## What is/are Mimo Nonlinear?

Mimo Nonlinear - Originality/valueThe uncertain MIMO nonlinear system described by Type-2 Takagi-Sugeno (T-S) fuzzy model, and successively LMI approach used to determine the system stability conditions.^{[1]}This paper first gives a review on recent advance in semiglobal asymptotic stabilization (SGAS) of MIMO nonlinear systems by sampled-data feedback and discusses their limitations.

^{[2]}It is shown that this control problem can be converted into a global robust stabilization problem of a more complicated MIMO nonlinear system with various uncertainties and well solved by a recursive state feedback controller design.

^{[3]}In this study, we propose an adaptive tracking dynamic surface back-stepping control based on Nussbaum disturbance observer for uncertain high-order strict-feedback MIMO nonlinear systems with external disturbances, unknown parameters and modelling uncertainties.

^{[4]}A general stability of the closed-loop disturbed MIMO nonlinear system is achieved by the Lyapunov theorem.

^{[5]}This article proposed a new fixed-time output tracking control scheme for a class of high-order MIMO nonlinear systems with unknown nonlinearities, parameter uncertainties and external disturbances.

^{[6]}The proposed controller (GRNNSMC) performance is verified with a generic MIMO nonlinear dynamic system and a hexacopter model with a variable center of gravity.

^{[7]}Also, the nonlinear functions in the MIMO nonlinear systems are not required to follow the linearly parameterization or growth conditions making the control design more generally available.

^{[8]}This paper investigates the time-varying output constraints tracking problem for a class of MIMO nonlinear system, where a new design of Iterative Learning Control (ILC) with adaptive sliding mode method is implemented.

^{[9]}This paper adduces the idea of linearization based on piecewise polynomial or essential spline functions as an integral solution to the MIMO nonlinearity and compares with the Crossover Memory polynomial model.

^{[10]}The capabilities of the toolbox and the modelling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.

^{[11]}Acquired results illustrate thatintroduced controller has substantially good performance on MIMO nonlinear systems.

^{[12]}A uniform quantizer is adopted to quantize state variables and control inputs of MIMO nonlinear systems.

^{[13]}The flexibility of the division method to convert k-input MIMO system to SISOs system combined with the optimal algorithm creates a powerful tool that can be applied to many different MIMO nonlinear systems with high success rates.

^{[14]}The proposed SO-DFL-BELC is applied to control two different MIMO nonlinear systems that are a 4D chaotic system and a four-tank system.

^{[15]}In this paper, we propose a low-cost and effective neuroadaptive PI control for MIMO nonlinear systems with actuation failures as well as unknown control direction.

^{[16]}The MIMO nonlinear systems are approximated by MIMO ARX-Laguerre multiple models.

^{[17]}An event-triggered modular neural network controller is designed for containment maneuvering of second-order MIMO nonlinear multi-agent systems under an undirected graph.

^{[18]}We show that under the proposed novel control scheme, each element in the system output tracking error vector of the MIMO nonlinear system can converge into small sets near zero with fixed-time convergence rate, while the asymmetric output constraint requirements on each element of the output tracking error are satisfied at all time.

^{[19]}The design of the MRAC requires the linear state–space representation of the MIMO nonlinear model of the robot arm.

^{[20]}A new problem of observer-based fractional adaptive type-2 fuzzy backstepping control for a class of fractional-order MIMO nonlinear dynamic systems with dead-zone input nonlinearity is considered in the presence of model uncertainties and external disturbances where the control scheme is constructed by combining the backstepping dynamic surface control (DSC) and fractional adaptive type-2 fuzzy technique.

^{[21]}The design of the MRAC requires the linear state–space representation of the MIMO nonlinear model of the robot arm.

^{[22]}In addition, the uncertainty rejection capability of the control system is achieved by exploiting the MIMO nonlinear extended disturbance observer.

^{[23]}Experimental results demonstrate that the MIMO nonlinear predictive controller performs better than the MIMO PID controller because employing the former controller, the settling time, the percent overshoot, as well as the steady–state error of the controlled outputs decrease.

^{[24]}After compared with other models, the test results show that the proposed model can be applied to obtain more satisfactory control performance and be more suitable to deal with the influence of the uncertainty of the MIMO nonlinear systems.

^{[25]}Based on the backstepping design technique and

^{[26]}A MIMO nonlinear mathematical model is derived for the 2DOF helicopter based on Euler-Lagrange equations, where the system parameters and the control coefficients are uncertain.

^{[27]}However, the MIMO nonlinear loops in the PTCS turn the selection of the sampling period TS for a digital implementation, while factoring the time delays introduced by the communication, into a non-trivial task.

^{[28]}Another advantage of the proposed controller and unlike other works on ILC, we do not need any prior knowledge of the control directions for MIMO nonlinear system.

^{[29]}Then, the following discrete-time MIMO nonlinear system is given by.

^{[30]}Therefore, this paper proposes an adaptive passive fault tolerant control method for actuator faults of affine class of MIMO nonlinear systems with uncertainties using sliding mode control.

^{[31]}A robust direct adaptive fuzzy controller for a class of MIMO nonlinear systems with uncertainties and external disturbances is presented.

^{[32]}In this paper, based on adaptive non-backstepping design algorithm, we proposed a novel variable universe fuzzy control (VUFC) algorithm for a class of MIMO nonlinear systems with unknown dead-zone inputs in pure-feedback form.

^{[33]}Experimental results demonstrate that the MIMO nonlinear predictive controller performs better than the MIMO PID controller because employing the former controller, the settling time, the percent overshoot, as well as the steady–state error of the controlled outputs decrease.

^{[34]}For synchronous generator within multi-machine power system described by MIMO nonlinear Differential Algebraic Equations (DAE) system model, the robust control problem is considered in this paper.

^{[35]}This paper presents a sequential learning machine based on the Takagi-Sugeno (TS) fuzzy inference system to model the dynamics of a MIMO nonlinear quadcopter using experimental data.

^{[36]}This paper investigates the characteristic modeling problem of a class of high-order MIMO nonlinear dynamical systems with zero dynamics and subjected to strong nonlinearities.

^{[37]}

## Order Mimo Nonlinear

This article proposed a new fixed-time output tracking control scheme for a class of high-order MIMO nonlinear systems with unknown nonlinearities, parameter uncertainties and external disturbances.^{[1]}An event-triggered modular neural network controller is designed for containment maneuvering of second-order MIMO nonlinear multi-agent systems under an undirected graph.

^{[2]}A new problem of observer-based fractional adaptive type-2 fuzzy backstepping control for a class of fractional-order MIMO nonlinear dynamic systems with dead-zone input nonlinearity is considered in the presence of model uncertainties and external disturbances where the control scheme is constructed by combining the backstepping dynamic surface control (DSC) and fractional adaptive type-2 fuzzy technique.

^{[3]}This paper investigates the characteristic modeling problem of a class of high-order MIMO nonlinear dynamical systems with zero dynamics and subjected to strong nonlinearities.

^{[4]}

## Different Mimo Nonlinear

The flexibility of the division method to convert k-input MIMO system to SISOs system combined with the optimal algorithm creates a powerful tool that can be applied to many different MIMO nonlinear systems with high success rates.^{[1]}The proposed SO-DFL-BELC is applied to control two different MIMO nonlinear systems that are a 4D chaotic system and a four-tank system.

^{[2]}

## mimo nonlinear system

Originality/valueThe uncertain MIMO nonlinear system described by Type-2 Takagi-Sugeno (T-S) fuzzy model, and successively LMI approach used to determine the system stability conditions.^{[1]}This paper first gives a review on recent advance in semiglobal asymptotic stabilization (SGAS) of MIMO nonlinear systems by sampled-data feedback and discusses their limitations.

^{[2]}It is shown that this control problem can be converted into a global robust stabilization problem of a more complicated MIMO nonlinear system with various uncertainties and well solved by a recursive state feedback controller design.

^{[3]}In this study, we propose an adaptive tracking dynamic surface back-stepping control based on Nussbaum disturbance observer for uncertain high-order strict-feedback MIMO nonlinear systems with external disturbances, unknown parameters and modelling uncertainties.

^{[4]}A general stability of the closed-loop disturbed MIMO nonlinear system is achieved by the Lyapunov theorem.

^{[5]}This article proposed a new fixed-time output tracking control scheme for a class of high-order MIMO nonlinear systems with unknown nonlinearities, parameter uncertainties and external disturbances.

^{[6]}Also, the nonlinear functions in the MIMO nonlinear systems are not required to follow the linearly parameterization or growth conditions making the control design more generally available.

^{[7]}This paper investigates the time-varying output constraints tracking problem for a class of MIMO nonlinear system, where a new design of Iterative Learning Control (ILC) with adaptive sliding mode method is implemented.

^{[8]}Acquired results illustrate thatintroduced controller has substantially good performance on MIMO nonlinear systems.

^{[9]}A uniform quantizer is adopted to quantize state variables and control inputs of MIMO nonlinear systems.

^{[10]}The flexibility of the division method to convert k-input MIMO system to SISOs system combined with the optimal algorithm creates a powerful tool that can be applied to many different MIMO nonlinear systems with high success rates.

^{[11]}The proposed SO-DFL-BELC is applied to control two different MIMO nonlinear systems that are a 4D chaotic system and a four-tank system.

^{[12]}In this paper, we propose a low-cost and effective neuroadaptive PI control for MIMO nonlinear systems with actuation failures as well as unknown control direction.

^{[13]}The MIMO nonlinear systems are approximated by MIMO ARX-Laguerre multiple models.

^{[14]}We show that under the proposed novel control scheme, each element in the system output tracking error vector of the MIMO nonlinear system can converge into small sets near zero with fixed-time convergence rate, while the asymmetric output constraint requirements on each element of the output tracking error are satisfied at all time.

^{[15]}After compared with other models, the test results show that the proposed model can be applied to obtain more satisfactory control performance and be more suitable to deal with the influence of the uncertainty of the MIMO nonlinear systems.

^{[16]}Another advantage of the proposed controller and unlike other works on ILC, we do not need any prior knowledge of the control directions for MIMO nonlinear system.

^{[17]}Then, the following discrete-time MIMO nonlinear system is given by.

^{[18]}Therefore, this paper proposes an adaptive passive fault tolerant control method for actuator faults of affine class of MIMO nonlinear systems with uncertainties using sliding mode control.

^{[19]}A robust direct adaptive fuzzy controller for a class of MIMO nonlinear systems with uncertainties and external disturbances is presented.

^{[20]}In this paper, based on adaptive non-backstepping design algorithm, we proposed a novel variable universe fuzzy control (VUFC) algorithm for a class of MIMO nonlinear systems with unknown dead-zone inputs in pure-feedback form.

^{[21]}

## mimo nonlinear model

The design of the MRAC requires the linear state–space representation of the MIMO nonlinear model of the robot arm.^{[1]}The design of the MRAC requires the linear state–space representation of the MIMO nonlinear model of the robot arm.

^{[2]}

## mimo nonlinear dynamic

The proposed controller (GRNNSMC) performance is verified with a generic MIMO nonlinear dynamic system and a hexacopter model with a variable center of gravity.^{[1]}A new problem of observer-based fractional adaptive type-2 fuzzy backstepping control for a class of fractional-order MIMO nonlinear dynamic systems with dead-zone input nonlinearity is considered in the presence of model uncertainties and external disturbances where the control scheme is constructed by combining the backstepping dynamic surface control (DSC) and fractional adaptive type-2 fuzzy technique.

^{[2]}

## mimo nonlinear predictive

Experimental results demonstrate that the MIMO nonlinear predictive controller performs better than the MIMO PID controller because employing the former controller, the settling time, the percent overshoot, as well as the steady–state error of the controlled outputs decrease.^{[1]}Experimental results demonstrate that the MIMO nonlinear predictive controller performs better than the MIMO PID controller because employing the former controller, the settling time, the percent overshoot, as well as the steady–state error of the controlled outputs decrease.

^{[2]}

## mimo nonlinear dynamical

The capabilities of the toolbox and the modelling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.^{[1]}This paper investigates the characteristic modeling problem of a class of high-order MIMO nonlinear dynamical systems with zero dynamics and subjected to strong nonlinearities.

^{[2]}