Neural Control Of Nonlinear Systems Research Articles

A Simplified Fuzzy Wavelet Neural Control for Nonlinear Systems With Quantized Inputs and Deferred Constraints

A Simplified Fuzzy Wavelet Neural Control for Nonlinear Systems With Quantized Inputs and Deferred Constraints

Improved finite-time prescribed performance based adaptive neural control for nonlinear systems with sensor faults

In this article, based on a novel finite-time prescribed performance function, we consider the issue of the adaptive neural fault-tolerant tracking control problem for a type of nonlinear system with sensor faults. The control performance of the system may be affected when sensor faults occur. For the controlled system with sensor faults, an improved finite-time performance function is proposed so that its initial conditions do not need to be set in advance. Compared with existing performance functions, the performance functions in this paper can ensure that the system is always controllable, even if the sensor faults occur suddenly during the steady operation. Moreover, Radial basis function (RBF) neural networks (NNs) are employed to estimate the uncertain smooth nonlinear functions. With the help of the adaptive backstepping control technique, an improved adaptive prescribed performance control technique is developed, which can realize the boundedness of every signal in the closed-loop system, and the tracking error can be limited within the neighborhood near the origin. Simulation results demonstrate the effectiveness of the proposed control scheme.

Command‐filter‐based fast finite‐time composite adaptive neural control for nonlinear systems with input dead‐zone

SummaryIn this paper, a command‐filter‐based fast finite‐time composite adaptive control scheme is proposed for nonlinear systems with input dead‐zone. The neural networks (NNs) are used to approximate the unknown functions of the controlled systems. The series‐parallel nonsmooth estimation models are used to predict the errors. In addition, the tracking error and prediction errors are anastomosed to update the weights of the NNs. To solve the problem of “explosion of complexity,” the command‐filter technology is adopted. Combining the Lyapunov stability theory and the adaptive backstepping algorithm, a composite adaptive backstepping control scheme is proposed. The proposed scheme dissolves the singularity problem which may emerge in the design process and the neural networks approximation performances can be realized as well. Meanwhile, the proposed scheme guarantees the tracking error converges into a small neighborhood of zero in fast finite‐time and the boundedness of all the closed‐loop signals. Finally, the simulation example is provided to verify the effectiveness of the proposed scheme.

A New Indirect Adaptive Neural Control for Nonlinear Systems: A Real Validation on a Chemical Process

In the present work, an indirect adaptive neural control method for nonlinear systems having unknown dynamics is proposed. The proposed control architecture is composed by a neural emulator (NE) and a neural controller (NC) where a new decoupled variable learning rates (VLRs) combined with Taylor development (TD) are used to train the NE and the NC. The developed VLRs mixed with the TD (TDVLRs) ensure a quick adaptation of neural networks parameters guaranteeing a faster output convergence and reducing the tracking error. The effectiveness of the proposed TDVLRs is illustrated by simulation with a nonlinear dynamic system. In order to validate simulation results, an application on a transesterification reactors is, also, presented.

Adaptive neural control for nonlinear systems with sensor fault and input nonlinearities

Adaptive neural control for nonlinear systems with sensor fault and input nonlinearities

Event-Based Adaptive Neural Control of Nonlinear Systems With Deferred Constraint

In this article, the problem of constant yet deferred output constraint for uncertain strict-feedback nonlinear systems is studied. By “deferred output constraint,” we mean that the system output is free/released from any constraint in the initial interval and then preserves within a bounded region right after a finite time. Due to such a form of output constraint, the normally employed Barrier Lyapunov Function (BLF)-based results become invalid because the corresponding BLF is undefined in the initial period. The problem will be rather complicated yet challenging if computation and communication constraints are taken into account. By developing an error-based nonlinear function and constructing a prescribed-time scaling function, together with the approximate ability of neural networks, a varying threshold-based event-triggering adaptive neural control algorithm is presented such that not only the deferred output constraint can be ensured and the network resources can be saved but also the tracking error is able to converge to a pregiven region in a prescribed time. Simulations are provided to demonstrate the effectiveness of the proposed control.

Adaptive neural control for nonlinear systems with actuator faults and unknown control directions via command filter

AbstractThis article studies the issue of command filter‐based adaptive fault‐tolerant control for a class of nonlinear systems subject to unknown control directions and disturbance. First, the neural network is employed to deal with the nonlinear functions, and the explosion of the complexity problem is handled by the command filter approach. Second, the bound estimation method and the Nussbaum function are utilized to compensate for the influence of the actuator faults and the unknown directions, respectively. Finally, the tracking error signals are guaranteed to converge into bounded compact sets around the origin, and all closed‐loop signals are bounded. The effectiveness of the proposed method is illustrated by three simulations.

Finite‐time adaptive neural control for nonlinear systems under state‐dependent sensor attacks

AbstractThis article proposes a finite‐time control scheme for a class of uncertain nonlinear systems in the presence of sensor attacks. Specifically, based on the backstepping technology and the nonlinear function approximation capability of radial basis function neural networks, we develop an adaptive controller, guaranteeing the finite‐time stability of the closed‐loop system despite sensor attacks. In particular, the unknown time‐varying state‐feedback gains caused by sensor attacks are handled by Nussbaum functions. To decrease the design difficulty of the finite‐time controller, a novel practical finite‐time stability criterion is given. Finally, two simulation examples are provided, demonstrating the effectiveness of the proposed adaptive control scheme.

Time-varying asymmetrical BLFs based adaptive finite-time neural control of nonlinear systems with full state constraints

This paper concentrates on asymmetric barrier Lyapunov functions &#x0028 ABLFs &#x0029 based on finite-time adaptive neural network &#x0028 NN &#x0029 control methods for a class of nonlinear strict feedback systems with time-varying full state constraints. During the process of backstepping recursion, the approximation properties of NNs are exploited to address the problem of unknown internal dynamics. The ABLFs are constructed to make sure that the time-varying asymmetrical full state constraints are always satisfied. According to the Lyapunov stability and finite-time stability theory, it is proven that all the signals in the closed-loop systems are uniformly ultimately bounded &#x0028 UUB &#x0029 and the system output is driven to track the desired signal as quickly as possible near the origin. In the meantime, in the scope of finite-time, all states are guaranteed to stay in the pre-given range. Finally, a simulation example is proposed to verify the feasibility of the developed finite time control algorithm.

Event-Triggered Neural Control of Nonlinear Systems With Rate-Dependent Hysteresis Input Based on a New Filter.

In controlling nonlinear uncertain systems, compensating for rate-dependent hysteresis nonlinearity is an important, yet challenging problem in adaptive control. In fact, it can be illustrated through simulation examples that instability is observed when existing control methods in canceling hysteresis nonlinearities are applied to the networked control systems (NCSs). One control difficulty that obstructs these methods is the design conflict between the quantized networked control signal and the rate-dependent hysteresis characteristics. So far, there is still no solution to this problem. In this paper, we consider the event-triggered control for NCSs subject to actuator rate-dependent hysteresis and failures. A new second-order filter is proposed to overcome the design conflict and used for control design. With the incorporation of the filter, a novel adaptive control strategy is developed from a neural network technique and a modified backstepping recursive design. It is proved that all the control signals are semiglobally uniformly ultimately bounded and the tracking error will converge to a tunable residual around zero.

Barrier Lyapunov functions-based localized adaptive neural control for nonlinear systems with state and asymmetric control constraints

A barrier Lyapunov functions (BLFs)-based localized adaptive neural network (NN) control is proposed for a class of uncertain nonlinear systems with state and asymmetric control constraints to track the reference trajectory. To handle system constraints, BLFs are used in the backstepping procedure, and the control input is considered as an extended state variable. This extends current research on BLFs-based control for systems with state and output constraints to systems with state and asymmetric control constraints. A locally weighted learning NN with projection modification is designed to estimate and compensate for the system uncertainty. The use of projection modification ensures the NN estimator is contained in a given bounded area and prevents the absolute value of NN output from being near to or larger than the bound of the tracking error. The feasibility and effectiveness of the proposed control have been demonstrated by formal proof and simulation results.

Adaptive Neural Control of Nonlinear Systems With Unknown Control Directions and Input Dead-Zone

This paper presents an adaptive neural control approach for nonstrict-feedback nonlinear systems in presence of unmodeled dynamics, unknown control directions and input dead-zone nonlinearity. To handle the difficulty due to uncertain control directions, Nussbaum gain functions are applied. Based on the structural characteristic of radial basis function neural networks, a backstepping-based adaptive neural control algorithm is developed. The main contributions of this paper lie in the fact that a backstepping-based neural control algorithm is developed for nonstrict-feedback nonlinear systems with unmodeled dynamics, unknown control directions and actuator dead-zone, and the total number of adaptive laws is not greater than the order of control system. As a beneficial result, the controller is much easier to be implemented in practice with less computational burden. A simulation example is given to reveal the viability of the presented approach. It is demonstrated by both theoretical analysis and simulation study that the presented control strategy ensures the semiglobally uniform ultimate boundedness of all closed-loop system signals.

Direct adaptive neural control of nonlinear systems with extreme learning machine

A direct adaptive neural control scheme for a class of nonlinear systems is presented in the paper. The proposed control scheme incorporates a neural controller and a sliding mode controller. The neural controller is constructed based on the approximation capability of the single-hidden layer feedforward network (SLFN). The sliding mode controller is built to compensate for the modeling error of SLFN and system uncertainties. In the designed neural controller, its hidden node parameters are modified using the recently proposed neural algorithm named extreme learning machine (ELM), where they are assigned random values. However, different from the original ELM algorithm, the output weight is updated based on the Lyapunov synthesis approach to guarantee the stability of the overall control system. The proposed adaptive neural controller is finally applied to control the inverted pendulum system with two different reference trajectories. The simulation results demonstrate good tracking performance of the proposed control scheme.

Decentralized adaptive neural control of nonlinear systems with unknown time delays

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear time-delay systems. RBF neural networks (NNs) are used to tackle unknown nonlinear functions, then the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The proposed controller guarantees semi-global uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop large-scale system, while the tracking errors converge to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, and three problems of “computational explosion,” “dimension curse” and “controller singularity” are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.

Learning from neural control of nonlinear systems in normal form

A deterministic learning theory was recently proposed which states that an appropriately designed adaptive neural controller can learn the system internal dynamics while attempting to control a class of simple nonlinear systems. In this paper, we investigate deterministic learning from adaptive neural control (ANC) of a class of nonlinear systems in normal form with unknown affine terms. The existence of the unknown affine terms makes it difficult to achieve learning by using previous methods. To overcome the difficulties, firstly, an extension of a recent result is presented on stability analysis of linear time-varying (LTV) systems. Then, with a state transformation, the closed-loop control system is transformed into a LTV form for which exponential stability can be guaranteed when a partial persistent excitation (PE) condition is satisfied. Accurate approximation of the closed-loop control system dynamics is achieved in a local region along a recurrent orbit of closed-loop signals. Consequently, learning of control system dynamics (i.e. closed-loop identification) from adaptive neural control of nonlinear systems with unknown affine terms is implemented.

Practical Adaptive Neural Control of Nonlinear Systems With Unknown Time Delays

Practical adaptive neural control is presented for a class of nonlinear systems with unknown time delays in strict-feedback form. Using appropriate Lyapunov-Krasovskii functionals, the unknown time delays are compensated for. Controller singularity problems are solved by practical neural network control. A novel differentiable control function is provided such that the practical design can be carried out in the decoupled backstepping design. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all the signals in the closed-loop system, and the tracking error is proven to converge to a small neighborhood of the origin.

Application of neural dynamic optimization to combustion-instability control

The suppression of thermoacoustic combustion instabilities represents one of the main goals in the design of reliable high-performances combustion chambers. Unstable dynamics arise when a non-linear coupling is established between the acoustic field and the flame front generating high-amplitude and low-frequency pressure and heat release oscillations, associated with the excitation of the combustor’s natural modes. Temperature and pressure peaks due to these phenomena are particularly harmful for the structural damage they can cause as well as for performance degradations and increase of pollutant emissions. Due to the non-linear nature of the phenomenon, relevant problems arise when it is necessary to define model-based control-systems. The aim of this study is to define a control strategy, based on the application of recent results in the field of neural control of non-linear systems. The proposed strategy is an application of an innovative neural-network-based technique, namely Neural Dynamic Optimization, which is able to exploit the potential of optimal control strategies in dealing with complex non-linear systems and the flexibility and the generalisation properties of neural networks. Reported simulations show the satisfactory performance of the proposed controller in suppressing undesired thermoacoustic combustion instabilities.

Dissipative-based adaptive neural control for nonlinear systems

A dissipative-based adaptive neural control scheme was developed for a class of nonlinear uncertain systems with unknown nonlinearities that might not be linearly parameterized. The major advantage of the present work was to relax the requirement of matching condition, i.e., the unknown nonlinearities appear on the same equation as the control input in a state-space representation, which was required in most of the available neural network controllers. By synthesizing a state-feedback neural controller to make the closed-loop system dissipative with respect to a quadratic supply rate, the developed control scheme guarantees that the L2-gain of controlled system was less than or equal to a prescribed level. And then, it is shown that the output tracking error is uniformly ultimate bounded. The design scheme is illustrated using a numerical simulation.

Neural controllers for nonlinear state feedback L>2-gain control

Design of an L/sub 2/-gain disturbance rejection neural controller for nonlinear systems is presented. The control input is generated from a radial basis network, which is trained offline such that a computed partial derivative of the network output satisfies a Hamilton-Jacobi inequality. Once the network is successfully trained for a given manifold in the state space, the closed-loop system ensures a finite gain between the system disturbance and the system input-output as long as the system states remain within the state manifold. The proposed method may also be applied to obtain an H/sub /spl infin// controller.