The adaptive control system of quadrocopter motion

In this paper we present a system for automatic control of a quadrocopter based on the adaptive control system. The task is to ensure the motion of the quadrocopter along the given route and to control the stabilization of the quadrocopter in the air in a horizontal or in a given angular position by sending control signals to the engines. The nonlinear model of a quadrocopter is expressed in the form of a linear non-stationary system

The adaptive control system of quadrocopter motion

In this paper we present a system for automatic control of a quadrocopter based on the adaptive control system. The task is to ensure the motion of the quadrocopter along the given route and to control the stabilization of the quadrocopter in the air in a horizontal or in a given angular position by sending control signals to the engines. The nonlinear model of a quadrocopter is expressed in the form of a linear non-stationary system

Fault-tolerant scheme for robotic manipulator -Nonlinear robust back-stepping control with friction compensation

Emerging applications of autonomous robots requiring stability and reliability cannot afford component failure to achieve operational objectives. Hence, identification and countermeasure of a fault is of utmost importance in mechatronics community. This research proposes a Fault-tolerant control (FTC) for a robot manipulator, which is based on a hybrid control scheme that uses an observer as well as a hardware redundancy strategy to improve the performance and efficiency in the presence of actuator and sensor faults. Considering a five Degree of Freedom (DoF) robotic manipulator, a dynamic LuGre friction model is derived which forms the basis for design of control law. For actuator's and sensor's FTC, an adaptive back-stepping methodology is used for fault estimation and the nominal control law is used for the controller reconfiguration and observer is designed. Fault detection is accomplished by comparing the actual and observed states, pursued by fault tolerant method using redundant sensors. The results affirm the effectiveness of the proposed FTC strategy with model-based friction compensation. Improved tracking performance as well robustness in the presence of friction and fault demonstrate the efficiency of the proposed control approach

Robust Backstepping Tracking Control of Mobile Robot Based on Nonlinear Disturbance Observer

This paper presents a robust backstepping control (BC) method based on nonlinear disturbance observer (NDOB) for trajectory tracking of the nonholonomic wheeled mobile robot (WMR) in the presence of external disturbances and parameters uncertainties. At first, a bounded Fuzzy logic based backstepping controller (BFLBC) is designed to control the WMR without considering the effects of the external disturbances and the parameters uncertainties. Typically, the conventional BC controller depends upon the state tracking errors analysis, where unbounded velocity signal is produced for the applications that have huge tracking errors. Therefore, a fuzzy logic controller (FLC) is introduced in this research in order to normalize the state tracking errors, so that the input errors to the BC are bounded to a finite interval. Finally, the designed BFLBC is integrated with the nonlinear disturbance observer in order to attenuate the external disturbances and model uncertainties. The simulation results show the effectiveness of the proposed controller to generate a bounded velocity signal as well as to stabilize the tracking errors to zero. In addition, the results prove that the proposed controller provide an excellent disturbance attenuation as well as robustness against the parameters uncertainties

Adaptive neural network control of a robotic manipulator with unknown backlash-like hysteresis

This study proposes an adaptive neural network controller for a 3-DOF robotic manipulator that is subject to backlashlike hysteresis and friction. Two neural networks are used to approximate the dynamics and the hysteresis non-linearity. A neural network, which utilises a radial basis function approximates the robot's dynamics. The other neural network, which employs a hyperbolic tangent activation function, is used to approximate the unknown backlash-like hysteresis. The authors also consider two cases: full state and output feedback control. For output feedback, where system states are unknown, a high gain observer is employed to estimate the states. The proposed controllers ensure the boundedness of the control signals. Simulations are also performed to show the effectiveness of the controllers