In this paper we develop a modified MRAC strategy for use on plants with unmodelled high frequency dynamics. The MRAC strategy is made up of two parts, an adaptive control part and a fixed gain control part. The adaptive algorithm uses a combination of low and high pass filters such that the frequency range for the adaptive part of the strategy is limited. This reduces adaptation to unexpected ...

This chapter shares with Chapter 9 the adoption of a model predictive control (MPC) framework for flight control applications, but the details differ substantially. In particular, the control feedback in this case is a superposition of a neural-network-based nonlinear mapping and a nonlinear state-dependent Riccati equation (SDRE) controller. The neural network is optimized (trained) online for...

This paper presents a method of MRAC(model reference adaptive control) for multi-input multi-output(MIMO) nonlinear systems using NNs(neural networks). The control input is given by the sum of the output of a model reference adaptive controller and the output of the NN(neural network). The NN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the us...

A neural network-based model reference adaptive control approach (MRAC) for ship steering systems is proposed in this paper. For the nonlinearities of ship steering system, performances of traditional adaptive control algorithms are not satisfactory in fact. The presented MRAC system utilizes RBF neural network to approximate the unknown nonlinearities in order to get a high adaptive control pe...

The development of control systems for a two degreeof-freedom vibration suppression system using a magnetorheological (MR) fluid damper is the subject of this paper. It is assumed that system encounters impulsive disturbance forces. The objective is to use the (MR) fluid damper for the position control and vibration suppression of the payload. The control force is generated by regulating the el...

Quick, precise control of a flexible manipulator in a space environment is essential for future Space Station repair and satellite servicing. Numerous control algorithms have proven successful in controlling rigid manipulators with colocated sensors and actuators; however, few have been tested on a flexible manipulator with noncolocated sensors and actuators. In this thesis, a model reference a...

This paper presents the dynamic performances of an indirect vector controlled induction motor (IVCIM) using a fuzzy logic (FL) based model reference adaptive control (MRAC) slip gain tuner for speed regulation in the drive. In high performance AC drives the motor speed should closely match with the specified reference speed irrespective of the variations in the load, motor parameters and model ...

The paper presents performance and robustness analysis of the modified reference model MRAC (model reference adaptive control) or M-MRAC in short, which differs from the conventional MRAC systems by feeding back the tracking error to the reference model. The tracking error feedback gain in concert with the adaptation rate provides an additional capability to regulate not only the transient perf...