Robust PID control of quadrotors with power reduction analysis

An Adaptive Control Scheme Based on Non-Interference Nonlinearity Approximation for a Class of Nonlinear Cascaded Systems and Its Application to Flexible Joint Manipulators

Control design for the nonlinear cascaded system is challenging due to its complicated system dynamics and system uncertainty, both of which can be considered some kind of system nonlinearity. In this paper, we propose a novel nonlinearity approximation scheme with a simplified structure, where the system nonlinearity is approximated by a steady component and an alternating component using only local tracking errors. The nonlinearity of each subsystem is estimated independently. On this basis, a model-free adaptive control for a class of nonlinear cascaded systems is proposed. A squared-error correction procedure is introduced to regulate the weight coefficients of the approximation components, which makes the whole adaptive system stable even with the unmodeled uncertainties. The effectiveness of the proposed controller is validated on a flexible joint system through numerical simulations and experiments. Simulation and experimental results show that the proposed controller can achieve better control performance than the radial basis function network control. Due to its simplicity and robustness, this method is suitable for engineering applications.

Comprehensive reconstructions and predictive control for quadrotor UAV information gathering tracking missions based on fully actuated system approaches

This paper introduces a novel approach to the comprehensive reconstruction and predictive control (PC) of the quadrotor UAV for information-gathering missions, employing fully actuated system (FAS) approaches. Unlike conventional PC methods applied to a quadrotor UAV with hybrid constraints, our work integrates reconstructions of the system model, hybrid constraints, and the receding horizon performance index into to an integrated tracking control scheme within the FAS-PC framework. Specifically, the under-actuated quadrotor UAV model is reconstructed into a full-actuated model to inject full-actuation properties. And the implicit hybrid constraints that arise from the model reconstruction are explicitly transformed and decoupled. Simultaneously, the cascaded predictive algorithm is established that the new time-varying input constraints are solved in each predictive horizon, and then the nonlinear optimization problem is decoupled into four linear convex optimization problems subject to the corresponding decoupled linear constraints and the pre-addressed input constraints. Within this framework, the intrinsic complexities, nonlinearities, and interdependencies of the quadrotor UAV system model, along with hybrid constraints and the optimization dilemma, are considerably diminished. This reduction significantly eases computational demands, enabling satisfactory real-time performance. Furthermore, the selection of predictive parameters guarantees the stability of the resultant tracking error closed-loop system. Finally, the efficacy of the proposed method is validated through two sets of flight missions, conducted via simulation and practical experimentation, respectively.

Attitude control of a 3-DoF quadrotor platform using a linear quadratic integral differential game approach

In this study, a linear quadratic integral differential game approach is applied to regulate and track the Euler angles for a quadrotor experimental platform using two players. One produces commands for each channel of the quadrotor and another generates the worst disturbance based on the mini-maximization of a quadratic criterion with integral action. For this purpose, first, the attitude dynamics of the platform are modeled and its parameters are identified based on the Nonlinear Least Squares Trust-Region Reflective method. The performance of the proposed controller is evaluated for regulation and tracking problems. The ability of the controller is also examined in the disturbance rejection. Moreover, the influence of uncertainty modeling is studied on the obtained results. Then, the performance of the proposed controller is compared with the classic Proportional Integral Derivative, Linear Quadratic Regulator, and Linear Quadratic Integral Regulator. The results demonstrate the effectiveness of the Game Theory on the Linear Quadratic Regulator approach when the input disturbance occurs.

Adaptive Fuzzy Fractional Order Global Sliding Mode Tracking Control Algorithm for Particleboard Glue System

In this paper, a novel flow tracking control scheme for particleboard glue system with complex disturbance and unmeasurable system state is investigated. The method is based on hyperbolic tangent extended state observer and adaptive fuzzy fractional order global sliding mode control with exponential reaching law. The novel compound control scheme has the following advantages: Firstly, the extended state observer with hyperbolic tangent function can improve the estimation ability for the system state and complex disturbance without detailed knowledge of the controlled plant and disturbance model. Secondly, the global sliding mode control method based on fractional calculus can improve the response speed and robustness of the system, and provide a more flexible controller structure than the traditional sliding mode controller. Thirdly, the adaptive fuzzy controller is introduced to approximate the sliding mode switching term, so as to reduce the chattering phenomenon of the system. In addition, the convergence of the proposed observer and asymptotic stability of the control system are verified based on strict Lyapunov analysis. Finally, the numerical simulation results show the effectiveness of the proposed compound control scheme for particleboard glue system.

Neuro-adaptive fast integral terminal sliding mode control design with variable gain robust exact differentiator for under-actuated quadcopter UAV

In this paper, a robust global fast terminal attractor based full flight trajectory tracking control law has been developed for the available regular form which is operated under matched uncertainties. Based on the hierarchical control principle, the aforesaid model is first subdivided into two subsystems, i.e., a fully-actuated subsystem and an under-actuated subsystem. In other words, the under-actuated subsystem is further transformed into a regular form whereby the under-actuated characteristics are decoupled in terms of control inputs. In the proposed design, the nonlinear drift terms, which certainly varies in full flight, are estimated via functional link neural networks to improve the performance of the controller in full flight. Besides, a variable gain robust exact differentiator (VG-RED) is designed to provide us with estimated flight velocities. It has consequently reduced the noise in system's velocities and has mapped this controller as a practical one. The finite-time sliding mode enforcement and the states' convergence are shown, for all flight loops, i.e., forward flight and backward flight, via the Lyapunov approach. All these claims are verified via numerical simulations and experimental implementation of the quadcopter system in a Matlab environment. For a more impressive presentation, the developed simulation results are compared with standard literature.

Quadrotor Attitude Control by Fractional-Order Fuzzy Particle Swarm Optimization-Based Active Disturbance Rejection Control

In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the attitude control of a quadrotor aircraft to address the problem of attitude destabilization in flight control caused by parameter uncertainties and external disturbances. Considering the difficulty of optimizing the parameter of ADRC, a fractional-order fuzzy particle swarm optimization (FOFPSO) algorithm is proposed to optimize the parameters of ADRC for quadrotor aircraft. Simultaneously, the simulation experiment is designed, which compares with the optimized performance of traditional particle swarm optimization (PSO), fuzzy article swarm optimization (FPSO) and adaptive genetic algorithm-particle swarm optimization (AGA-PSO). In addition, the turbulent wind field model is established to verify the disturbance rejection performance of the controller. Finally, the designed controller is deployed to the actual hardware platform by using the model-based design method. The results show that the controller has a small overshoot and stronger disturbance rejection ability after the parameters are optimized by the proposed algorithm.

Composite prescribed performance control of small unmanned aerial vehicles using modified nonlinear disturbance observer

An integrated control scheme composed of modified nonlinear disturbance observer and predefined-time prescribed performance control is proposed to address the high-accuracy tracking problem of the unmanned aerial vehicles (UAVs) subjected to external mismatched disturbances. By utilizing the transformation technique that incorporates the desired performance characteristic and the newly predefined-time performance function, the original controlled system can be transformed into a new unconstrained one to achieve the fixed-time convergence of the tracking error. Then, by virtual of the transformed unconstrained system, a modified nonlinear disturbance observer (NDO) which possesses fast convergence speed is established to estimate the external disturbance. With the application of the precise estimation value to compensate the normal control design in each back-stepping step, a novel composite control scheme is constructed. The light spot of the proposed scheme is that it not only has the superior capability to attenuate unknown mismatched disturbances, but also can guarantee that the output tracking errors converge to their prescribed regions within predefined time. Finally, simulation studies verify the effectiveness of the proposed control scheme.

Fixed-time trajectory following for quadrotors via output feedback

A fixed-time trajectory following problem for quadrotors via output feedback is concerned. Based on the inner-outer separation design philosophy, the under-actuated quadrotor is formulated as a hierarchical structure composed by position and attitude dynamics. With an emphasis on removing the demand on unmeasured velocity and eliminating the negative effect of disturbances, fixed-time extended state observers utilizing two kinds of polynomial feedback terms are proposed to simultaneously identify unavailable velocity states and unknown uncertainties with a fixed-time estimation capability. With these observation results, a velocity free fixed-time control protocol is synthesized to enable a satisfied trajectory regulation with a uniform convergence time independent of initial positions, such that a prescribed fixed-time stability and enhanced robustness can be obtained with chattering-free inputs. By virtue of bi-limit homogeneity properties, all error variables of the resultant quadrotor system are demonstrated to be fixed-time convergent. Eventually, the benefits of developed algorithm are illustrated via simulations.

Data Driven Model-Free Adaptive Control Method for Quadrotor Formation Trajectory Tracking Based on RISE and ISMC Algorithm

In order to solve the problems of complex dynamic modeling and parameters identification of quadrotor formation cooperative trajectory tracking control, this paper proposes a data-driven model-free adaptive control method for quadrotor formation based on robust integral of the signum of the error (RISE) and improved sliding mode control (ISMC). The leader-follower strategy is adopted, and the leader realizes trajectory tracking control. A novel asymptotic tracking data-driven controller of quadrotor is used to control the system using the RISE method. It is divided into two parts: The inner loop is for attitude control and the outer loop for position control. Both use the RISE method in the loop to eliminate interference and this method only uses the input and output data of the unmanned aerial vehicle(UAV) system and does not rely on any dynamics and kinematics model of the UAV. The followers realize formation cooperative control, introducing adaptive update law and saturation function to improve sliding mode control (SMC), and it eliminates the general SMC algorithm controller design dependence on the mathematical model of the UAV and has the chattering problem. Then, the stability of the system is proved by the Lyapunov method, and the effectiveness of the algorithm and the feasibility of the scheme are verified by numerical simulation. The experimental results show that the designed data-driven model-free adaptive control method for the quadrotor formation is effective and can effectively realize the coordinated formation trajectory tracking control of the quadrotor. At the same time, the design of the controller does not depend on the UAV kinematics and dynamics model, and it has high control accuracy, stability, and robustness.

Finite-Time Attitude Fault Tolerant Control of Quadcopter System via Neural Networks

This study investigates the design of fault-tolerant control involving adaptive nonsingular fast terminal sliding mode control and neural networks. Unlike those of previous control strategies, the adaptive law of the investigated algorithm is considered in both continuous and discontinuous terms, which means that any disturbances, model uncertainties, and actuator faults can be simultaneously compensated for. First, a quadcopter model is presented under the conditions of disturbances and uncertainties. Second, normal adaptive nonsingular fast terminal sliding mode control is utilized to handle these disturbances. Thereafter, fault-tolerant control based on adaptive nonsingular fast terminal sliding mode control and neural network approximation is presented, which can handle the actuator faults, model uncertainties, and disturbances. For each controller design, the Lyapunov function is applied to validate the robustness of the investigated method. Finally, the effectiveness of the investigated control approach is presented via comparative numerical examples under different fault conditions and uncertainties.