Nonlinear Systems Research Articles

Barrier Lyapunov Function-Based Output Feedback Control for a Class of Nonlinear Systems with Constraints

Barrier Lyapunov Function-Based Output Feedback Control for a Class of Nonlinear Systems with Constraints

Predefined-Time Adaptive Neural Control for Nonlinear Systems With Unknown Interconnections.

Adaptive control (AC) has been extensively proved as a promising strategy for nonlinear systems, especially in the dynamic and changeable environments. However, due to the unavoidable existence of unknown interconnections in the real nonlinear systems, it is difficult for typical AC to guarantee its stability within the bounded convergence time. Thus, to solve this issue, a predefined-time adaptive neural control (PTANC) scheme is developed for nonlinear systems with unknown interconnections in this aritcle. First, an integrated control framework, where the controlled objectives not only relate with each other but also change over time, is able to catch more characteristics of nonlinear systems than the existing works. Then, the proposed control scheme can address the impact of unknown interconnections on the system stability. Second, a predefined adaptive law mechanism is employed to estimate the unknown interconnections to assist in PTANC. Then, the proposed PTANC scheme can ensure its predefined-time stable in the occurrence of unknown interconnections. Third, a neural network-based self-regulating strategy is designed to construct the Lyapunov function to prove the stability of PTANC. Then, the comprehensive stability analysis can make PTANC scheme be successful applied in nonlinear systems. Finally, the proposed PTANC scheme is tested on the numerical simulation and BSM1 simulation platform. The experimental results illustrate that the envisioned control method attains exceptional performance.

Neural-network-based accelerated safe Q-learning for optimal control of discrete-time nonlinear systems with state constraints.

Neural-network-based accelerated safe Q-learning for optimal control of discrete-time nonlinear systems with state constraints.

Neural-network-based practical specified-time resilient formation maneuver control for second-order nonlinear multi-robot systems under FDI attacks.

Neural-network-based practical specified-time resilient formation maneuver control for second-order nonlinear multi-robot systems under FDI attacks.

Prescribed performance observer-controller design for MIMO nonlinear systems with unknown control directions.

Prescribed performance observer-controller design for MIMO nonlinear systems with unknown control directions.

Self-triggered neural tracking control for discrete-time nonlinear systems via adaptive critic learning.

Self-triggered neural tracking control for discrete-time nonlinear systems via adaptive critic learning.

Biological pattern formation in cell dynamics under cross-diffusion: An Isogeometric analysis perspective.

Biological pattern formation in cell dynamics under cross-diffusion: An Isogeometric analysis perspective.

Resonances in nonlinear systems with a decaying chirped-frequency excitation and noise

Resonances in nonlinear systems with a decaying chirped-frequency excitation and noise

Data-driven-based fully distributed event-triggered control for nonlinear multi-agent systems

Data-driven-based fully distributed event-triggered control for nonlinear multi-agent systems

On the necessity and sufficiency of the small phase theorem for nonlinear fading memory systems

On the necessity and sufficiency of the small phase theorem for nonlinear fading memory systems

Observer-Based Double Event-Triggered Fast Finite-Time Consensus Control for Heterogeneous Nonlinear Multi-Agent Systems

Observer-Based Double Event-Triggered Fast Finite-Time Consensus Control for Heterogeneous Nonlinear Multi-Agent Systems

Adaptive observer-based bipartite tracking consensus for nonlinear multi-agent systems with unknown sensor failures.

Adaptive observer-based bipartite tracking consensus for nonlinear multi-agent systems with unknown sensor failures.

Distributed Practical Fixed-Time Resource Allocation Algorithm for Disturbed Multiagent Systems: An Integrated Framework.

The practical fixed-time resource allocation problem is investigated for multi-input-multi-output nonlinear uncertain multiagent systems with disturbed dynamics, subject to global equality and local inequality constraints. Due to the coexistence of distributed high-order dynamics system within agents and decision-making constraints, decision variables in resource allocation optimization problems cannot be directly obtained from the system. Existing strategies are insufficient to solve such complex fixed-time optimization control problems with coupled decision-making constraints. To address these challenges, a novel integrated framework is proposed, fusing symbolic-function-based fixed-time control theory with gradient consistency. The proposed algorithm is implemented through an output-feedback backstepping design process, which involves two stages. First, in the output-feedback design stage, a fixed-time high-order extended state observer estimates the uncertain dynamics and disturbances. Second, in the backstepping design stage, a time-switching controller is developed. This controller's virtual control law has two components: the first employs the proportional-integral control method to satisfy the equality constraints, while the second uses gradient information from the $\epsilon $ -exact penalty function to address the inequality constraints. Using the Lyapunov stability criterion, the proposed algorithm can ensure that all signals remain practical fixed-time stable, and that the error between the outputs of all agents and the optimal solution is maintained within a neighborhood of the origin. Finally, simulations are presented to demonstrate the effectiveness of the approach.

Nonlinear optomechanical systems with quasi-periodic and chaotic dynamics

Nonlinear optomechanical systems with quasi-periodic and chaotic dynamics

Distributed Optimal Consensus Problem of Input Constrained Nonlinear Discrete-Time MASs: A Mode-Free Reinforcement Learning Approach.

In this article, a model-free reinforcement learning (RL) approach is proposed for solving the optimal consensus control issue of nonlinear discrete-time multiagent systems with input constraint. To address the challenge of solving the coupled discrete Hamilton-Jacobi-Bellman (HJB) equation, a RL approach based on actor-critic framework is proposed for optimal consensus control. A well-defined cost function is designed, and the actor and critic networks are updated through online learning to obtain the optimal controllers. Furthermore, the actuator's performance is often limited due to physical constraints. To address such actuator constraints, a gradual transition control (GTC) method is proposed, and update-free and update-weak policies are introduced to further optimize network performance. Additionally, in real-world distributed systems, the actor-critic networks deployed in each agent rely on data from neighboring agents, which necessitates addressing the issue of distributed synchronization. To address this challenge, the synchronization blocking method is designed, which designs additional control signals for each agent to handle these issues. Finally, two simulations under different scenarios are presented to verify the effectiveness of the proposed approach.

A study on a novel multi-body floating-point absorber with a nonlinear power take-off system and its hydrodynamic performance

A study on a novel multi-body floating-point absorber with a nonlinear power take-off system and its hydrodynamic performance

Exact expression for the propagating front velocity in nonlinear discrete systems under nonreciprocal coupling

Exact expression for the propagating front velocity in nonlinear discrete systems under nonreciprocal coupling

Synchronization of chaotic and hyperchaotic nonlinear dynamical systems and their numerous applications: A review

Synchronization of chaotic and hyperchaotic nonlinear dynamical systems and their numerous applications: A review

Bilateral Cooperative Control of Nonlinear Multiagent Systems With State and Output Quantification.

The fuzzy adaptive state and output quantization bilateral cooperative control problem for nonlinear multiagent systems (NMASs) is studied. Since the considered system is nonlinear, fuzzy logic system (FLS) is applied to approximate the unknown nonlinear function, and a fuzzy state observer is constructed because the state cannot be measured. A second-order command filter is used to solve the complex problem of calculating the time derivative of the virtual control function, and a uniform quantizer is used for fuzzy adaptive inversion design in the process of controller design. Ultimately, the effectiveness of the proposed control method is verified by a series of simulation experiments and research results.

Optimal transport-based displacement interpolation with data augmentation for reduced order modeling of nonlinear dynamical systems

Optimal transport-based displacement interpolation with data augmentation for reduced order modeling of nonlinear dynamical systems