PID &amp;amp; LQR control for a quadrotor: Modeling and simulation

Khatoon, Shahida; Gupta, Dhiraj; Das, Lalit Kumar

doi:10.1109/icacci.2014.6968232

Cited by 66 publications

(30 citation statements)

References 5 publications

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“…In the simulation, we assume the saturation limit in the external yaw moment is 4.0 kNm [39]. To verify the advantage of the modified CNF technique, we have compared it with the LQR technique, which is a commonly used optimal control strategy due to its superiority in yielding low steady-state error with fast response [40]. The controller gains in the reference yaw rate generation for both of the two cases are: k 1 = 3/v x , k 2 = 30k 1 .…”

Section: Simulationmentioning

confidence: 99%

Composite Nonlinear Feedback Control for Path Following of Four-Wheel Independently Actuated Autonomous Ground Vehicles

Wang

Yan

et al. 2016

IEEE Trans. Intell. Transport. Syst.

162

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This paper investigates the path-following control problem for four-wheel independently actuated autonomous ground vehicles through integrated control of active front-wheel steering and direct yaw-moment control. A modified composite nonlinear feedback strategy is proposed to improve the transient performance and eliminate the steady-state errors in path-following control considering the tire force saturations, in the presence of the time-varying road curvature for the desired path. Path following is achieved through vehicle lateral and yaw control, i.e., the lateral velocity and yaw rate are simultaneously controlled to track their respective desired values, where the desired yaw rate is generated according to the path-following demand. CarSim-Simulink joint simulation results indicate that the proposed controller can effectively improve the transient response performance, inhibit the overshoots, and eliminate the steady-state errors in path following within the tire force saturation limits.

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Section: Simulationmentioning

confidence: 99%

Composite Nonlinear Feedback Control for Path Following of Four-Wheel Independently Actuated Autonomous Ground Vehicles

Wang

Yan

et al. 2016

IEEE Trans. Intell. Transport. Syst.

162

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“…Another common approach is the Linear Quadratic Regulator (LQR) for attitude stabilization [23][24][25]. While this may be applicable for some configurations, multirotors are non-linear systems that present uncertainties, such as unknown physical parameters, actuator degradation, unknown delays in-process communication and unmodeled dynamics [26].…”

Section: Related Workmentioning

confidence: 99%

Neural PD Controller for an Unmanned Aerial Vehicle Trained with Extended Kalman Filter

et al. 2020

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Flying robots have gained great interest because of their numerous applications. For this reason, the control of Unmanned Aerial Vehicles (UAVs) is one of the most important challenges in mobile robotics. These kinds of robots are commonly controlled with Proportional-Integral-Derivative (PID) controllers; however, traditional linear controllers have limitations when controlling highly nonlinear and uncertain systems such as UAVs. In this paper, a control scheme for the pose of a quadrotor is presented. The scheme presented has the behavior of a PD controller and it is based on a Multilayer Perceptron trained with an Extended Kalman Filter. The Neural Network is trained online in order to ensure adaptation to changes in the presence of dynamics and uncertainties. The control scheme is tested in real time experiments in order to show its effectiveness.Algorithms 2020, 13, 40 2 of 18 and compact sensors are required for the navigation, and in most of the cases, inertial sensors are not enough to obtain some states of the system, such as its position. Usually, multirotors are teleoperated by a ground station with a limited operational range, but when autonomous tasks are required, the positional feedback is crucial to control the UAV [5].Drones are equipped with a Global Positioning System (GPS) to solve the problem of the estimation of the position. However, depending on the accuracy of the assignment, a GPS sensor may not be suitable; besides, the GPS signal is lost when working in indoor environments. For indoor flight control, the positional feedback is commonly carried out by motion capture systems, which each consist of a set of fixed cameras in a room. Unfortunately, approaches like this require previous knowledge of the scene and assembly and calibration of the motion capture system, which, in practice, would not be possible in search and rescue tasks. Generally, a combination of visual and inertial information is used to solve the problem of Simultaneous Localization and Mapping (SLAM) [6]. In this paper, a vision sensor is used; cameras are compact and lightweight sensors with low power consumption. These characteristics make them suitable for drones flying in unknown, GPS-denied environments.Once the SLAM problem has been solved, it is possible to control the position of the vehicle. Proportional-Integral-Derivative (PID) controllers are widely applied in industry because of their simplicity, and they are usually used to control these kinds of UAVs. Nevertheless, multirotors are highly nonlinear, underactuated systems with six Degrees of Freedom (Dof) and four control inputs-torque in x, y and z, and thrust-and therefore, they are difficult to control with conventional methods [7,8]. To overcome the limitations of conventional controllers, direct control using a neural network is proposed. In this work, a Multilayer Perceptron (MLP) is implemented to adapt the gains of a PD controller. As reported in [9], Artificial Neural Networks (ANN) have shown satisfactory results when controlling nonlinear systems, ...

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“…Theorem 1. Considering the system defined by (6) and the estimator model (9) by using a EKF adjustment law defined by (14). The neural observer based on EKF is semiglobal uniformly ultimate bounded (SGUUB) and it is stable.…”

Section: Assumptionmentioning

confidence: 99%

“…[9][10][11] Other studies proposed the UAV control based on PID controllers with artificial intelligence and dynamic properties, such as the controllers presented in the work of Yang et al, 12 where authors defined a single-neuron PID, where the adjusting of the weights is a function of the control errors. However, since quadrotor is a nonlinear underactuated systems, 8 it is not always possible to use PID control directly for the quadrotor system.…”

mentioning

confidence: 99%

“…Consequently, some authors proposed controllers designed based only on static PID for the UAV control such as the controllers presented in other works. [9][10][11] Other studies proposed the UAV control based on PID controllers with artificial intelligence and dynamic properties, such as the controllers presented in the work of Yang et al, 12 where authors defined a single-neuron PID, where the adjusting of the weights is a function of the control errors. Besides, simulations results were displayed to validate the proposal but the stability analysis is omitted.…”

mentioning

confidence: 99%

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Identification and adaptive PID Control of a hexacopter UAV based on neural networks

Rosales

Soria

Rossomando

2018

Adaptive Control & Signal

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In this paper, a novel adaptive PID controller for trajectory-tracking tasks is proposed. It is implemented in discrete time over a hexacopter, and it takes into consideration the unmanned aerial vehicles (UAVs) nonlinear model. The PID controller is developed following an adaptive neural technique, and its stability is verified by the Lyapunov discrete theory. Besides, the neural identification of the dynamic model of the UAV is presented to backpropagate output errors to adjust PID gains with the purpose of reducing the control errors. The validation of the proposed algorithm is performed through experimental results with a hexacopter. KEYWORDSadaptive PID, discrete stability analysis, hexacopter, identification, neural networks 74 ;33:74-91. ROSALES ET AL. 75steady-state errors, and the derivative gain is used to modify any system overshoot. However, since quadrotor is a nonlinear underactuated systems, 8 it is not always possible to use PID control directly for the quadrotor system. Consequently, some authors proposed controllers designed based only on static PID for the UAV control such as the controllers presented in other works. [9][10][11] Other studies proposed the UAV control based on PID controllers with artificial intelligence and dynamic properties, such as the controllers presented in the work of Yang et al, 12 where authors defined a single-neuron PID, where the adjusting of the weights is a function of the control errors. Besides, simulations results were displayed to validate the proposal but the stability analysis is omitted. In addition, Xu and Zhou 13 use self-tuning PID controller based on adaptive pole placement to a quadrotor by using a linearized version of the dynamic model. Another class of PID controllers had the capabilities to backpropagate control errors for a better adjust of the controller gains, some of those are described in this work. Tsakalis and Dash 14 proposed an algorithm for  ∞ for online tuning of PID gains. The estimator is very complex to implement, although is robust with respect to the excitation spectrum. The algorithm is validated by using simulations results for different transfer functions. In the work of Liu et al, 15 the design of a Gaussian potential function network PID (GPFN-PID) controller based on GPFN NN was described. The proposed controller had the functions of online training, self-training, and self-adjusting, which made control of the UAV longitudinal channel more effective. However, the results obtained were based on simulations. Babu et al 16 proposed a gradient descent-based methodology for adjusting online the gains of a PID controller based on a cost function defined to penalize errors. The controller is designed only for positioning task and a commercial quadrotor is used to experiment the controller in position task. In the work of Wang et al, 17 an intelligent PID controller based on radial basis functions (RBFs) NN was designed for longitudinal attitude control of a small UAV using a nonlinear model. An RBF NN was utilized for online updatin...

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PID & LQR control for a quadrotor: Modeling and simulation

Cited by 66 publications

References 5 publications

Composite Nonlinear Feedback Control for Path Following of Four-Wheel Independently Actuated Autonomous Ground Vehicles

Composite Nonlinear Feedback Control for Path Following of Four-Wheel Independently Actuated Autonomous Ground Vehicles

Neural PD Controller for an Unmanned Aerial Vehicle Trained with Extended Kalman Filter

Identification and adaptive PID Control of a hexacopter UAV based on neural networks

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