Adaptive fuzzy proportional-integral-derivative control for micro aerial vehicle

With multiple industries employing Micro Aerial Vehicles (MA V) to accomplish various tasks comprising agricultural spraying, package delivery and disaster monitoring, the MA V system has attracted researchers towards resolving its stability issue as emerged from external disturbances. Disruptions caused by both wind and payload change disturbances have prevailed as natural mishaps which degrade performance of the quadrotor MA V system at the horizontal and vertical positions in the aspects of overshoot (OS), rise time (Tr), settling time (Ts)and steady-state error (ess)· Such adversities then cause increased error between the system's desired and actual positions, with a longer rise time and settling time towards reaching its steady-state condition. Adopting the rotary wing quad-rotor MAV system with 'X' configuration as the groundwork, the current study has especially set to explore a new approach for the system's robust positional control in the concurrent presence of wind and payload change disturbances. Earlier literatures have simultaneously suggested the adoptions of linear, nonlinear and hybrid approaches towards handing stability challenge of the quad-rotor MA V. Notably, most hybrid approaches are unable to account for current changes in the system's environment, whilst incapable of concomitantly handle multiple disturbances. An instance being the Fuzzy-PID (FPID) method which merely adjusts the Proportional-Integral-Derivative (PID) gains ensuing discovered positional error from emergence of system's overshoot. Acknowledging such incompetency, this research further proposed Adaptive Fuzzy-PlD (AFPID) controller as the contemporary hybrid approach that includes adaptability function for overcoming nonlinearity of the quad-rotor MA V system, while maintaining the system's robust performance facing current environmental changes from simultaneous wind and payload change disturbances. With the proposed adaptive fuzzy control being adopted to adjust the PID gains in accordance to surrounding changes, undertaken improvement is hereby targeted to eliminate the effect of wind and payload change disturbances amidst stabilizing the employed system. In return, encountered error on both the quad-rotor MA V's horizontal and vertical positions is expected to decline despite concurrent bombardment of multiple external disturbances, following a decrease to the system's overshoot (OS), rise time (Tr), settling time (Ts)and steady-state error (ess). In simulation, performance of the proposed AFPID controller on the horizontal, y position as studied under circumstances of different incoming wind velocities and water flow rates with respect to OS, Tr, Ts and e55 is placed in comparison to the performance of the PID and FPID methods. Improvement is observed in the system's ess for the AFPID controller on the horizontal, y position amid disruption of combined disturbances, with respective reductions of0.93 x 10-3 % and 1.35 X 10-3 % over the performances of PID and FPID controllers. Obtained results then confirm corresponding decline of 27.5% and 21.70% in OS for the AFPID controller over the PID and FPID controllers. A decline of 13 7.50 s and 13.40 s in Ts is further recorded for the AFPID controller as compared to the respective PID and FPID controllers. Accumulated findings, thus, validate AFPID as an effective controller for minimized positional error, smaller overshoot (OS) and steady-state error (esJ, as well as shorter settling time (Ts) and rise time (Tr) as compared to the earlier PID and FPID controllers when faced with uncertain situations of wind and payload change disturbances

Quad-rotor lifting-transporting cable-suspended payloads control

This paper presents the control of quadrotor UAV with cable-suspended stability. A linear quadratic regulator (LQR) control algorithm is proposed for lifting and transporting the load. The nonlinear dynamic model of the vehicle is represented with considering the cable-suspended load in eight degree of freedom, then the model is linearized at the hovering point. Two modes of taking-off are used, starting with taking-off without the load effect then switching to taking-off with the effect of load. The simulation presents the results to show the system stability and verify the LQR gains. The results are compared with the PD controller results

Optimal Control of Multiple Quadrotors for Transporting a Cable Suspended Payload

In this thesis, the main aim is to improve the flight control performance for a cable suspended payload with single and two quadrotors based on optimised control techniques. The study utilised optimal controllers, such as the Linear Quadratic Regulator LQR, the Iterative based LQR (ILQR), the Model Predictive Control MPC and the dynamic game controller to solve tracking control problems in terms of stabilisation, accuracy, constraints and collision avoidance. The LQR control was applied to the system as the first control method and compared with the classical Proportional-Derivative controller PD. It was used to achieve the load path tracking performance for single and two quadrotors with a cable slung load. The second controller was ILQR, which was developed based on the LQR control method to deal with the model nonlinearity. The MPC technique was also applied to the linearised nonlinear model LMPC of two quadrotors with a payload suspended by cables and compared with a nonlinear MPC (NMPC). Both MPC controllers LMPC and NMPC considered the constraints imposed on the system states and control inputs. The dynamic game control method was developed based on an incentive strategy for a leader-follower framework with the consideration of different optimal cost functions. It was applied to the linearised nonlinear model. Selecting these control techniques led to a number of achievements. Firstly, they improved the system performance in terms of achieving the system stability and reducing the steady-state errors. Secondly, the system parameter uncertainties were taken into consideration by utilising the ILQR controller. Thirdly, the MPC controllers guaranteed the handling of constraints and external disturbances in linear and nonlinear systems. Finally, avoiding collision between the leader and follower robots was achieved by applying the dynamic game controller. The controllers were tested in MATLAB simulation and verified for various desired predefined trajectories. In real experiments, these controllers were used as high-level controllers, which produce the optimised trajectory points. Then a low-level controller (PD controller) was used to follow the optimised trajectory points