LQR Controller Research Articles

MOPSO TABANLI LQG SERVO KONTROL YAKLAŞIMI İLE ARAÇ ÜZERİNDE KONUMLU TERS SARKACIN KONTROLÜ

The Inverted Pendulum, located on the vehicle, is widely used in the academic sense for the application of various control methods and comparison of their performance. An unstable and non-linear Inverted Pendulum is sensitive and fragile to system disturbances and measurement noises. Exposure to disturbances and sensor measurement noise adversely affects the performance of control systems and causes a decrease in control quality. One of the methods used as a solution to the mentioned problem is the control design method known as Linear Quadratic Gaussian (LQG), which is a combination of Kalman Filter and LQR control.
 
 In this study, for the Inverted Pendulum located on the vehicle, which is exposed to system disturbances and sensor noise, while the vehicle carrying the pendulum follows a given reference, the pendulum is required to maintain its unstable vertical equilibrium position. System disturbances and sensor noises are chosen as Gaussian White Noise. LQG servo control approach is adopted to provide reference tracking and maintain stability around the balance point. It is necessary to optimize the performance of the controller to be used in order to best meet the control requirements. For this purpose, the performance criterion weight matrices for the LQI block within the LQG servo controller have been optimized with the help of the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO).

Robust Control and Thermal Analysis of a Reduced Model of Kirchhoff Composite Plate with Random Distribution of Thermopiezoelectric Sensors and Actuators

This paper presents an implementation of a robust control LQG-Kalman model applied to composite Kirchhoff plate dynamics. A reduced model of a finite element method and control procedure is considered in the modeling of a structure because of the important number of piezoelectric patches used in control. Replacing the full model with a short model reduces the computational and time costs, especially when the number of degrees of freedom is significant. In robust control, the measurement of all states is not necessary and the observability and estimability criteria can be exploited, while conventional LQR control assumes that the data accessibility of all states is available. For this reason, robust control is proposed to control the random external disturbances and is compared to LQR control to illustrate its practicability and efficiency. The sensors and actuators in the thermo-piezoelectric material are randomly distributed on both sides of the plate to establish the control procedure. A Monte Carlo simulation is used in the selection of the degrees of freedom of sensors presenting high electrical outputs. Numerical simulations are performed to demonstrate the effectiveness of the proposed control procedure in a reduced model and under mechanical and thermal disturbances in comparison with the LQR control.

Online reinforcement learning with passivity-based stabilizing term for real time overhead crane control without knowledge of the system model

Due to the existing uncertainties such as the payload mass and unmodeled dynamics in the overhead crane system, classical model-based control methods yielding fixed control gain can exhibit certain limitations. In this study, a novel model-free online Reinforcement Learning (RL) control method is proposed for the real-time overhead crane position regulation and anti-swing control problem, combining the benefits of adaptive control and optimal control. The crane control problem is first formulated as an optimal regulation problem with a user-specified objective function. Then, an improved neural-network updating rule with an additional passivity-based stabilizing term is developed to ensure the system stability during learning based on the overhead crane system passivity analysis. The proposed method, unlike other online RL algorithms, does not require the initial stabilizing control policy or prior knowledge of the crane mathematical model. Lyapunov approach is used to prove the closed-loop system stability, and the learned controller is shown to be near-optimal within a finite bound. Finally, simulation studies are conducted to demonstrate the effectiveness of the proposed method in the presence of system parameter variations and external disturbances, exhibiting satisfactory performance when compared to LQR and passivity-based control.

Dynamic Analysis and Control for a Bioreactor in Fractional Order

In this paper, a mathematical model was developed to describe the dynamic behavior of a bioreactor in which a fermentation process takes place. The analysis took into account the bioreactor temperature controlled by the refrigerant fluid flow through the reactor jacket. An optimal LQR control acting in the water flow through a jacket was used in order to maintain the reactor temperature during the process. For the control design, a reduced-order model of the system was considered. Given the heat transfer asymmetry observed in reactors, a model considering the fractional order heat exchange between the reactor and the jacket using the Riemann–Liouville differential operators was proposed. The numerical simulation demonstrated that the proposed control was efficient in maintaining the temperature at the desired levels and was robust for disturbances in the inlet temperature reactor. Additionally, the proposed control proved to be easy to apply in real life, bypassing the singularity problem and the difficulty of initial conditions for real applications that can be observed when considering Riemann–Liouville differential operators.

LQR Controller for Stabilization of Bio-Inspired Flapping Wing UAV in Gust Environments

LQR Controller for Stabilization of Bio-Inspired Flapping Wing UAV in Gust Environments

Online reinforcement learning for the shape morphing adaptive control of 4D printed shape memory polymer

Combining 3D printing and smart materials, 4D printing technologies enable the printed actuators to further change their shapes or other properties after prototyping. However, the shape morphing of 4D printed actuators suffers from poor controllability and low precision. One of the main challenges is that the 4D printed actuators are hard to be modeled and it is difficult to develop an appropriate controller for them. In this study, various popular reinforcement learning (RL) methods are applied to address the problem of online and adaptive model-free control of 4D printed shape memory polymer (SMP). Their training efficiencies are compared and an adaptive LQR controller based on Q learning is developed to realize efficient online learning. The RL controller achieves precise and quick shape control within 2−−3 learning episodes and is adaptive to the changing properties of SMP. The RL controller performance is then compared with a model-based LQR controller and shows high control precision and excellent adaptability to the varying control plant.

Study on the LQR Control of High-speed Elevator Car Horizontal Vibration Based on the Jumping Inertia Weight Particle Swarm Optimization

To reduce the horizontal vibration of a high-speed elevator car caused by the excitation of a guide rail more effectively, a differential magnetic suspension active guide shoe based on the principle of the differential magnetic suspension actuator is designed. Then the dynamic model of the active vibration damping system of the elevator car is established and an LQR controller is designed to reduce the horizontal vibration of the car. To search the weighting coefficient matrix of the LQR controller more efficiently, an algorithm named Jumping inertia Weight Particle Swarm Optimization (JWPSO) algorithm is proposed, and the frequently used fitness function verifies the optimization effect of the JWPSO algorithm. The weighting coefficient matrix of the LQR controller is optimized using the JWPSO algorithm. Finally, the impact of the JWPSO algorithm-optimized LQR controller is verified by MATLAB. The simulation result shows that the designed active vibration controller can effectively attenuate the horizontal vibration of the elevator car, and the control effect is significantly better than the LQR controller optimized by the GA algorithm and the H-inf controller optimized by LMI. This paper provided a new method for horizontal vibration reduction of the high-speed elevator car.

Comparative analysis of full car model with driver using PID and LQR controllers

In this study, an active suspension model is proposed to increase the driving safety and passenger comfort of the car, which is adversely affected by car-road interaction. In this context, the full car model with a driver is considered as eight degrees of free-dom. All the motion equations of the car were determined by the Lagrangian method and converted into state-space forms. Then, these equations are solved precisely by us-ing Euler's method in Matlab software. PID and LQR controllers are designed to reduce the vertical displacement of the passenger and the rotational movements of the car. In order to evaluate the performance of the designed controllers, two different road inputs and two different car speeds are taken into account. In the simulation results, the verti-cal displacement and acceleration values of the driver and the rotational movements of the car were examined. While the PID controller stands out in damping the dis-placement values at low speeds, the LQR controller is much more successful in other cases.

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.

루프 형성기법을 이용한 필터보상형 LQ-PID 제어기 설계

System control requires high-speed response performance, stability, and robustness. However, tuning controller design parameters for high-speed response performance, stability, and robustness is a challenging task. The LQ-PID controller design parameter tuning method proposed in this paper satisfies the design condition since it ensures stability and robustness, which are the advantages of LQR control and the design weighting factors Q and R are selected using the loop shaping method. Since the proposed method achieves the characteristic of avoiding the high-and the low-frequency region through the setting of the filter coefficients included in the two zero points and the differential time term of the PID controller. it satisfies the performance in the frequency domain with improved features compared to the existing LQ-PID controller design parameter tuning method.

Directional Control of Wheel Syncronization in Vehicle Steer by Wire System

In conventional steering system a drive directly control the movement of the front wheel by control the steering wheel through a mechanical column shaft. However, in steer by wire (SBW) system, the front wheel is control by electronic function due to elimination of the mechanical column shaft and are replaced with several sensors and actutator. This paper propose a control algorithm to syncronize the front wheel angle accordance to steering wheel input angle. The models of SBW system are indentified. Two control method are compare which is a PID and LQR controller is used to control the front axle motor of front axle system. The effect with and without disturbace input are applied to analyze the robustess of the controllers. To investigate the effectiveness of the proposed control algorithm, the matlab tools software is used. And, based on the result shows, both controller able to control the front wheel angle, however method using the LQR control provide more better steering response.

Magnetic Torquers Only Attitude Control of a 3U Cube Satellite

In this paper, attitude control of a 3U CubeSat is discussed using magnetic torquers only. As the capabilities of attitude control systems increase, the use of magnetic torques in the CubeSat is becoming more popular. Attitude control using only magnetic torquers has been researched for years and is still a current problem to study. This paper presents the use of an LQR controller for a 3U CubeSat without full gravity gradient stability of the model in a deterministic approach.

Design, Development and Experimental Assessment of a Cost-Effective Bellow Pneumatic Actuator

Soft pneumatic actuators offer great advantages compared to rigid ones, particularly due to their compliant nature, which allows them to adapt to uncertainties in the environment. As such, they enable human-safe interactions and are often applied to various applications, such as for example, soft grippers or wearable devices for human motion assistance. The presented research describes the process of design, development and finally control of two cost-effective bellow pneumatic actuators. The properties of the developed devices are experimentally assessed by performing three different types of experiments. In a first instance, the testing of blocking force was performed, followed by experimental assessment of velocity-displacement characteristics, and finally, the dynamical properties for sinusoidally forced motion were examined. It was shown that the actuator can provide over 100 N force and assure a contraction ratio over 40% of its full length, with maximum velocity exceeding 60 mm/s. Experimental responses to a sinusoidally forced motion allowed establishing that no significant change due to the fatigue, creep and relaxation occur in material properties. Finally, the positioning performances of both developed devices were assessed by employing PID and LQR controllers which allowed their precise position control with fast responses and steady-state errors within the 0.2 mm margin. The performed research gives some insights into the future development of the pneumatically driven mechatronics systems used for position control.

A Comparative Study on Hybrid Vibration Control of Base-isolated Buildings Equipped with ATMD

The vibration control for building structures using hybrid control (base isolators BI and Active Tuned Mass Dampers-ATMDs) has attracted the attention of researchers. This paper establishes a hybrid vibration control system of structure and compares structural response and active tuned mass damper performance among the structure using two different control algorithms (PID and LQR). Through simulation research, from the comparative analysis of performance indexes of structural response and ATMD performance, it is concluded that the LQR controller outperforms the PID controller in reducing the structural responses.

Dynamic characteristics of underframe semi-active inerter-based suspended device for high-speed train based on LQR control

Dynamic characteristics of underframe semi-active inerter-based suspended device for high-speed train based on LQR control

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.

Control of Semi-Active Suspension System Using Kalman Observer

Optimal control methods are increasingly used in automatic control systems, especially in automotive suspension system. However, the optimal control algorithm only achieves the highest efficiency in suspension control system when the required number of sensors is sufficient, corresponding to the number of states in the system. The arrangement of sufficient number of sensors depends on the capacity, economic conditions and responsiveness of the sensor. The Kalman observer is designed to reliably estimate the required parameters in the control where the number of sensors is limited. The article focuses on analyzing the theory of building a quarter-car model, developing and determining the optimal control matrix, the Kalman observer design method. The findings of the article reveal the effectiveness of automotive body vibration suppression and the required force for control corresponding to LQG control and LQR control, under the influence of square pulse road surface, when using two similar sensors are installed on the sprung and unsprung, thereby providing a choice of sensor type and the location on the semi-active ¼ suspension.

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.

Aerobatic Tic-Toc Control of Planar Quadcopters via Reinforcement Learning

This letter studies aerobatic tic-toc control of quadcopters. Tic-toc control enables rotorcraft to fly almost in the vertical plane rather than the horizontal plane. It is one of the most challenging manoeuvrers to achieve autonomously. The problem has to our knowledge not yet been studied for quadcopters. Studying it could expand their flight envelope and improve their performance in extreme, aerobatic flight tasks. In this letter, we employ a deep deterministic gradient policy approach to train reinforcement learning (RL) controllers based on carefully designed rewards. The obtained RL controllers are shown to generate two flight modes, spin and tic-toc. We analyse the properties of these flight modes and screen out unfavourable RL controllers. The qualified RL controller is then enhanced by combining it with PID and LQR controllers which achieves better flight performance and enables the quadcopter to track a moving reference point and recover to hovering flight status. Physical simulations using Simscape are presented to verify the proposed approach.

Modeling and Control of a Novel Design of Series Elastic Actuator for Upper Limb Rehabilitation

The field of rehabilitation robotics for upper limb assistance has grown rapidly in the past few years. Rehabilitation robots have direct contact with human joints, which presents a serious safety concern. In this paper, a novel design for a series elastic actuator (SEA) is presented for upper limb rehabilitation of sensorimotor dysfunctions. The proposed SEA ensures safety and robust torque control under various disturbances. The proposed SEA is modeled and simulated using MATLAB Simulink to obtain Input/Output driven data. For the proposed system, two modeling approaches, ARMAX and NN, are used along with comparative analysis for each model fitting. After SEA dynamic modeling, the PSO-tuned LQR controller is designed and implemented. Comparison parameters are then chosen to test the controller behavior during different applied disturbances. The chosen controller showed optimum set-point tracking performance, which is equivalent to upper limb movement. This research can be extended to develop and implement a test bench for upper limb rehabilitation.