Uncertain External Disturbances Research Articles

Robust optimal tracking control of multiple autonomous underwater vehicles subject to uncertain disturbances

AbstractThis paper considers the problem of robust optimal tracking control of multiple autonomous underwater Vehicles (AUVs) subject to uncertain external disturbances. First, the Takagi‐Sugeno (T‐S) fuzzy based technique is utilized to convert the high‐order nonlinear multi‐AUV system into a series of linearized subsystems. Second, a novel fully distributed sliding mode control (FDSMC) strategy is proposed to attenuate the disturbances. Meanwhile, the leader‐following consensus and the nearly optimization of the energy‐cost function for the multi‐AUV system can be achieved simultaneously through the designed optimal nominal control protocol. Moreover, the proposed control strategy has more mild constraints on the communication topologies. Finally, the effectiveness of the proposed FDSMC strategy is verified by numerical simulation studies.

Ultralocal Model-Free Adaptive Supertwisting Nonsingular Terminal Sliding Mode Control for Magnetic Levitation System

The magnetic levitation technology has attracted blooming attention from all walks of life thanks to its superiorities such as non-contact and active control. While the intrinsic features of open-loop instability and strong nonlinearity, along with the uncertain external disturbance make the levitation control particularly significant and difficult. For speeding up the convergence and obtaining a better stability and disturbance robustness, this paper proposes an ultra-local model-free adaptive super-twisting nonsingular terminal sliding mode control (ULMF-ASTNTSMC) strategy for the magnetic levitation system. Different from the conventional model-based control, ultra-local model-free control breaks the constraint of a precise system model. Therefore, an improved second-order ultra-local model is established for the magnetic levitation system. For the benefit of finite time convergence and non-singularity, the nonsingular terminal sliding mode surface is chosen. As a second-order algorithm, super-twisting (ST) algorithm can fundamentally suppress the chattering effect of the sliding mode control (SMC). An adaptive super-twisting algorithm is designed for the further improvement of the system dynamic response. The stability of the proposed scheme is strictly verified based on the Lyapunov stability theory. Simulation and experimental comparisons with the existing control methods validate the exceeding performance of the proposed strategy under various operating conditions.

Adaptive event-triggered tracking control for nonlinear systems with prescribed performance: A time-domain mapping approach

This paper proposed an event-triggered adaptive fuzzy prescribed time control strategy for a class of nonlinear systems with uncertain external disturbances and unknown virtual control coefficients under prescribed performance. A novel time-domain mapping technique is proposed to transform the prescribed time tracking control problem into an asymptotic convergence problem. An observer is designed to estimate for the external disturbance and fuzzy approximation error of fuzzy logic systems (FLSs). The problem of “explosion of complexity” is addressed by applying dynamic surface control (DSC) technique to backstepping. An adaptive fuzzy event-triggered strategy is designed using the Lyapunov stability theorem. This strategy ensures that, after time-domain mapping, the tracking error asymptotically converges to zero. Additionally, there is no Zeno behavior and all system signals are bounded. Finally, the effectiveness of the proposed scheme is verified through three simulation examples.

Non-fragile adaptive fuzzy observer for nonlinear systems with uncertainties

In this article, a non-fragile adaptive fuzzy observer is proposed for nonlinear systems with uncertain external disturbance and measurement noise. Firstly, the nonlinear system is augmented by an output filtered transformation. The output with measurement disturbance is put into the state equation of the augment system. Then, we introduce fuzzy logic system (FLS) to approximate the measurement disturbance, and construct an augmented non-fragile adaptive fuzzy observer for the augment system. A Lyapunov function is constructed to reveal that the characteristic of estimation errors is uniformly ultimately boundedness (UUB). Finally, two experimental simulations are offered to confirm the validity of the proposed design method.

Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller.

Tracking control and disturbance rejection of micro quadrotor via adaptive internal model

In this paper, to achieve attitude tracking and disturbance rejection of micro quodrotor in the presence of uncertain external disturbances, the output regulation technique is utilized. First of all, the model that established regarding micro quadrotor is transformed to a stabilization problem of the second-order augmented system by employing the internal model. Second, an adaptive internal model is designed via exosystem information. The nonlinear unknown functions of the system are approximated by fuzzy logic systems, and the fuzzy adaptive laws are designed to tackle the uncertainty parameters. Then the new adaptive fuzzy state controllers and adaptive control laws are proposed by combining the backstepping technique and the adaptive control method. Unlike the traditional controller design method, the proposed controllers and adaptive control laws are designed respectively for roll angle, pitch angle, yaw angle and the height of the four-channel helicopter. Moreover, the new algorithm can perform well in attitude tracking and disturbance rejection of the closed-loop systems. Finally simulation results have demonstrated the effectiveness of the proposed approach.

Distributed Observer-Based Leader-Follower Consensus of Multiple Euler-Lagrange Systems.

This article investigates the leader-follower consensus problem of multiple Euler-Lagrange (EL) systems, where each agent suffers uncertain external disturbances, and the communication links among agents experience faults. Besides, we consider a more general case that only a portion of followers can measure partial components of leader's output and access the dynamic information of leader. The main idea of solving the consensus problem in this article is proceeded in two steps. First, we design an adaptive distributed observer to estimate the full state information of leader in real time with resilience to communication link faults. Second, based on the proposed distributed observer, we propose a proportional-integral (PI) control protocol for each agent to track the trajectory of leader, which is model-independent and robust to uncertain external disturbances. Distinct from the existing leader-follower consensus protocols of multiple EL systems, the proposed distributed observer-based PI consensus protocol in this article is model-independent, which is irrelevant to the structures or features of EL system model. Finally, we present a simulation example to show the resilience of the above adaptive distributed observer and the robustness of the distributed observer-based consensus protocol.

Walking position commanded NAO robot using nonlinear disturbance observer-based fixed-time terminal sliding mode

The walking stability of a humanoid robot is a fundamental problem due to the complex nonlinear dynamic model of the robot’s legs. This work introduces the performance tracking control for the humanoid NAO robot by using a Nonlinear Disturbance Observer (NDO)-based Fixed-time Terminal Sliding Mode (FTSM). The influence of uncertain external disturbance is considered while implementing the control strategy to improve the walking motion of the NAO robot. An NDO is adapted to estimate the uncertainties and external disturbances. A novel FTSM surface is proposed to drive the tracking errors to zero in fixed-time. The designed NDO-based FTSM control law achieves robustness while reducing the chattering phenomenon. The Lyapunov’s stability theory is used to establish the fixed-time stability of the sliding surface and system states under the proposed control method. To validate the performance of the proposed NDO-based FTSM control, a real-time experiment was conducted on a humanoid NAO robot to demonstrate the improved tracking performance in the presence of the uncertain perturbation effect. The effectiveness of the proposed controller design is validated on a flat, upward inclined surface, and compared to another controller.

A Theoretical Study on the Resilience Evaluation Method of Operational Road Tunnel Systems

Road tunnel operation will suffer from a lot of uncertain external disturbances, which will greatly affect the operational safety of road tunnels and even block traffic. Focusing on road tunnel operation safety and disaster-resistant ability, the concept of resilience is introduced to provide a scientific and effective basis for road tunnel operation and emergency management. In this paper, the concept of tunnel system resilience was proposed based on the concept of system resilience. A theoretical analysis model of road tunnel resilience was built to describe the change in road tunnel system function over time due to external disturbances (e.g., fires, traffic accidents, floods, earthquakes). Five fundamental attributes of road tunnel system resilience were proposed to describe the resilience level. A resilience evaluation method for road tunnels was proposed based on the functional network. The vulnerable links of road tunnels subjected to external disturbances can be analyzed using this method. This study will provide important references for the resilience evaluation method of road tunnels and risk mitigation strategies.

Self-Organizing Robust Fuzzy Neural Network for Nonlinear System Modeling.

Fuzzy neural network (FNN) is a structured learning technique that has been successfully adopted in nonlinear system modeling. However, since there exist uncertain external disturbances arising from mismatched model errors, sensor noises, or unknown environments, FNN generally fails to achieve the desirable performance of modeling results. To overcome this problem, a self-organization robust FNN (SOR-FNN) is developed in this article. First, an information integration mechanism (IIM), consisting of partition information and individual information, is introduced to dynamically adjust the structure of SOR-FNN. The proposed mechanism can make itself adapt to uncertain environments. Second, a dynamic learning algorithm based on the α -divergence loss function ( α -DLA) is designed to update the parameters of SOR-FNN. Then, this learning algorithm is able to reduce the sensibility of disturbances and improve the robustness of Third, the convergence of SOR-FNN is given by the Lyapunov theorem. Then, the theoretical analysis can ensure the successful application of SOR-FNN. Finally, the proposed SOR-FNN is tested on several benchmark datasets and a practical application to validate its merits. The experimental results indicate that the proposed SOR-FNN can obtain superior performance in terms of model accuracy and robustness.

Research on state-parameter estimation of unmanned Tractor—A hybrid method of DEKF and ARBFNN

Unmanned tractor relies on multi-sensor information collection to obtain the current state or parameters. However, when driving in complex field, it will inevitably suffer from the uneven ground, the impact of crop straw and clods et al., which usually poses considerable challenges for multi-sensor to obtain stable and accurate value of states and parameters to realize tractor automatic lane guidance control. Therefore, a novel state and parameter estimation method by mixing the dual extended Kalman filter (DEKF) technology and adaptive radial basis function neural network (ARBFNN) technology is proposed in this paper. Firstly, DEKF technique is applied to estimate key states and initial model time-varying parameters–front/rear axle cornering stiffnesses at the same time. Then, in order to further improve the accuracy of estimation value of front/rear axle cornering stiffnesses during the automatic lane guidance control process, an ARBFNN technology is investigated by taking the heading error, lateral error and initial estimation value of front/rear axle cornering stiffnesses from DEKF as inputs to approach ideal estimation value. Finally, results of automatic lane guidance control scenarios from both simulation and hardware-in-loop (HIL) implementation show that the proposed hybrid estimated method of DEKF and ARBFNN can robustly obtain satisfactory estimation value and automatic lane guidance control performance for tractor when the control system is characterized by both time-varying model parameters and uncertain external energy-bounded disturbance. A comparative study is also conducted to investigate cases to show its effectiveness when the hybrid DEKF-ARBFNN state and parameter estimation method is used and when it is not.

Output Feedback Control of Overhead Cranes Based on Disturbance Compensation

In practice, various factors such as friction, unmodeled dynamics and uncertain external disturbances often affect overhead cranes. The existing crane control methods often neglect these factors or address these factors by robust techniques. Moreover, most of them do not take input saturation into account and require full-state feedback. In this paper, taking the practical issues of uncertain disturbances, input saturation and output feedback into account, we propose an input-saturated output feedback control strategy for the underactuated two-dimensional (2-D) overhead crane systems with uncertain disturbances. Specifically, we first design a disturbance observer that can accurately estimate the external disturbance. Then, the virtual horizontal location signal is introduced and the new energy storage function is constructed. A novel composite control method for overhead crane systems is proposed based on the developed disturbance observer and the new energy storage function. The stability and convergence analysis are given through Lyapunov techniques and LaSalle’s invariance theorem. In order to verify the performance of the proposed controller, we perform a series of simulation tests and compare the proposed method with some existing control methods.

Event-Triggered Bounded Consensus Tracking for Second-Order Nonlinear Multi-Agent Systems with Uncertainties.

This paper is concerned with event-triggered bounded consensus tracking for a class of second-order nonlinear multi-agent systems with uncertainties (MASs). Remarkably, the considered MASs allow multiple uncertainties, including unknown control coefficients, parameterized unknown nonlinearities, uncertain external disturbances, and the leader's control input being unknown. In this context, a new estimate-based adaptive control protocol with a triggering mechanism is proposed. We rule out Zeno behavior by testifying that the lower bound on the interval between two consecutive events is positive. It is shown that under the designed protocol, all signals caused by the closed-loop systems are bounded globally uniformly and tracking errors ultimately converge to a bounded set. The effectiveness of the devised control protocol is demonstrated through a simulation example.

Fast terminal synergetic control for morphing quadcopter with time-varying parameters

To enhance flexibility and improve versatility, recent research has focused on the development of variable structure vehicles. In line with this, this paper introduces a morphing quadcopter with time-varying parameters that enables independent rotation of its arms around the main body. This study highlights the influence of these parameters on the geometric properties, dynamics, flight stability, and control of this aerial robot, while considering nonlinear functions, unknown parameters, and external disturbances. The system dynamics are derived using Newton-Euler formalism, and a robust control approach based on synergetic theory is proposed. The distinctive features of this strategy are demonstrated through finite-time convergence, enhanced control accuracy, and effective chattering suppression. In the control aspect, a Synergetic Controller (SC) is initially developed and further improved by incorporating fast finite-time convergence of the state variables. A comparative study is conducted, quantitatively and qualitatively, among four controllers: BackStepping (BS) controller, Sliding Mode Controller (SMC), SC, and Fast Terminal Synergetic Controller (FTSC). The FTSC exhibits numerous advantages, including rapid tracking towards desired states, adaptability to varying system parameters and uncertain external disturbances, as well as suitability for digital implementation. To conduct a significant comparative study, a Genetic Algorithm is employed to select the optimal gains for each control technique. Additionally, to imitate the real situation as much as possible, external disturbances are considered in the performed numerical simulation. Overall, the results demonstrate that the proposed control technique outperforms in terms of accuracy and robustness. It offers superior control performance and effective handling of system variations and external disturbances, making it a promising approach for the morphing quadcopter system.

Robust adaptive finite-time course tracking control of vessel under actuator attacks

This paper studies the course tracking control problem of unmanned surface vessels under the influence of uncertain dynamics, external unknown disturbances, constraints, and actuator attacks. In the design of the control scheme, adaptive technology is applied to approach the uncertain dynamics of the system, and a nonlinear finite-time disturbance observer is established to reconstruct the actuator attack signal and the unknown time-varying disturbances online. Combining disturbance compensation and adaptive technology, a finite-time course tracking control scheme is designed. The control scheme does not need to obtain the prior knowledge of the model in advance, and it has good robustness in the face of uncertain dynamics within the system, external disturbances, and actuator attacks. A rigorous stability analysis is provided for the control scheme based on the Lyapunov stability theory. Finally, the simulation shows that the proposed control scheme can effectively resist the influence of actuator attacks and external uncertain disturbances.

Integral back-stepping active disturbance rejection control for piezoelectric stick-slip drive nanopositioning stage

Piezoelectric stick-slip driven nanopositioning stage (PSSNS) with nanometer resolution has been widely used in the field of micro-operation. However, it is difficult to achieve nanopositioning over large travel, and its positioning accuracy is affected by the hysteresis characteristics of the piezoelectric elements, external uncertain disturbances, and other nonlinear factors. To overcome the above-mentioned problems, a composite control strategy combining stepping mode and scanning mode is proposed in this paper, and an integral back-stepping linear active disturbance rejection control (IB-LADRC) strategy is proposed in the scanning mode control phase. First, the transfer function model of the system in the micromotion part was established, and then the unmodeled part of the system and the external disturbance were treated as the total disturbance and extended to a new system state variable. Second, a linear extended state observer was used as the core of the active disturbance rejection technique to estimate displacement, velocity, and total disturbance in real time. In addition, by introducing virtual control variables, a new control law was designed to replace the original linear control law and improve the positioning accuracy and robustness of the system. Furthermore, the effectiveness of the IB-LADRC algorithm was verified by simulation comparison experiments and experimentally validated on a PSSNS. Finally, experimental results show that the IB-LADRC is a practical solution for a controller capable of handling disturbances during the positioning of a PSSNS with a positioning accuracy of less than 20 nm, which essentially remains constant under load.

Quadrotor Cascade Control System Design Based on Linear Active Disturbance Rejection Control

In this paper, we propose a cascade control system design based on linear active disturbance rejection control (SMC&A-LADRC) to address the following quadrotor UAV problems: that the path is easily yawed when disturbed, the control parameters are difficult to optimize, and the tracking accuracy is low. The strategy can effectively eliminate external disturbances and adjust the controller parameters online so that the quadrotor UAV always flies on the optimal path to achieve energy optimization and long-endurance flight. The proposed cascade control system combines the advantages of sliding mode control (SMC) and linear active-disturbance rejection control (LADRC), using the linear extended state observer (LESO) to estimate the uncertain external disturbances and unmodeled internal dynamics of the quadrotor system, compensate for the uncertain signals under SMC chattering, optimize the PD controller parameters online using adaptive control to eliminate the effects of parameter deviations, and simplify the parameter adjustment process. Finally, a stability analysis of the quadrotor cascade control system is carried out by using the Lyapunov theorem of stability, and a simulation analysis is carried out using MATLAB to compare the results with those obtained with classical LADRC. The test results indicate that the control strategy of this paper is reasonable for utilization with a quadrotor attitude and displacement control system, allowing the quadrotor to fly on the optimal path with good anti-interference ability and a fast response speed.

Adaptive neural network finite‐time command filter tracking control for nonlinear systems with multiple coupling high‐order terms and disturbances

AbstractThis paper is devoted to the research of adaptive command filter tracking control for a class of uncertain nonlinear systems. The coexistence of multiple coupling high‐order terms, uncertain external disturbances, and unknown time‐varying coefficients makes the studied system more general compared with the existing results. By skillfully combining adaptive technology, command filter control, and backstepping method, a new type of adaptive command filter tracking control algorithm is designed. This controller not only solves the problem of complex explosion but also introduces compensation signals to achieve a higher precision tracking effect. Ultimately, the validity of the control algorithm is verified by a numerical simulation and a practical application model simulation.

A novel asymmetric barrier Lyapunov function-based fixed-time ship berthing control under multiple state constraints

The construction of an asymmetric barrier function with internal security set, while ensuring its invariance in the presence of interference, is a challenging task to achieve stability and security. In this paper, a translation transformation is used to construct a novel asymmetric Barrier Lyapunov Function. To the best of our knowledge, this is the first designed continuously differentiable barrier Lyapunov function (BLF) of this type. A new adaptive sliding mode control (SMC) method based on asymmetrical BLF is proposed for uncertain external disturbances. The robust control method can ensure the safety performance in the presence of model uncertainty with bounded tracking error of the ship trajectory and velocity. Its fixed-time convergence is then proved by the Lyapunov theory. The results show that it has stronger anti-interference ability in bad sea conditions, better robustness, and concise form. In addition, the system can deal with unknown disturbances, ensure the safe navigation, and keep the good tracking of the route under wind and current disturbance due to random sea conditions.

Global fuzzy adaptive asymptotic tracking control for unknown semi-linear parabolic distributed parameter systems

This paper is mainly devoted to investigating the globally asymptotic tracking problem of unknown semi-linear parabolic distributed parameter systems (SLPDPSs) with uncertain external disturbance. Firstly, an analysis of the well-posedness of the system solution is given based on the operator semigroup theory. Then, from the Lyapunov direct method, a distributed adaptive fuzzy controller (AFC) is designed so that the unknown SLPDPSs can asymptotically track target systems. More importantly, the globally asymptotic tracking condition can be obtained, which overcomes the semi-global stability results in the existing literatures. Finally, several simulation cases are introduced to verify the availability and practicability of the proposed method.