Research on Neural Network PID Control Algorithm for a Quadrotor

2015 ◽  
Vol 719-720 ◽  
pp. 346-351 ◽  
Author(s):  
Wei Nan Gao ◽  
Jia Lu Fan ◽  
Yan Nong Li
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Simple Structure ◽  
Position Control ◽  
Network Control ◽  
Pid Algorithm ◽  
Control Scheme ◽  
Challenging Tasks ◽  
Space Function ◽  
Aerial Vehicle

Quadrotor is a kind of popular unmanned aerial vehicle which obtains prime advantages in simple structure, vertically taking off and landing and hovering ability; hence it possesses wide application prospects in reconnaissance and rescue, geological exploration and video surveillance. However, attitude and position control of the quadrotor are challenging tasks because it is an under-actuated system with strong nonlinear, coupling and model uncertainty characteristics. In this paper, the dynamics model and the state space function of the micro-quadrotor are firstly established. Then, a cascade control scheme is proposed to decouple the control system and a multivariate RBF(Radial Basis Function) neural network control PID algorithm is proposed to realize robust control of the quadrotor. This algorithm is not only characterized by simple structure and easy implementation, but also capable of self-adaption and online learning. Simulation results show that the proposed control algorithm performs well in tracking and under disturbances and model uncertainties.

scholarly journals Neural Network Control Design for an Unmanned Aerial Vehicle with a Suspended Payload

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 931 ◽  
Author(s):  
Cai Luo ◽  
Zhenpeng Du ◽  
Leijian Yu
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Sliding Mode ◽  
Network Control ◽  
Outdoor Environment ◽  
Test Results ◽  
Cluttered Environments ◽  
Aerial Vehicle ◽  
Backstepping Sliding Mode Control

Unmanned aerial vehicles (UAVs) demonstrate excellent manoeuvrability in cluttered environments, which makes them a suitable platform as a data collection and parcel delivering system. In this work, the attitude and position control challenges for a drone with a package connected by a wire is analysed. During the delivering task, it is very difficult to eliminate the external unpredictable disturbances. A robust neural network-based backstepping sliding mode control method is designed, which is capable of monitoring the drone’s flight path and desired attitude with a suspended cable attached. The convergence of the position and attitude errors together with the Lyapunov function are employed to attest to the robustness of the nonlinear transportation platform. The proposed control system is tested with a simulation and in an outdoor environment. The simulation and open field test results for the UAV transportation platform verify the controllers’ reliability.

scholarly journals Robust Quadrotor Control through Reinforcement Learning with Disturbance Compensation

2021 ◽  
Vol 11 (7) ◽  
pp. 3257
Author(s):  
Chen-Huan Pi ◽  
Wei-Yuan Ye ◽  
Stone Cheng
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Control Strategy ◽  
External Disturbance ◽  
Control Agent ◽  
Network Control ◽  
Outdoor Environment ◽  
Disturbance Compensation ◽  
Tracking Accuracy ◽  
Control Scheme

In this paper, a novel control strategy is presented for reinforcement learning with disturbance compensation to solve the problem of quadrotor positioning under external disturbance. The proposed control scheme applies a trained neural-network-based reinforcement learning agent to control the quadrotor, and its output is directly mapped to four actuators in an end-to-end manner. The proposed control scheme constructs a disturbance observer to estimate the external forces exerted on the three axes of the quadrotor, such as wind gusts in an outdoor environment. By introducing an interference compensator into the neural network control agent, the tracking accuracy and robustness were significantly increased in indoor and outdoor experiments. The experimental results indicate that the proposed control strategy is highly robust to external disturbances. In the experiments, compensation improved control accuracy and reduced positioning error by 75%. To the best of our knowledge, this study is the first to achieve quadrotor positioning control through low-level reinforcement learning by using a global positioning system in an outdoor environment.

scholarly journals Neural Network Based Contact Force Control Algorithm for Walking Robots

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 287
Author(s):  
Byeongjin Kim ◽  
Soohyun Kim
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
Force Control ◽  
Control Algorithm ◽  
Position Control ◽  
Linear Quadratic Regulator ◽  
Walking Robots ◽  
Test Model ◽  
Linear Quadratic ◽  
Contact Force Control

Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly.

Fault-Tolerant Formation Tracking Control for Heterogeneous Multiagent Systems with Directed Topology

2021 ◽  
Vol 01 (01) ◽  
pp. 2150001
Author(s):  
Jianye Gong ◽  
Yajie Ma ◽  
Bin Jiang ◽  
Zehui Mao
Keyword(s):  
Tracking Control ◽  
Formation Control ◽  
Control Algorithm ◽  
Fault Tolerant ◽  
Control Technique ◽  
Fault Estimation ◽  
Consensus Control ◽  
Formation Tracking ◽  
Control Scheme ◽  
Aerial Vehicle

In this paper, the adaptive fault-tolerant formation tracking control problem for a set of heterogeneous unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) systems with actuator loss of effectiveness faults is investigated. The cooperative fault-tolerant formation control strategy for UAV and UGV collaborative systems is classified into the altitude consensus control scheme for follower UAVs and the position cooperative formation control scheme for all followers. The altitude consensus control algorithm is designed by utilizing backstepping control technique to drive all UAVs to a desired predefined height. Then, based on synchronization formation error information, the position cooperative formation control algorithm is proposed for all followers to reach the expected position and perform the desired formation configuration. The adaptive fault estimation term is adopted in the designed fault-tolerant formation control algorithm to compensate for the actuator loss of effectiveness fault. Finally, a simulation example is proposed to reveal the validity of the designed cooperative formation tracking control scheme.

scholarly journals An adaptive hierarchical control for aerial manipulators

Robotica ◽  
2018 ◽  
Vol 36 (10) ◽  
pp. 1527-1550 ◽  
Author(s):  
Francesco Pierri ◽  
Giuseppe Muscio ◽  
Fabrizio Caccavale
Keyword(s):  
Control Algorithm ◽  
Hierarchical Control ◽  
Bottom Layer ◽  
Trajectory Tracking Control ◽  
Aerial Manipulator ◽  
Control Scheme ◽  
Modelling Uncertainties ◽  
Aerial Vehicle ◽  
The Stability

SUMMARYThis paper addresses the trajectory tracking control problem for a quadrotor aerial vehicle, equipped with a robotic manipulator (aerial manipulator). The controller is organized in two layers: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables; in the bottom layer, a motion control algorithm is in charge of tracking the motion references computed by the upper layer. To the purpose, a model-based control scheme is adopted, where modelling uncertainties are compensated through an adaptive term. The stability of the proposed scheme is proven by resorting to Lyapunov arguments. Finally, a simulation case study is proposed to prove the effectiveness of the approach.

Barrier Lyapunov function-based robot control with an augmented neural network approximator

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zuguo Zhang ◽  
Qingcong Wu ◽  
Xiong Li ◽  
Conghui Liang
Keyword(s):  
Neural Network ◽  
Lyapunov Function ◽  
Control Algorithm ◽  
Tracking Performance ◽  
Content Type ◽  
Barrier Lyapunov Function ◽  
Output Error ◽  
Control Scheme ◽  
Stringent Constraint ◽  
Continuous Dynamic

Purpose Considering the complexity of dynamic and friction modeling, this paper aims to develop an adaptive trajectory tracking control scheme for robot manipulators in a universal unmodeled method, avoiding complicated modeling processes. Design/methodology/approach An augmented neural network (NN) constituted of radial basis function neural networks (RBFNNs) and additional sigmoid-jump activation function (SJF) neurons is introduced to approximate complicated dynamics of the system: the RBFNNs estimate the continuous dynamic term and SJF neurons handle the discontinuous friction torques. Moreover, the control algorithm is designed based on Barrier Lyapunov Function (BLF) to constrain output error. Findings Lyapunov stability analysis demonstrates the exponential stability of the closed-loop system and guarantees the tracking errors within predefined boundaries. The introduction of SJFs alleviates the limitation of RBFNNs on discontinuous function approximation. Owing to the fast learning speed of RBFNNs and jump response of SJFs, this modified NN approximator can reconstruct the system model accurately at a low compute cost, and thereby better tracking performance can be obtained. Experiments conducted on a manipulator verify the improvement and superiority of the proposed scheme in tracking performance and uncertainty compensation compared to a standard NN control scheme. Originality/value An enhanced NN approximator constituted of RBFNN and additional SJF neurons is presented which can compensate the continuous dynamic and discontinuous friction simultaneously. This control algorithm has potential usages in high-performance robots with unknown dynamic and variable friction. Furthermore, it is the first time to combine the augmented NN approximator with BLF. After more exact model compensation, a smaller tracking error is realized and a more stringent constraint of output error can be implemented. The proposed control scheme is applicable to some constraint occasion like an exoskeleton and surgical robot.

Recurrent Higher Order Neural Network Control for Output Trajectory Tracking with Neural Observers and Constrained Inputs

2010 ◽  
pp. 286-311 ◽  
Author(s):  
Luis J. Ricalde ◽  
Edgar N. Sanchez ◽  
Alma Y. Alanis
Keyword(s):  
Neural Network ◽  
Tracking Error ◽  
Network Control ◽  
Design Parameters ◽  
The Neural Network ◽  
Control Scheme ◽  
Output Trajectory ◽  
Error Dynamics ◽  
Adaptation Law ◽  
Constrained Inputs

This Chapter presents the design of an adaptive recurrent neural observer-controller scheme for nonlinear systems whose model is assumed to be unknown and with constrained inputs. The control scheme is composed of a neural observer based on Recurrent High Order Neural Networks which builds the state vector of the unknown plant dynamics and a learning adaptation law for the neural network weights for both the observer and identifier. These laws are obtained via control Lyapunov functions. Then, a control law, which stabilizes the tracking error dynamics is developed using the Lyapunov and the inverse optimal control methodologies . Tracking error boundedness is established as a function of design parameters.

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