Robust Adaptive Trajectory Tracking Research Articles

Fault-tolerant control of underactuated unmanned surface vessels in presence of deception and injection attacks

This paper studies the trajectory tracking control problem of underactuated unmanned surface vessels (USVs) under the influence of internal and external uncertainties, actuator faults, and injection and deception attacks. In the control design, we separately designed virtual adaptive laws to compensate for the time-varying gains suffered by the kinematics channel and combined the event-triggered control (ETC) mechanism to establish an online approximator to compensate for the time-varying gains of the kinematics channels and the loss-of-effectiveness (LOE) and bias faults suffered by the system. Combining robust neural damping technology and finite-time disturbance observer (FTDO), the dynamic uncertainties and external disturbances are suppressed. Finally, a novel robust adaptive trajectory tracking control scheme is proposed. The scheme achieves active compensation for heterogeneous uncertainties including internal and external uncertainties, actuator faults, and attack signals. The Lyapunov stability theory analysis shows that all signals of the tracking system are bounded. The simulation results prove the effectiveness of this control scheme.

Robust Adaptive Control Based on Incremental Nonlinear Dynamic Inversion for a Quadrotor in Presence of Partial Actuator Fault

This paper presents a novel nonlinear robust adaptive trajectory tracking control architecture for stabilizing and controlling a quadrotor in the presence of actuator partial faults. The proposed control strategy utilizes an Incremental Nonlinear Dynamic Inversion (INDI) algorithm as the baseline controller in the inner loop and augments a nonlinear model reference adaptive controller in the outer loop to ensure robustness against unmodeled faults. Additionally, a modified PID controller is introduced in the most outer-loop to track the desired path. The effects of actuator faults are modeled by considering sudden variations in motor thrust and torques. To enhance the control algorithm's robustness, a projection operator is employed in the robust adaptive structure. Comparative performance evaluations with a previous successful algorithm implemented on a quadrotor model demonstrate that the proposed controller achieves full controllability of the faulty quadrotor in pitch, roll, and yaw channels in the presence of actuator partial faults up to 50%.

Robust adaptive tracking control for dynamic positioning ships subject to dynamic safety constraints and actuator saturation

This paper proposed a novel robust adaptive trajectory tracking control scheme for dynamic positioning (DP) ships subjected to unknown time-varying disturbances, unknown model parameters, dynamic safety constraints, and actuator saturation. The dynamic safety constraints were guaranteed based on the tan-type barrier Lyapunov function (BLF) technique, which ensured the position and velocity of the DP vessel within the given constraints. Moreover, the radial basis function neural network (RBFNN) was adopted to approximate the unknown parameters of the dynamics model, and an adaptive neural disturbance observer (ANDO) was designed to compensate for lumped disturbances. An anti-windup system was developed to handle the actuator saturation effect. By combining the above techniques and back-stepping method, the final control law was presented, and the semi-global asymptotic convergence was achieved via rigorous theoretical analysis and Lyapunov proof. The results of simulations demonstrated the superiority and robustness of this approach in resolving the trajectory tracking challenges of DP ships. Moreover, the proposed control scheme is highly effective in optimizing the trajectory tracking of DP ships, as it takes into account the multiple constraints that may arise during the process, which is a valuable contribution to the field of DP ship trajectory tracking, providing a more comprehensive and effective solution to this complex problem.

Robust adaptive trajectory tracking for wheeled mobile robots based on Gaussian process regression

In this paper, we propose a novel learning-based robust adaptive trajectory tracking controller for wheeled mobile robots (WMR) subject to velocity input uncertainties. Gaussian process regression (GPR) is employed in view of its powerful estimation ability and wide scope of applications as a nonparametric regression method. The velocity uncertainties are estimated online using the real-time measured data. The prediction mean and variance of the GPR are used to counteract the effect of the uncertainties and to design the robust control term, respectively. A continuous robust controller is proposed which has the advantage of achieving asymptotic convergence of the trajectory tracking error instead of uniformly ultimately boundedness. Comparisons with the neural network based adaptive controller and a state of the art GPR-based design demonstrate the effectiveness of the proposed control strategy.

Robust adaptive self‐structuring neural networks tracking control of unmanned surface vessels with uncertainties and time‐varying disturbances

AbstractThe trajectory tracking control problem of unmanned surface vessels (USVs) with uncertainties and time‐varying disturbances is investigated. A robust adaptive trajectory tracking control scheme is proposed based on finite‐time control and a self‐structuring neural networks (SSNN) identifier, which can obtain satisfactory performance with an norm‐bounded, expected attenuation level within a finite time. The SSNN is developed to approximate USVs system uncertainties and external disturbances by online learning. Most importantly, a balance is achieved between the optimal number of neurons and the expected performance, which saves significant network resources. The Lyapunov stability analysis shows that the scheme ensures convergence of the tracking error to a small neighborhood around zero in finite time, while all the other closed‐loop signals remain bounded. Moreover, the application of a high‐gain observer effectively reduces the cost of velocity sensors. The feasibility and effectiveness of this control scheme are verified by theorem analysis and numerical simulations.

Robust Adaptive Trajectory Tracking for Wheeled Mobile Robots Based on Gaussian Process Regression

Robust Adaptive Trajectory Tracking for Wheeled Mobile Robots Based on Gaussian Process Regression

Robust adaptive tracking control of UMSVs under input saturation: A single-parameter learning approach

This paper investigates the robust adaptive trajectory tracking control problem for unmanned marine surface vessels (UMSVs) subject to nonparametric uncertainties and external disturbances under input saturation. The non-smooth saturation nonlinearity is replaced by the Gaussian error function. The lumped uncertain term including nonparametric uncertainties, external disturbances and approximation error is formulated by the linear parameterization-like form with a single unknown parameter. Combining the parameter adaptive technique with backstepping design tool, single parameter learning-based tracking control schemes are developed, which involves both full state feedback and output feedback control. The theoretical analysis indicates that under our proposed schemes, all signals in the closed-loop trajectory tracking control system are bounded. Simulation results and comparison illustrate the effectiveness of the developed schemes.

Robust Adaptive Trajectory Tracking Control Based on Sliding Mode of Electrical Wheeled Mobile Robot

Robust Adaptive Trajectory Tracking Control Based on Sliding Mode of Electrical Wheeled Mobile Robot

Robust adaptive trajectory tracking of nonlinear systems based on input-to-state stability tracking control Lyapunov functions

Abstract In this paper, we propose a trajectory-tracking controller for input-affine nonlinear systems, which guarantees robustness in the presence of both input uncertainties and external disturbances. The framework of input-to-state stability tracking control Lyapunov function(ISS-TCLF) is employed to construct a disturbance attenuation tracking controller. Then, we introduce a Lyapunov-based adaptive compensator to mitigate the effect of input uncertainties. The proposed controller guarantees that (i) the closed-loop system is input-to-state stable, and (ii) the tracking error converges to zero if the disturbance is a constant signal. Simulation results of a wheeled mobile robot validate the effectiveness of the proposed controller.

Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo algorithm

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law; which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques (i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem.

Global Robust Adaptive Trajectory Tracking Control for Surface Ships Under Input Saturation

In this paper, we investigate the trajectory tracking control problem of surface ships subject to unknown parameters, unknown time-varying disturbances, and input saturation. The input saturation is approximated using a Gaussian error function. The compounded uncertain term caused by the unknown parameters and disturbances is transformed into a linear parametric form with a single parameter called virtual parameter. Then, a novel robust adaptive ship trajectory tracking control law is designed using the adaptive vector-backstepping design method, where the adaptive law online provides the estimation of the virtual parameter. Further, a finite-time control scheme is developed injecting a power function vector of the position and velocity errors into the proposed control scheme such that the control performance is improved. Both our control schemes are simple to compute and easy to implement in engineering applications. Theoretical analysis indicates that the designed control laws can force the surface ship to track the desired trajectory while guaranteeing the global uniform ultimate boundedness of all the signals in the closed-loop trajectory tracking control system of surface ships. Simulation results and comparison verify the effectiveness of our novel robust adaptive trajectory tracking control schemes.

Machine learning–based robust trajectory tracking control for FSGR

Here, a robust adaptive trajectory tracking algorithm is proposed for free-form surface grinding robot (FSGR) in metal surface production line. Machine-learning method is used for robot dynamic approximation which is hard to obtain directly. Adaptive law is proposed to adjust the neural network parameters. Sliding-mode control is employed to deal with the disturbance, joint friction, and approximation error of the adaptive machine learning. The scheme based on machine-learning feedforward compensation can significantly reduce the chattering of sliding mode. The performance of the proposed control scheme is illustrated through simulations.

A Robust Adaptive Trajectory Tracking Algorithm Using SMC and Machine Learning for FFSGRs with Actuator Dead Zones

The actuator dead zone of free-form surface grinding robots (FFSGRs) is very common in the grinding process and has a great impact on the grinding quality of a workpiece. In this paper, an improved trajectory tracking algorithm for an FFSGR with an asymmetric actuator dead zone was proposed with consideration of friction forces, model uncertainties, and external disturbances. The presented control algorithm was based on the machine learning and sliding mode control (SMC) methods. The control compensator used neural networks to estimate the actuator’s dead zone and eliminate its effects. The robust SMC compensator acted as an auxiliary controller to guarantee the system’s stability and robustness under circumstances with model uncertainties, approximation errors, and friction forces. The stability of the closed-loop system and the asymptotic convergence of tracking errors were evaluated using Lyapunov theory. The simulation results showed that the dead zone’s non-linearity can be estimated correctly, and satisfactory trajectory tracking performance can be obtained in this way, since the influences of the actuator’s dead zone were eliminated. The convergence time of the system was reduced from 1.1 to 0.8 s, and the maximum steady-state error was reduced from 0.06 to 0.015 rad. In the grinding experiment, the joint steady-state error decreased by 21%, which proves the feasibility and effectiveness of the proposed control method.

Robust adaptive trajectory tracking control of underactuated autonomous underwater vehicles with prescribed performance

SummaryIn this paper, a novel robust adaptive trajectory tracking control scheme with prescribed performance is developed for underactuated autonomous underwater vehicles (AUVs) subject to unknown dynamic parameters and disturbances. A simple error mapping function is proposed in order to guarantee that the trajectory tracking error satisfies the prescribed performance. A novel additional control based on Nussbaum function is proposed to handle the underactuation of AUVs. The compounded uncertain item caused by the unknown dynamic parameters and disturbances is transformed into a linear parametric form with only single unknown parameter called virtual parameter. On the basis of the above, a novel robust adaptive trajectory tracking control law is developed using dynamic surface control technique, where the adaptive law online provides the estimation of the virtual parameter. Strict stability analysis indicates that the designed control law ensures uniform ultimate boundedness of the AUV trajectory tracking closed‐loop control system with prescribed tracking performance. Simulation results on an AUV in two different disturbance cases with dynamic parameter perturbation verify the effectiveness of our adaptive trajectory tracking control scheme.

Robust adaptive trajectory tracking control of underactuated surface vessel in fields of marine practice

To enhance the control system robustness of an underactuated surface vessel with model uncertainties and environmental disturbances, a robust adaptive trajectory tracking algorithm based on proportional integral (PI) sliding mode control and the backstepping technique is proposed. In this algorithm, a continuous adaptive term is constructed to reduce the chattering magenta phenomenon of the system due to the sliding mode surface, and the backstepping technique is employed to force the ship position and the orientation on the desired values. In addition, we have proved the Lyapunov stability of the closed-loop system under the discontinuous environment disturbances or the thruster discontinuity. Finally, numerical simulations are performed to demonstrate the effectiveness of this novel methodology.

Robust adaptive tracking control of wheeled mobile robot

This paper considers the problem of robust adaptive trajectory tracking control for a wheeled mobile robot (WMR). First, the trajectory tracking of the WMR is converted to a problem of the stabilization of a double integral system. Next, a continuous finite-time control method is employed to design a tracking controller. Then, a disturbance observer and an adaptive compensator are designed to cooperate with the tracking controller for dealing with system uncertainties of the WMR. Finally, a switching adaptive law is presented in combination with the boundary layer approach to attenuate the chattering in the adaptive compensator. As a result, the control system yields the ultimate boundedness of both the tracking error and the adaptive gain. Simulation results demonstrate the validity of the new method.

Nonlinear robust adaptive trajectory tracking control for spacecraft proximity operations

Six degrees of freedom (6DOF) relative motion trajectory tracking control is studied for a chaser spacecraft with parametric uncertainties and external disturbances to approach a tumbling target in deep space. After modeling the nonlinear and coupled relative motion dynamics in the chaser body-fixed frame, reference trajectories of the relative attitude and relative position are designed, then a 6DOF integrated trajectory tracking controller is developed based on a robust adaptive method. Asymptotic stability of the 6DOF closed-loop system is derived within the Lyapunov framework. The performance of proposed controller is demonstrated through a numerical simulation example.

Robust Adaptive Trajectory Tracking Sliding mode control based on Neural networks for Cleaning and Detecting Robot Manipulators

This paper proposes an robust adaptive control method based on Radial Basis Function Neural networks (RBFNN) to investigate the joint position control for periodic motion and predefined trajectory ...

Robust adaptive trajectory tracking independent of models for robotic manipulators

AbstractThis article proposes a robust adaptive trajectory control scheme for robotic trajectory tracking under uncertainties. The control scheme is globally exponentially convergent without the knowledge of the robotic dynamics and is simple in structure with a small computation. It can make the trajectory error convergent to an arbitrary small region. Lyapunov approach is used to analyze the stability and the robustness of this control scheme. Experiments on a two‐link direct‐drive robotic manipulator verify the validity of the proposed control scheme. © 2001 John Wiley & Sons, Inc.