Mathematical Preliminaries.- Stabilizing Switching for Autonomous Systems.- Controllability, Observability, and Normal Forms.- Feedback Stabilization.- Optimization.- Conclusions Perspectives.

In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence unknown functions. The functions are handled via on-line network (NN) using only measurements. A barrier Lyapunov function (BLF) introduced to address two open and challenging problems neuro-control area: 1) any initial compact set, how determine priori superset, on which NN approximation valid; 2) ensure that arguments remain within specified superset. By ensuring boundedness...

In this paper, adaptive neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms. The MIMO consist interconnected subsystems, with couplings the forms unknown nonlinearities and/or parametric uncertainties input matrices, as well system interconnections without any bounding restrictions. Using structure properties, stability analyses closed-loop shown a nested iterative manner all states. By exploiting...

The paper first describes the problem of goals unreachable with obstacles nearby when using potential field methods for mobile robot path planning. Then, new repulsive functions are presented by taking relative distance between and goal into consideration, which ensures that position is global minimum total potential.

In this paper, robust adaptive neural network (NN) control is investigated for a general class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems with unknown coefficient matrices and input nonlinearities. For nonsymmetric nonlinearities saturation deadzone, variable structure (VSC) in combination backstepping Lyapunov synthesis proposed NN design guaranteed stability. the control, usual assumption on nonsingularity approximation boundary between error have been eliminated....

In this paper, adaptive neural control is presented for a class of strict-feedback nonlinear systems with unknown time delays. The proposed design method does not require priori knowledge the signs virtual coefficients. delays are compensated using appropriate Lyapunov-Krasovskii functionals in design. It proved that backstepping able to guarantee semiglobal uniformly ultimately boundedness all signals closed-loop. addition, output system proven converge small neighborhood origin. Simulation...

In this paper, direct adaptive neural-network (NN) control is presented for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. By utilizing special property term, developed scheme,avoids controller singularity problem completely. All signals closed loop are guaranteed to be semiglobally uniformly ultimately bounded and output system proven converge small neighborhood desired trajectory. The performance closed-loop by suitably choosing design...

Following the idea of using first-order time derivatives, this paper presents a general recurrent neural network (RNN) model for online inversion time-varying matrices. Different kinds activation functions are investigated to guarantee global exponential convergence exact inverse given matrix. The robustness proposed is also studied with respect different and various implementation errors. Simulation results, including application kinematic control redundant manipulators, substantiate...

Presents a robust adaptive control approach for class of time-varying uncertain nonlinear systems in the strict feedback form with completely unknown virtual coefficients, parameters and bounded disturbances. The proposed design method does not require any priori knowledge coefficients except their bounds. It is proved that scheme can guarantee global uniform ultimate boundedness closed-loop system signals disturbance attenuation.