Adaptive trajectory tracking controller design for a quadrotor UAV with payload variation

The PID control strategy is usually used to control the motor in the electric servo loading system CESLS), however there are some nonlinear factors such as mechanism clearance and surplus torque in the loading process, the conventional PID control method can't guarantee the control performance and the loading accuracy of the ESLS. A position feed-forward compensation controller is introduced in the ESLS, and an outer loop active disturbance rejection control (ADRC) strategy is designed to eliminate position disturbance and surplus torque disturbance in this paper. The method of “observation + compensation” used in the outer loop ADRC controller is not only to suppress the surplus torque, but also to improve the steady-state performance of the ESLS. The simulation result shows that the electric servo loading system based on ADRC strategy can effectively improve the tracking accuracy of the ESLS and enhance the stability of the ESLS.

This paper presents the design and implementation of a novel sliding mode control integrated with active disturbance rejection (SMCDR) for precise robust trajectory tracking of piezoelectric nanopositioning stages. The model uncertainties, nonlinearity, and external disturbances of the piezoelectric nanopositioning stage are regarded as a lumped disturbance, which is estimated by an extended state observer. The active disturbance rejection control (ADRC) strategy is used to realize a preliminary trajectory tracking, while the sliding mode control is adopted to handle the estimation error and residual uncertainties, and to improve the tracking performance. The exponential stability of the proposed SMCDR is proved using Lyapunov’s direct method. The proposed SMCDR controller combines the strength of both ADRC and sliding mode control, exhibits a simple structure, requires only the position measurements, and does not require any information of model parameters except for an approximate constant gain. Experimental results reveal the robustness of the SMCDR approach in suppressing the hysteresis of piezoelectric actuator, and illustrate the superior performance over conventional ADRC, integral sliding mode controller (ISMC) and PID controller for trajectory tracking control of piezoelectric nanopositioning stages.

In order to overcome the problems of linear control method, which are unfavorable performance and highly depending on the accuracy of vehicle model, active disturbance rejection control(ADRC) strategy is used to complete active front wheel steering(AFS) control in this paper. According to the general principle of ADRC, a second order ADRC strategy is designed. Under the operation conditions of high speed single lane change and braking on µ-split road, the control performance is studied by means of simulation. The results show that the ADRC strategy can realize AFS control effectively and decrease the dependence on the accuracy of vehicle model. At the same time, the performance is better than that of PID control.

Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization-based active disturbance rejection control

Introduction/purpose: The trajectory tracking control problem in an unmanned tracked vehicle (UTV) represents a challenging task, due to unknown and unmeasurable slippage dynamics which inevitably exists during movement. Therefore, the application of standard industrial control schemes is often limited. Methods: In this paper, an active disturbance rejection control (ADRC) scheme is proposed for the longitudinal (vehicle longitudinal velocity control) and lateral (vehicle course angle control) control channels of the UTV to collectively handle all the plant modeling uncertainties and acting slippage disturbances. Results: A step-by-step procedure for applying the ADRC algorithm for the specific case of UTV trajectory tracking is presented. It includes systematic design, discretization as well as performance analysis and validation utilizing FPGA-in-the-loop (FIL) simulations. Conclusions: The proposed FIL-based validation method reduces the gap between pure simulation design (which may be too idealized) and implementation on the real vehicle (which may be time-consuming). The obtained experimental results show the advantages of the proposed control structure over standard industrial PI/PID controllers in different working conditions.

Aiming at the tip trajectory tracking against system parameter uncertainty and disturbance during the painting process of the spray painting manipulator, a sliding model active disturbance rejection trajectory tracking controller is designed integrating terminal sliding mode control (SMC) approach and active disturbance rejection control (ADRC) technique. The reference curve trajectory of the tip position/velocity is planned by the kinematics equation. in which the compensation is taken into account for the deflection of the manipulator. The extended state observer (ESO) is used to estimate the total disturbance consisting of the uncertain system parameters and the uncertain interference resulting from deflection and load. Utilizing non-singular terminal sliding mode control, a feedback control law is designed to achieve fast and accurate trajectory tracking. Simultaneously, according to the actual system parameters in the engineering, the simulation validation is given to show that the proposed control strategy can guarantee good tracking performance of the closed-loop system.

In this paper, two-time scale control design for trajectory tracking of two cooperating planar rigid robots moving a flexible beam which does not require any vibration measurement of the beam is presented. First, kinematics and dynamics of the robots and the object are derived. Then, using the relations between different forces acting on the object by the manipulators' end-effectors, the dynamic equations of the robots and the object are combined. Applying two-time scale control theory on the combined dynamics, a control scheme is elaborated which makes the position and orientation of the mass center of the beam track a desired trajectory while suppressing the beam vibration. For the controller algorithm, first a slow controller is applied for the slow (rigid) subsystem and then a fast stabilizing controller is considered for the fast (flexible) subsystem; these two controllers constitute the composite control scheme. To avoid requiring any beam vibration measurement for the fast control law, a linear observer is also designed. The simulation results show the efficiency of the proposed control scheme.

In most extant flight trajectory tracking control schemes, such as sliding mode control, gain parameter selection is time-consuming owing to the lack of explicit methods or rationales for the selection. Therefore, we herein propose a simple and efficient parameter selection method for sliding mode control. To demonstrate the effectiveness of the proposed method, we designed several types of trajectory tracking control laws for a rotorcraft. We derived the relation between gain parameters and desired damping ratio/desired natural frequency by utilizing modal analysis to transform trajectory tracking error as a second order system with respect to position and heading angle. Thus, we rationally determined gain parameters based on the expected system response, which corresponded to the classical response expected in a second order system. After implementing our method to the trajectory tracking control law for a Bo-105 rotorcraft in simulations, we determined that tracking performances can be upgraded or degraded based on damping ratio and natural frequency. In our controller design, as the desired damping ratio increased, although no significant differences exist at high damping ratio, undesired oscillatory responses disappeared. Further, the effects of variation in desired natural frequencies are not as significantly evident as those due to variation in damping ratio. However, the effects of desired natural frequency can be observed in the heading angle of the current trajectory tracking control design; as the natural frequency increased, the tracking accuracy increased. Therefore, the proposed method can serve as a guideline for tuning many parameters involved in the design of an advanced control law.KeywordsRotorcraftTrajectory tracking controlSliding mode controlGain selection

A novel attitude control strategy based on active disturbance rejection control (ADRC) and input shaping technique was proposed for a flexible spacecraft without angular velocity sensors. Aiming at achieving high rapidity and high pointing accuracy, we focus on the suppression of vibration of flexible appendages when the spacecraft's attitude maneuvers a large angle. In the proposed scheme, a transient process which combines path planning for attitude maneuver with input shaping is designed to reduce vibration of flexible appendages. Furthermore, an extended state observer (ESO) for the flexible spacecraft is designed to estimate the immeasurable attitude angular velocity and the disturbances, which include parameter uncertainties and external disturbances. Also a nonlinear attitude feedback control law is designed to track the desired attitude and compensate the disturbances in real time way. Simulation results demonstrate the effectiveness and robustness of the proposed control strategy.

Robots and automated systems are increasingly being introduced to unknown and dynamic environments where they are required to handle disturbances, unmodeled dynamics, and parametric uncertainties. Robust and adaptive control strategies are required to achieve high performance in these dynamic environments. In this paper, we propose a novel adaptive model predictive controller that combines model predictive control (MPC) with an underlying $\mathcal{L}_{1}$ adaptive controller to improve trajectory tracking of a system subject to unknown and changing disturbances. The $\mathcal{L}_{1}$ adaptive controller forces the system to behave in a predefined way, as specified by a reference model. A higher-level model predictive controller then uses this reference model to calculate the optimal reference input based on a cost function, while taking into account input and state constraints. We focus on the experimental validation of the proposed approach and demonstrate its effectiveness in experiments on a quadrotor. We show that the proposed approach has a lower trajectory tracking error compared to non-predictive, adaptive approaches and a predictive, nonadaptive approach, even when external wind disturbances are applied.

To resolve the problem of high-precision trajectory tracking control under interference conditions in a missile’s mid-guidance phase, according to the constructed nominal trajectory, an improved adaptive high-order sliding mode trajectory tracking controller (AHSTC) is proposed. In this method, the open-loop nominal trajectories are established according to the nonlinear programming and Gaussian pseudospectra method. A high-precision trajectory tracking controller is developed by designing a nonlinear sliding mode surface and an adaptive high-order sliding mode approaching law combined with the trajectory tracking nonlinear error model. To verify the effectiveness and superiority of the proposed method, analysis and simulation are carried out through the example of a missile mid-guidance phase tracking control. Compared to the linear quadratic regulator (LQR) and active disturbance rejection controller (ADRC) method, the simulation results show that the proposed AHSTC method shows faster convergence and improved tracking effect. Therefore, the proposed AHSTC method has a good results and engineering application value.

Heavy-duty unmanned tracked vehicles are characterized by large inertia and complex operating conditions, which may lead to vehicle instability and trajectory divergence during trajectory tracking control. To address the trajectory tracking problem of heavy-duty unmanned vehicles, a series control method combining Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) is proposed. In this framework, the MPC algorithm based on a small-angle assumption calculates the desired yaw rate by solving local path points. The first-order linear ADRC controller then drives the vehicle to track the desired yaw rate by controlling the motor torques on both sides. Compared with conventional control methods, the proposed control method achieves decoupled lateral-longitudinal dynamics for tracked vehicles while incorporating yaw rate response dynamics into the control scheme. Simulation results demonstrate that this control method enables accurate trajectory tracking for heavy-duty tracked vehicles.

Neural network-based active disturbance rejection control and its application in quadrotor unmanned aerial vehicle trajectory tracking.

This paper introduces tracking control method for robot manipulator based on Active Disturbance Rejection Control technique (ADRC). The main purpose of the ADRC technique is to obtain a proper estimation, cancel the total disturbance along with the assurance of a good trajectory tracking. First, the equations of motion of the robot manipulator is rearranged to be suitable for the ADRC technique. Then, an extended model is derived from the dynamic model. This extended model has an extra state includes the total disturbances, which are estimated online through the extended state observer (ESO). Finally, this estimated disturbance is compensated through a simple PD feedback controller. This control strategy is tested on two-DOF serial link manipulator and compared by a simple PID controller. Simulation results show the advantage of the ADRC system over the PID controller.

To address the challenges of accurately modeling the motion of deep-sea aquaculture vessels under the combined effects of dynamic disturbances from the external environment and coupled disturbances from the liquid levels within the aquaculture tanks, as well as the performance limitations of traditional Active Disturbance Rejection Control (ADRC), this study proposes an improved Active Disturbance Rejection Control strategy based on the ADRC structure, and applies it to the design of the controller for the dynamic positioning system of aquaculture vessels. Furthermore, to tackle the issues of numerous uncertain parameters and tuning difficulties inherent in ADRC, an Improved Whale Optimization Algorithm (IWOA) is introduced for parameter tuning. The optimization algorithm incorporates a chaotic optimization strategy and an improved adaptive inertia weight ω with nonlinear characteristics, which simplifies the complex process of constructing an adaptive function in the Grey Wolf Optimization (GWO) algorithm and addresses the slow global search speed and poor local optimization ability of the traditional Whale Optimization Algorithm (WOA). The final Simulink simulation results demonstrate that the ADRC controller designed with IWOA outperforms traditional WOA, GWO, and Genetic Algorithm-optimized ADRC controllers in terms of control effectiveness and disturbance rejection capability, improved the dynamic positioning control performance of aquaculture vessels.