Direct Adaptive Neural Control Scheme Research Articles

Neuroadaptive asymptotic consensus tracking control for a class of uncertain nonlinear multiagent systems with sensor faults

This paper proposes an adaptive neural consensus tracking control approach for a class of leader–follower uncertain multiagent systems with sensor faults. Based on backstepping technique, a new direct adaptive neural control scheme is proposed to adaptively approximate the sensor faults. In order to improve the stability of the system and transient performance, a series of smooth functions are incorporated into control design and Lyapunov analysis. In addition, a class of reduced-order smooth functions is introduced to achieve a simpler virtual controller implementation. It is proved that the closed-loop signals are bounded and the synchronization errors can converge to a preset interval. Besides the asymptotic performance, a tunable L2-norm transient performance is achieved. Finally, numerical and physical example are presented to validate the effectiveness of the proposed control scheme.

A direct adaptive neural control for maximum power point tracking of photovoltaic system

This paper represents a novel direct adaptive neural control (DANC) method for maximum power point tracking (MPPT) of photovoltaic (PV) systems. A DC/DC buck converter to regulate the output power of the photovoltaic system is considered. The direct adaptive neural control scheme operates on MPP and improves the performance of solar energy conversion efficiency. The online adaptation procedure is based on learning law of the δ rule and only the system output error is required. The prime contributions of this study are a simple and effective solution for MPPT, the fast learning and the straightforward digital implementation. The performance of direct adaptive neural controller on photovoltaic sources with different characteristics is assessed. The simulation results confirm the feasibility and effectiveness of the DANC. The proposed MPPT control system is compared to the conventional perturbation and observation (P&O) method.

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.

Multilayer neural networks-based direct adaptive control for switched nonlinear systems

This paper is concerned to present a direct adaptive neural control scheme for switched nonlinear systems with unknown constant control gain. Multilayer neural networks (MNNs) are used as a tool for modeling nonlinear functions up to a small error tolerance. The adaptive updated laws have been derived from the switched multiple Lyapunov function method, also an admissible switching signal with average dwell-time technique is given. It is proved that the resulting closed-loop system is asymptotically Lyapunov stable such that the output tracking error performance is well obtained. Finally, a simulation example of two Duffing forced-oscillation systems is given to illustrate the effectiveness of the proposed control scheme.

Direct adaptive neural control for affine nonlinear systems

This paper presents a direct adaptive neural control scheme for a class of affine nonlinear systems which are exactly input–output linearizable by nonlinear state feedback. For single-input–single-output (SISO) systems of the form x ˙ = f ( x ) + g ( x ) u , the control problem is comprehensively solved when both f ( x ) and g ( x ) are unknown. In this case, the control input comprises two terms. One is an adaptive feedback linearization term and the other one is a sliding mode term. The weight update laws for two neural networks, which approximate f ( x ) and g ( x ) , have been derived to make the closed loop system Lyapunov stable. It is also shown that a similar control approach can be applied for a class of multi-input–multi-output (MIMO) systems whose structure is formulated in this paper. Simulation results for both SISO- and MIMO-type nonlinear systems have been presented to validate the theoretical formulations.

A direct adaptive neural control scheme with integral terms

A direct adaptive neural control scheme with single and double integral-plus-state (IPS) actions is proposed. The control scheme contains two recurrent trainable neural network (RTNN) models, which are a plant parameter identifier and state estimator, an IPS feedback/feedforward controller, and one or two I-terms. The good performance of the adaptive IPS control scheme is confirmed by closed-loop systems analysis and by simulation results obtained with a MIMO plant, corrupted by noise. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 213–224, 2005.