RBFNN Variable Structure Controller for MIMO System and Application to Ship Rudder/Fin Joint Control
Han Yao Zhen,Hairong Xiao +1 more
TL;DR: Aiming at a class of multiple-input multiple-output (MIMO) system with uncertainty, a sliding mode control algorithm based on neural network disturbance observer is designed and applied to ship yaw and roll joint stabilization as mentioned in this paper.
read more
Abstract: Aiming at a class of multiple-input multiple-output (MIMO) system with uncertainty, a sliding mode control algorithm based on neural network disturbance observer is designed and applied to ship yaw and roll joint stabilization The nonlinear disturbance observer is finished by radial basis function neural network and with that a terminal sliding mode control algorithm is proposed The asymptotic stability of closed-loop system is proved based on Lyapunov theorem The proposed control law is applied to anti-roll control under simulative wave disturbances Simulation results verified robustness and effectiveness of the suggested algorithm A good anti-rolling effect is achieved and yaw angle is also reduced greatly with less mechanical loss
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Reducing Computational Complexity and Enhancing Performance of IKSD Algorithm for Encoded MIMO Systems
TL;DR: This paper has proposed an enhancing IKSD algorithm by adding the combining of column norm ordering (channel ordering) with Manhattan metric to enhance the performance and reduce the computational complexity.
A New Approach of Detection Algorithm for Reducing Computation Complexity of MIMO systems
TL;DR: Frequent Improve K-best Sphere Decoding (FIKSD) algorithm with stopping rule depending on the Manhattan metric is proposed to use with FIKSD in order to achieve the lowest complexity.
3
An Optimization Model on Virtual Machines Allocation Based on Radial Basis Function Neural Networks
TL;DR: A systemic method on virtual machine array optimization control based on artificial intelligence and matrix control theory to achieve low consumption optimization and ensure the stability of the system is presented.
3
•Journal Article
Adaptive fuzzy sliding mode control based on terminal attractors for multi-input multi-output nonlinear systems
TL;DR: The proposed controller utilizes an adaptive fuzzy learning rule with terminal attractors having property of rapidity and stability to replace the traditional adaptive fuzzyLearning rule, which guarantees the stability and fast con- vergence of control systems, so the output of nonlinear systems can trace the given input reference signal in finite time.
1
References
Derivative and integral terminal sliding mode control for a class of MIMO nonlinear systems
TL;DR: Different from traditional TSMC, this paper accomplishes finite convergence time for more general high-order MIMO systems and avoids the singular problem in the controller design.
298
The application of the self-tuning neural network PID controller on the ship roll reduction in random waves
TL;DR: A mathematical model including seakeeping and maneuvering characteristics to analyze the roll reduction for a ship traveling with the stabilizer fin in random waves and shows that the present developed self-tuning PID control scheme based on the neural network theory is indeed quite practical and sufficient for the ship roll reduction in the realistic sea.
127
Brief paper: Adaptive sliding mode controller design based on T-S fuzzy system models
TL;DR: An adaptive sliding mode control (ASMC) technique based on T-S fuzzy system models is proposed in this paper for a class of perturbed nonlinear MIMO dynamic systems in order to solve tracking problems.
90
Output-feedback finite-time stabilization of disturbed feedback linearizable nonlinear systems
TL;DR: A novel methodology for designing multivariable High-Order Sliding-Mode (HOSM) controllers for disturbed feedback linearizable nonlinear systems is introduced, which provides for the finite-time stabilization of the origin of the state-space by using output feedback.
41