Tsung-Chih Lin

Comments on "Direct adaptive fuzzy-neural control with state observer and supervisory controller for unknown nonlinear dynamical systems"

In this paper, an observer-based direct adaptive fuzzy-neural network (FNN) controller with supervisory mode for a certain class of high order unknown nonlinear dynamical system is presented. The direct adaptive control (DAC) has the advantage of less design effort by not using FNN to model the plant. By using an observer-based output feedback control law and adaptive law, the free parameters of the adaptive FNN controller can be tuned on-line based on the Lyapunov synthesis approach. A supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be de-activated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Simulation results also show that our initial control effort is much less than those in previous works, while preserving the tracking performance.

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with a state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chuas (1989) chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.

Direct adaptive interval type-2 fuzzy control of multivariable nonlinear systems

A fuzzy logic controller equipped with a training algorithm is developed such that the H∞ tracking performance should be satisfied for a model-free nonlinear multiple-input multiple-output (MIMO) system, with external disturbances. Due to universal approximation theorem, fuzzy control provides nonlinear controller, i.e., fuzzy logic controllers, to perform the unknown nonlinear control actions and the tracking error, because of the matching error and external disturbance is attenuated to arbitrary desired level by using H∞ tracking design technique. In this paper, a new direct adaptive interval type-2 fuzzy controller is developed to handle the training data corrupted by noise or rule uncertainties for nonlinear MIMO systems involving external disturbances. Therefore, linguistic fuzzy control rules can be directly incorporated into the controller and combine the H∞ attenuation technique. Simulation results show that the interval type-2 fuzzy logic system can handle unpredicted internal disturbance, data uncertainties, very well, but the adaptive type-1 fuzzy controller must spend more control effort in order to deal with noisy training data. Furthermore, the adaptive interval type-2 fuzzy controller can perform successful control and guarantee the global stability of the resulting closed-loop system and the tracking performance can be achieved.

Observer-based indirect adaptive fuzzy-neural tracking control for nonlinear SISO systems using VSS and H∞ approaches

Fuzzy control is a model free approach, i.e., it does not require a mathematical model of the system under control. An observer-based indirect adaptive fuzzy neural tracking control equipped with VSS and H∞ control algorithms is developed for nonlinear SISO systems involving plant uncertainties and external disturbances. Three important control methods, i.e., adaptive fuzzy neural control scheme, VSS control design and H∞ tracking theory, are combined to solve the robust nonlinear output tracking problem. A modified algebraic Riccati-like equation must be solved to compensate the effect of the approximation error via adaptive fuzzy neural system on the H∞ control. The overall adaptive scheme guarantees the stability of the resulting closed-loop system in the sense that all the states and signals are uniformly bounded and arbitrary small attenuation level of the external disturbance on the tracking error can be achieved. The simulation results confirm the validity and performance of the advocated design methodology.

Observer-based robust adaptive interval type-2 fuzzy tracking control of multivariable nonlinear systems

In this paper, in order to deal with training data corrupted by noise or rule uncertainties, a new observer-based indirect adaptive interval type-2 fuzzy controller is developed for nonlinear MIMO systems involving external disturbances using fuzzy descriptions to model the plant. Based on the universal approximation theorem, a fuzzy logic controller equipped with a training algorithm is proposed such that the tracking error, because of the matching error and external disturbance, is attenuated to an arbitrary desired level using the H^~ tracking design technique. Simulation results show that the interval type-2 fuzzy logic system can handle unpredicted internal disturbances-data uncertainties, very well, but the adaptive type-1 fuzzy controller must expend more control effort in order to handle noisy training data. In the meantime, the adaptive fuzzy controller can perform successful control and guarantee that the global stability of the resulting closed-loop system and the tracking performance can be achieved.

Direct adaptive fuzzy-neural control with state observer and supervisory controller for unknown nonlinear dynamical systems

In this paper, an observer-based direct adaptive FNN controller with supervisory mode for a certain class of high order unknown nonlinear dynamical system is presented. The direct adaptive control (DAC) has the advantage of less design effort by not using FNN to model the plant. By using an observer-based output feedback control law and adaptive law, the free parameters of the adaptive FNN controller can be tuned online based on the Lyapunov synthesis approach. A supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be de-activated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded.

Adaptive hybrid type-2 intelligent sliding mode control for uncertain nonlinear multivariable dynamical systems

A new adaptive hybrid interval type-2 fuzzy neural network (FNN) controller incorporating sliding mode and Lyapunov synthesis approaches is proposed in this paper to handle the training data corrupted by noise or rule uncertainties for a class of uncertain nonlinear multivariable dynamic systems. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an output feedback control law and adaptive laws, is a combination of interval type-2 indirect and direct adaptive FNN controllers to meet the requirement of sufficient reach for the sliding mode control. A weighting factor, which can be adjusted based on the trade-off between plant knowledge and control knowledge, is included when combining the control efforts of the indirect adaptive FNN controller and the direct adaptive FNN controller. The overall adaptive control scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. The mass--spring--damper nonlinear system is fully illustrated to track sinusoidal signals. The resulting adaptive hybrid interval type-2 FNN control system shows better performance than the adaptive hybrid type-1 FNN control system; it reduces both the tracking error and the control effort and it is more flexible in the design process.

Combined direct/indirect adaptive fuzzy-neural networks control with state observer and supervisory controller for nonlinear dynamic systems

A state observer-based combined direct/indirect adaptive FNN controller with supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The combined adaptive FNN controller, whose free parameters can be tuned online by observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which is adjusted by trade-off between plant knowledge and control knowledge, appended between indirect adaptive FNN control and direct adaptive FNN control. A supervisory controller is appended into the FNN controller to force the state to be within the constraint set.

Indirect adaptive fuzzy-neural control with observer and supervisory control for unknown nonlinear systems

In this paper, we develop an observer-based indirect adaptive fuzzy-neural controller with supervisory mode for a certain class of high order unknown nonlinear dynamical system. The free parameters of the adaptive fuzzy-neural controller with supervisory mode can be tuned on-line by an observer-based output feedback control law and adaptive law, based on the Lyapunov synthesis approach. The fuzzy controller is appended with a supervisory controller. If the fuzzy control system tends to unstable, the supervisory controller starts working to guarantee stability. From the energy point of view, this is a very economical design methodology. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded.