International Journal of Robust and Nonlinear Control最新文献

In this paper, an adaptive optimal predictive control method based on improved prescriptive performance is proposed for the path tracking control of an underactuated two-wheeled mobile robot. The system is challenging to control due to its underactuated characteristics and nonlinear dynamics behavior. To improve the robustness of the system under uncertainty and reduce the jitter phenomenon, a new control strategy is designed by combining the improved prescribed performance control with adaptive optimal predictive control. The method ensures that the system output can satisfy the prescribed performance requirements within a specific time range and recover the stable operation faster under certain disturbances through the design of reasonable soft-constraint performance indexes. In addition, a rigorous stability analysis of the control system is carried out to demonstrate the boundedness of the error function and the transient and steady-state performances specified by the output tracking error. Simulation results show that the proposed control method can significantly improve the stability and control accuracy of the system under disturbances.

Precise modeling of real complex nonlinear systems is fundamental to control design, but reaching it in practical engineering and industrial applications is often challenging. Data-driven approaches, by effectively capturing complex nonlinear behaviors, can enhance the performance of nonlinear robust controllers. In this paper, a new intelligent discrete fractional model-free backstepping sliding mode controller is proposed to enhance the performance and stability of practical engineering systems by utilizing a reinforcement learning (RL)-based disturbance estimator. The proposed controller leverages the advantages of Caputo fractional-order discrete sliding mode control, offering higher adaptability in handling uncertainties and external disturbances. Additionally, a Caputo fractional-order RL-based disturbance estimator is designed to estimate and compensate for the existing disturbances, without requiring an accurate model of the system, which makes it more applicable for various real complex systems. All controller coefficients in this study have been optimally selected using the Ant Colony Optimization algorithm. Its performance is evaluated through simulations, comparing it to the conventional discrete Backstepping Sliding Mode Controller (DBSMC). The proposed intelligent model-free controller, due to its data-driven and model-free structure, has the potential to be applied to various practical engineering systems. Its effectiveness is demonstrated through a wind turbine case study under different operating scenarios. In this study, the wind turbine system under three scenarios: normal conditions, parametric uncertainty, and external disturbances are examined. The results demonstrate that the proposed method outperforms the DBSMC, with improvements of 69.25% in normal conditions, 65.28% under parametric uncertainty, and 76.23% under external disturbances. These findings highlight the superior performance and robustness of the proposed approach in various operating conditions.

This paper proposes a novel nonlinear $��{�H�}_{�\infty �}�$ control strategy for the suspended transport system of dual quadrotor Unmanned Aerial Vehicles (UAVs), aimed at achieving precise trajectory tracking under unknown disturbance. First, a non-redundant model of the dual quadrotor UAVs' suspended transport system is established, which can greatly reduce the singular value problem. Then, a hierarchical control structure is designed. For the outer-loop subsystem, a novel nonlinear $��{�H�}_{�\infty �}�$ control strategy is proposed. This approach incorporates the integral effects of all degrees of freedom in the state space representation. The integral effects of the uncontrolled degrees of freedom are integrated into the cost function. The nonlinear $��{�H�}_{�\infty �}�$ controller is then designed within a weighted Sobolev space framework. Subsequently, the stability of the closed-loop system is established via Lyapunov's theorem, and the analytical solution of the control law is derived. For the inner loop subsystem, Active Disturbance Rejection Control (ADRC) is employed. Comparative simulations demonstrate the proposed controller's superior performance and robustness over existing methods.

For heterogeneous multi-robot systems with unknown parameters and input constraints, the problem of simultaneous target tracking and collision avoidance is studied in this paper. A novel auxiliary system is proposed to generate the corresponding compensation signal to tackle the difficulty of controller design resulting from input constraints. For each heterogeneous robot system, a unique collision avoidance controller is constructed utilizing the repulsive potential field method. Then, the adaptive collisions-free tracking control strategy is presented under the control framework of the proposed auxiliary system. It can be proved via Lyapunov stability theory that the designed controllers can allow the heterogeneous multi-robot systems to track the reference target with collision avoidance and guarantee that all the closed-loop signals in the multi-robot systems are bounded. Subsequently, the validity of our control scheme is further verified through a common simulation example.

A formation-containment control strategy for unmanned surface vehicles (USVs) is proposed in this study through the integration of finite-time prescribed performance control theory with an event-triggered mechanism, while explicitly addressing actuator saturation and failure constraints. The control architecture is hierarchically structured into two layers: a formation layer and a containment layer. First, to enhance transient and steady-state performance under input saturation, a saturation-tolerant finite-time prescribed performance function (SFPPF) is developed. When input saturation occurs, performance boundaries are adaptively regulated by the SFPPF through autonomous adjustment mechanisms, effectively circumventing potential singularity issues. Subsequently, an event-triggered mechanism is proposed, with formation and containment controllers designed by integrating Lyapunov stability theory and the backstepping methodology, while simultaneously reducing communication bandwidth consumption and actuator fatigue. Actuator faults and environmental disturbances are estimated via an interval type-2 fuzzy neural network combined with parameter adaptive methods. Furthermore, theoretical analysis demonstrates that all closed-loop system signals remain bounded with finite-time convergence of formation and containment errors. Notably, the prescribed performance controller ensures that the event-triggered mechanism does not degrade system control accuracy. The effectiveness of the proposed formation-containment control framework is validated via numerical simulations with USV dynamic models.

This paper primarily investigates the problem of fault-tolerant consensus control in multi-agent systems with actuator faults. Firstly, a novel fault observer with intermediate variables is utilized, which can rapidly and effectively estimate both the states and faults of the multi-agent systems. Secondly, a dynamic event-triggered mechanism based on periodic sampling is introduced between the sensors and the observer, effectively reducing the waste of communication resources. Then, by observing the faults and states, a distributed fault-tolerant controller is established, enabling effective fault compensation. Additionally, $��{�H�}_{�\infty �}�$ control is employed to mitigate the disturbances' impact on the multi-agent systems, ensuring that all follower agents achieve consensus with the leader agent. Finally, the proposed method's effectiveness is validated through a simulation.

This paper addresses the problem of fault-tolerant control in discrete multi-agent systems affected by actuator faults, sensor faults, and measurement noises with or without bandwidth limitation. Without bandwidth limitation, a distributed unknown input observer based on adjustable parameters is first developed to estimate augmented information (states, faults, and noises). Subsequently, leveraging the estimated information, a distributed fault-tolerant controller is designed to achieve state-bounded consensus within the finite horizon. The proposed dual-distributed control strategy enhances the information-sharing capability within the cluster, thereby augmenting both the local observation accuracy and the overall tolerance. With limited bandwidth, a novel dynamic quantization strategy, incorporating an encoding and decoding mechanism, is devised to efficiently compress the transmitted data. Afterwards, the compressed data is utilized to design a quantized observer for output estimation, and a fault-tolerant controller for the average consensus achievement. Based on balancing data accuracy and bandwidth utilization, the proposed quantization scheme employs the time-varying quantization factor to address the challenge of synchronous quantization while offering enhanced data protection. The simulation outcomes confirm the validity of the proposed algorithms under respective environmental conditions.

This paper presents an integrated framework for time-sequential trajectory planning and adaptive control of a transfer manipulator operating in compact spaces, with the aim of ensuring the safety of operation, reducing total load transfer time, and improving efficiency. Unlike conventional approaches that treat trajectory planning and control as separate processes, this work proposes a tightly coupled framework that simultaneously optimizes both trajectory generation and motion control for enhanced performance. First, a radial basis function neural network (RBFNN) is employed to online approximate state-dependent unknown nonlinearities in the system. Second, a composite nonlinear disturbance observer (NDO) incorporating RBFNN approximation results is designed to compensate for both external disturbances and approximation errors, thereby significantly reducing the system uncertainties. Third, an adaptive finite-time prescribed performance (AFPP) control method is developed to guarantee output constraints, establishing fundamental safety conditions for time-overlapped trajectory planning. Based on this high-precision controller with guaranteed output constraints, time-sequential trajectories with overlapping phases are optimally designed considering physical space constraints, which not only ensures operational safety in compact spaces but also substantially improves load transfer efficiency. The simulation results validate the robustness and effectiveness of the proposed approach, demonstrating significant improvements in both safety and control precision in highly constrained environments.