Chaio-Shiung Chen

Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems

This paper proposes a novel dynamic structure neural fuzzy network (DSNFN) to address the adaptive tracking problems of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control scheme uses a four-layer neural fuzzy network (NFN) to estimate system uncertainties online. The main feature of this DSNFN is that it can either increase or decrease the number of fuzzy rules over time based on tracking errors. Projection-type adaptation laws for the network parameters are derived from the Lyapunov synthesis approach to ensure network convergence and stable control. A hybrid control scheme that combines the sliding-mode control and the adaptive bound estimation control with different weights improves system performance by suppressing the influence of external disturbances and approximation errors. As the employment of the DSNFN, high-quality tracking performance could be achieved in the system. Furthermore, the trained network avoids the problems of overfitting and underfitting. Simulations performed on a two-link robot manipulator demonstrate the effectiveness of the proposed control scheme.

Quadratic optimal neural fuzzy control for synchronization of uncertain chaotic systems

This paper presents a novel quadratic optimal neural fuzzy control for synchronization of uncertain chaotic systems via H^~ approach. In the proposed algorithm, a self-constructing neural fuzzy network (SCNFN) is developed with both structure and parameter learning phases, so that the number of fuzzy rules and network parameters can be adaptively determined. Based on the SCNFN, an uncertainty observer is first introduced to watch compound system uncertainties. Subsequently, an optimal NFN-based controller is designed to overcome the effects of unstructured uncertainty and approximation error by integrating the NFN identifier, linear optimal control and H^~ approach as a whole. The adaptive tuning laws of network parameters are derived in the sense of quadratic stability technique and Lyapunov synthesis approach to ensure the network convergence and H^~ synchronization performance. The merits of the proposed control scheme are not only that the conservative estimation of NFN approximation error bound is avoided but also that a suitable-sized neural structure is found to sufficiently approximate the system uncertainties. Simulation results are provided to verify the effectiveness and robustness of the proposed control method.

TSK-Type Self-Organizing Recurrent-Neural-Fuzzy Control of Linear Microstepping Motor Drives

In this paper, a Takagi-Sugeno-Kang-type self-organizing recurrent-neural-fuzzy network (T-SORNFN) is proposed for the trajectory tracking control of linear microstepping motor (LMSM) drives. Without a priori knowledge, the T-SORNFN is constructed to model the inverse dynamics of a LMSM drive by a set of recurrent fuzzy rules built online through concurrent structure and parameter learning. The fuzzy rules in the T-SORNFN can be either generated or eliminated to obtain a suitable-sized network structure, and a recursive recurrent learning laws of network parameters are derived based on the supervised gradient-descent method to achieve fast-learning converge. Based on the Lyapunov stability approach, the convergence of the T-SORNFN is guaranteed by choosing varied learning rates. Furthermore, an inverse-control architecture that incorporates T-SORNFN and a proportional-derivative controller is used to control the LMSM drive in a changing environment. A recursive least-squares (RLS) algorithm is utilized for online fine-tuning the consequent parameters in T-SORNFN to obtain a more precision model. Simulated and experimental results of a LMSM drive are provided to verify the effectiveness of the proposed T-SORNFN control system, and its superiority is validated in comparison with NFN and RNFN control systems.

Supervisory Interval Type-2 TSK Neural Fuzzy Network Control for Linear Microstepping Motor Drives With Uncertainty Observer

This paper proposes a supervisory interval type-2 Tagaki-Sugeno-Kang neural fuzzy network (IT2TSKNFN) control system for precision motion control of linear microstepping motor (LMSM) drives. The IT2TSKNFN incorporates interval type-2 fuzzy sets and TSK fuzzy reasoning into an NFN to handle uncertainties in LMSM drives, including payload variation, external disturbance, and sense noise. Based on the IT2TSKNFN, an uncertainty observer is first introduced to watch compound system uncertainties. Subsequently, an IT2TSKNFN-based controller is developed with robust hybrid control scheme, in which H∞ approach and a supervisory controller are embedded to overcome the effects of unstructured uncertainties and reconstruction errors. The supervisory controller combines variable structure control and adaptive IT2TSKNFN control with different weights based on the tracking error. Moreover, projection-type adaptive algorithms that can tune parameters of the IT2TSKNFN online are derived from the Lyapunov synthesis approach; thus, the stability and robustness of the overall control system are guaranteed. Finally, the proposed control algorithms are realized within a TMS320VC33 DSP-based control computer. Simulated and experimental results of an LMSM drive are provided to verify the effectiveness of the proposed IT2TSKNFN control system.

Self-adaptive interval type-2 neural fuzzy network control for PMLSM drives

This paper proposes a self-adaptive interval type-2 neural fuzzy network (SAIT2NFN) control system for the high-precision motion control of permanent magnet linear synchronous motor (PMLSM) drives. The antecedent parts in the SAIT2NFN use interval type-2 fuzzy sets to handle uncertainties in PMLSM drives, including payload variation, external disturbance, and sense noise. The SAIT2NFN is firstly trained to model the inverse dynamics of PMLSM through concurrent structure and parameter learning. The fuzzy rules in the SAIT2NFN can be generated automatically by using online clustering algorithm to obtain a suitable-sized network structure, and a back propagation is proposed to adjust all network parameters. Then, a robust SAIT2NFN inverse control system that consists of the SAIT2NFN and an error-feedback controller is proposed to control the PMLSM drive in a changing environment. Moreover, the Kalman filtering algorithm with a dead zone is derived using Lyapunov stability theorem for online fine-tuning all network parameters to guarantee the convergence of the SAIT2NFN. Experimental results show that the proposed SAIT2NFN control system achieves the best tracking performance in comparison with type-1 NFN control systems.

Quadratic optimal synchronization of uncertain chaotic systems

Abstract This paper investigates the quadratic optimal synchronization of uncertain chaotic systems with parameter mismatch, parametric perturbations and external disturbances on both master and slave systems. A robust control scheme based on Lyapunov stability theory and quadratic optimal control approach is derived to realize chaotic synchronization. The sufficient criterion for stability condition is formulated in a linear matrix inequality (LMI) form. The effect of uncertain parameters and external disturbance is suppressed to an H ∞ norm constraint. An adaptive algorithm is proposed to adjust the uncertain bound in the robust controller avoiding the chattering phenomena. The simulation results for synchronization of the Chua’s circuit system and the Lorenz system demonstrate the effectiveness of the proposed scheme.