International Journal of Robust and Nonlinear Control最新文献

Rigid body systems usually consider measurements of the pose of the body using onboard cameras/LiDAR systems, that of linear acceleration using an accelerometer and of angular velocity using an IMU. However, the measurements of the linear acceleration and angular velocity are usually biased with an unknown constant or slowly varying bias. Estimating this bias is important to recover the true linear acceleration and angular velocity of the system. We propose two measurement bias estimators for such systems under assumption of boundedness of angular velocity. We also provide continuous estimates of the linear velocity of the system. These estimates are globally exponentially convergent to the true state of the rigid body system. The first observer assumes knowledge of bounds of the angular velocity, while the second observer uses a Riccati observer to overcome this limitation. We validate that the observer is able to estimate the bias using numerical simulations and experimental data collected from an Intel Realsense camera.

Underactuated systems have been widely studied and applied due to their unique performance advantages. This paper investigates constraint-following control based on an extended state observer to address the control problems in underactuated systems caused by uncertainty and input saturation. Firstly, a modified extended state observer is used to estimate the equivalent acceleration variation caused by uncertainty. Secondly, relying on the equivalent acceleration estimation, a constraint-following uncertainty compensation control is proposed, enabling the system to autonomously counteract uncertainty. Finally, stability is guaranteed by a gain saturation strategy while avoiding the control inputs from exceeding the preset boundaries. In addition, a projection operator is selected to decompose uncertainty into matched and mismatched portions. A necessary and sufficient condition to ensure that mismatched uncertainty does not affect stability is also given, which reduces the number of required effective constraints. The Lyapunov method is used to prove the uniform ultimate boundedness of the estimation error and the first-order tracking error. Simulation examples illustrate the effectiveness of the proposed control scheme.

The predefined-time nonsingular fast terminal sliding mode (NFTSM) control problem is studied based on a prescribed-time disturbance observer (PTDO) for a robotic manipulator system with Denial-of-Service (DoS) attack. Firstly, a new predefined-time NFTSM control strategy is constructed to enable fast-tracking performance for the uncertain manipulator with DoS attack. Then, a well-designed PTDO is developed to deal with the unknown lumped uncertainty, and the observation error variable is proved to converge within a prescribed time. Next, the entire prescribed-time disturbance observer-based NFTSM controller is devised, and the error system of the uncertain manipulator is proved to be tunable predefined-time stability (PdTS) without DoS attack. Meanwhile, the DoS attack issue is addressed, and the quantitative relationship between the PdTS and the attack parameters is formalized utilizing a switched system framework. Finally, the developed prescribed-time disturbance observer-based NFTSM control method is demonstrated via a 2-link robotic manipulator, which provides a higher quality control performance compared with existing control methods.

This paper proposes a monotone tube-based optimal and prescribed-time backstepping consensus control strategy for nonlinear multi-agent systems in strict-feedback form. The control laws are derived from a combination of optimal policy obtained through reinforcement learning (RL) and prescribed-time control techniques. This approach ensures that followers can track the leader within a predefined time frame, irrespective of initial conditions, through online learning. The model accounts for uncertainties and disturbances in all state variables without assuming the boundedness of unknown nonlinear functions. The gradient descent method is utilized to streamline the adaptation laws for updating neural network weights in reinforcement learning, which are modified to guarantee the system's prescribed-time stability. To address output constraints, a Monotone Tube Boundary (MTB) methodology is employed, ensuring the prescribed performance of the tracking error while maintaining output constraints. Unlike the Prescribed Performance Function (PPF), the MTB approach allows for the pre-assignment of transient characteristics for tracking error, avoiding excessive overshoot and enhancing adjustability. A solution is proposed to mitigate the complexity explosion caused by the derivative of virtual control laws, treating these derivatives and unknown functions as general nonlinear functions at each step. A neural network and a disturbance observer estimate unknown nonlinear functions and disturbances, ensuring prescribed-time stability. The neural network inputs are truncated to require only the output of neighboring agents for control, resulting in a simpler control law and reducing energy consumption for data transmission. The method's effectiveness is demonstrated through simulations, showcasing its superiority over existing approaches.

As a typical underactuated system, the overhead crane is highly susceptible to oscillations and instability under external disturbances. To address this issue, an anti-disturbance control strategy is proposed by integrating the back-stepping method with a nonlinear disturbance observer. First, the crane system is reformulated into a novel strict-feedback form without linearization or approximation, and the lumped disturbances are defined accordingly. Subsequently, a nonlinear disturbance observer is designed to estimate these disturbances in real time. Based on the estimation results, a back-stepping-based controller is constructed, and a robust enhancement term is added to suppress the influence of observation errors. Lyapunov stability theory is then employed to rigorously prove that the closed-loop system is uniformly ultimately bounded under various disturbances, ensuring that the state errors remain within a prescribed bound. Finally, simulations in MATLAB/Simulink demonstrate the effectiveness and robustness of the proposed control strategy.

This paper proposes novel distributed optimization algorithms, which exhibit predefined-time convergence for two classes of distributed time-varying optimization problems—consensus optimization and resource allocation—over the directed communication network. First, based on the time-varying scaling function mechanism, a distributed average estimator is ingeniously designed to estimate the global states of the optimized multi-agent systems (MASs). Utilizing the estimator's outputs, a predefined-time convergence algorithm is constructed, which makes the decision variable of the MASs converge to the optimal solution of the consensus optimization problem after $��{�T�}_{�1�}�\to �{�T�}_{�2�}�\to �{�T�}_{�3�}�$. Then, for the quadratic objective functions, a distributed optimization algorithm based on Lagrangian multipliers is developed to solve the time-varying resource allocation problem within the predefined time $��{�T�}_{�2�}�$. Finally, two simulation cases verify the effectiveness of the proposed algorithms.

This paper proposes a novel control method for an active suspension system aimed at achieving precise and rapid regulation of vehicle body height and attitude, while significantly enhancing ride comfort and overall vehicle stability. A state-space model of a half-car active suspension system is first formulated. To better reflect practical scenarios, the model incorporates system uncertainties. Building on this foundation, an improved periodic delayed feedback control method based on sliding mode control is developed to ensure that the system states converge within a prescribed time, regardless of initial conditions. To further improve robustness, the sliding mode switching function is refined to effectively mitigate chattering. Compared with PID and adaptive backstepping control, the proposed method exhibits significant advantages in control accuracy and convergence rate. The effectiveness of the proposed method is validated through comprehensive numerical simulations.