Stable Indirect Adaptive Control Research Articles

Stable Adaptive Neural Control of a Robot Arm

In this paper, stable indirect adaptive control with recurrent neural networks (RNN) is presented for square multivariable non-linear plants with unknown dynamics. The control scheme is made of an adaptive instantaneous neural model, a neural controller based on fully connected “Real-Time Recurrent Learning” (RTRL) networks and an online parameters updating law. Closed-loop performances as well as sufficient conditions for asymptotic stability are derived from the Lyapunov approach according to the adaptive updating rate parameter. Robustness is also considered in terms of sensor noise and model uncertainties. This control scheme is applied to the manipulator robot process in order to illustrate the efficiency of the proposed method for real-world control problems.

Stable adaptive control with recurrent neural networks for square MIMO non-linear systems

In this paper, stable indirect adaptive control with recurrent neural networks is presented for square multivariable non-linear plants with unknown dynamics. The control scheme is made of an adaptive instantaneous neural model, a neural controller based on fully connected “Real-Time Recurrent Learning” (RTRL) networks and an online parameters updating law. Closed-loop performances as well as sufficient conditions for asymptotic stability are derived from the Lyapunov approach according to the adaptive updating rate parameter. Robustness is also considered in terms of sensor noise and model uncertainties. The control scheme is then applied to the Tennessee Eastman Challenge Process in order to illustrate the efficiency of the proposed method for real-world control problems.

Stable indirect adaptive control based on discrete-time T–S fuzzy model

This paper presents an indirect adaptive fuzzy control scheme for uncertain nonlinear asymptotically stable plants. A discrete-time T–S fuzzy input–output model is employed to approximate the unknown plant dynamics. The T–S fuzzy model is fed with its own states, which are indeed its past outputs, rather than the measurements from the plants. Entirely based on this model, a feedback linearization control law is designed by using the model structure, model parameters and model states and then applied to control the model and the plant. Premise and consequent parameters of the model are updated on-line by gradient descent algorithm and recursive least square estimation method using plant output measurements. Stability analysis shows that if there the model structure is accurate the adaptive controller achieves the bounded tracking error and boundedness of all the closed-loop signals. In the ideal case where the signals are persistently exciting during the model parameter adaptation a perfect asymptotic tracking is ensured. The effectiveness of the method is verified by simulation application to SISO and MIMO example plants.

ADAPTIVE FUZZY CONTROL OF A CLASS OF DECENTRALIZED NONLINEAR SYSTEMS AND UNKNOWN DYNAMICS

In this paper, a stable indirect adaptive controller is presented for a class of interconnected nonlinear systems with unknown dynamics and interconnections. The feedback and adaptive mechanisms for each subsystem depend only upon local measurements to provide asymptotic tracking of a reference trajectory. Based on the combination of H∞ optimal control and fuzzy logic control using a fuzzy identifier and fuzzy logic control, the proposed decentralized fuzzy control can easily tackle the output tracking control problem in a completely decentralized way. An example is given to illustrate the tracking performance of the proposed method.

STABLE ADAPTIVE CONTROL WITH RECURRENT NEURAL NETWORKS

In this paper, stable indirect adaptive control with recurrent neural networks is presented for multi-input multi-output (MIMO) square non linear plants with unknown dynamics. The control scheme is made of a neural model and a neural controller based on fully connected RTRL networks. On-line weights updating law, closed loop performance, and boundedness of the neural network weights are derived from the Lyapunov approach. Sufficient conditions for stability are obtained according to the adaptive learning rate parameter.

Extended dynamic fuzzy logic system (DFLS) based indirect stable adaptive control of non-linear systems

Abstract The dynamic fuzzy logic system (DFLS) consists of static fuzzy logic system added with a dynamic element—the integrator—with a feedback constant α. It was shown to possess the important universal approximation capability. Further, Lee and Vukovich developed DFLS based stable indirect adaptive control scheme via Lyapunov synthesis approach for a class of non-linear systems of the form x =f(X)+bu . In this paper, this is extended so that now, it can be applied to a larger class of non-linear dynamic systems, i.e., of the form x =f(X)+g(X)u . It was successfully investigated on a chaotic system-modified Duffing’s equation.

Adaptive control of a class of decentralized nonlinear systems

Within this brief paper, a stable indirect adaptive controller is presented for a class of interconnected nonlinear systems. The feedback and adaptation mechanisms for each subsystem depend only upon local measurements to provide asymptotic tracking of a reference trajectory. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The adaptive scheme is illustrated through the longitudinal control of a string of vehicles within an automated highway system (AHS).

027 Stable indirect adaptive control of continuous-time systems with no A priori knowledge on the parameters

027 Stable indirect adaptive control of continuous-time systems with no A priori knowledge on the parameters

A stable indirect adaptive control scheme for first-order plants with no prior knowledge on the parameters

An indirect adaptive control scheme for a first-order linear time-invariant plant with unknown parameters is presented and analyzed. The scheme requires a priori knowledge of the sign or bounds of the plant parameters. A discontinuous control law, involving the use of probing, is utilized to avoid any singularities caused by the lack of stabilizability of the estimated model. The scheme guarantees signal boundedness and zero residual tracking error. >

Stable Indirect Adaptive Control of Continuous-Time Systems with No A Priori Knowledge on the Parameters

Abstract A crucial drawback of indirect adaptive control is the stabilizability or controllability of the estimated plant model which is required in order to calculate the controller parameters. Loss of stabilizability at a particular instant of time may lead to unbounded signais. In this paper, we propose an indirect adaptive control scheme that overcomes the stabilizability problem, and meets the control objective exactly with no additional a priori knowledge on the unknown plant other than its order. The control scheme switches from the usual certainty equivalence control law to a rich open loop control input, according to an adjustable lower bound for the degree of stabilizability of the estimated plant model. The switching terminates in finite time after which no loss of stabilizability can occur, and the certainty equivalence control law is on for the rest of the: me. In contrast to previous schemes, the proposed scheme, referred to as the switched-excitation approach, requires no knowledge of bounds for the stabilizability degree of the unknown plant and guarantees closed-loop stability and zero residual tracking error.

Adaptive control for systems with bounded disturbances

The authors present an identification algorithm that can be used to design globally stable indirect adaptive controllers for minimum- and nonminimum-phase systems subject to bounded disturbances. The parameter estimation scheme is a least-squares algorithm with dead zone. The dead zone is such that the estimates converge and make it possible to define projected estimates having the same convergence properties as the original estimates. In the minimum-phase case, the projection facility can be used to ensure that the leading coefficient projected estimate is greater than or equal to the true leading coefficient in absolute value. The projection procedure can also be used to avoid pole-zero cancellations in an adaptive pole-placement algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An approach to stable indirect adaptive control

Stable indirect adaptive control of minimum and nonminimum phase plants is established for cases where a priori bounds on the unknown plant parameters are known and where for each set of parameter values within these bounds the plant has no unstable pole-zero cancellation. By incorporating this partial parameter knowledge in the adaptive law and using a nonminimal representation of the plant, it is shown that the adaptive closed loop control system can be written as an exponentially stable system driven by the identification error. The stability of the adaptive control system is then shown using techniques similar to those known from the model reference adaptive control approach.