrate the effectiveness of the proposed method, a variable loaded rotary servo system was modelled as a time-varying linear system and used in simulations in Matlab-Simulink environment. Based on the simulation results and performance measurements, it was observed that the proposed method increases the system performance and stability by minimizing variable load effect.