AIMC Topic: Nonlinear Dynamics

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Dynamic Learning From Adaptive Neural Control for Discrete-Time Strict-Feedback Systems.

IEEE transactions on neural networks and learning systems
This article first investigates the issue on dynamic learning from adaptive neural network (NN) control of discrete-time strict-feedback nonlinear systems. To verify the exponential convergence of estimated NN weights, an extended stability result is...

Adaptive Neural Network Control for Full-State Constrained Robotic Manipulator With Actuator Saturation and Time-Varying Delays.

IEEE transactions on neural networks and learning systems
This article proposes an adaptive neural network (NN) control method for an n -link constrained robotic manipulator. Driven by actual demands, manipulator and actuator dynamics, state and input constraints, and unknown time-varying delays are taken i...

Physics guided neural networks for modelling of non-linear dynamics.

Neural networks : the official journal of the International Neural Network Society
The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human intervention. However, it is diffi...

Adaptive Fuzzy Output-Feedback Control for Switched Uncertain Nonlinear Systems With Full-State Constraints.

IEEE transactions on cybernetics
This article investigates an adaptive fuzzy tracking control approach via output feedback for a class of switched uncertain nonlinear systems with full-state constraints under arbitrary switchings. The adaptive observer and controller are designed ba...

Adaptive Full-State-Constrained Control of Nonlinear Systems With Deferred Constraints Based on Nonbarrier Lyapunov Function Method.

IEEE transactions on cybernetics
In this article, the problem of tracking control is considered for a class of uncertain strict-feedback nonlinear systems with deferred asymmetric time-varying full-state constraints. A novel adaptive robust full-state-constrained control scheme is d...

Hardware-in-the-loop implementation of an unknown input observer for synchronous reluctance motor.

ISA transactions
In this paper, we design a proportional integral observe for a nonlinear synchronous reluctance motor described by a Takagi-Sugeno multi-model. In this design, both states and unknown inputs are estimated simultaneously. First, the mathematical nonli...

Novel optimal trajectory tracking for nonlinear affine systems with an advanced critic learning structure.

Neural networks : the official journal of the International Neural Network Society
In this paper, a critic learning structure based on the novel utility function is developed to solve the optimal tracking control problem with the discount factor of affine nonlinear systems. The utility function is defined as the quadratic form of t...

Reinforcement learning based adaptive optimal control for constrained nonlinear system via a novel state-dependent transformation.

ISA transactions
Existing schemes for state-constrained systems either impose feasibility conditions or ignore the optimality. In this article, an adaptive optimal control scheme for the strict-feedback nonlinear system is proposed, which benefits from two design ste...

Observer-Based Fixed-Time Neural Control for a Class of Nonlinear Systems.

IEEE transactions on neural networks and learning systems
This article is concerned with an issue of fixed time adaptive neural control for a class of uncertain nonlinear systems subject to hysteresis input and immeasurable states. The state observer and neural networks (NNs) are used to estimate the immeas...

Vibration Control of a Constrained Two-Link Flexible Robotic Manipulator With Fixed-Time Convergence.

IEEE transactions on cybernetics
With the more extensive application of flexible robots, the expectation for flexible manipulators is also increasing rapidly. However, the fast convergence will cause the increase of vibration amplitude to some extent, and it is difficult to obtain v...