Fujun Peng

Actuator Placement Optimization and Adaptive Vibration Control of Plate Smart Structures

In this paper, a performance criterion is proposed for the optimization of piezoelectric patch actuator locations on flexible plate structures based on maximizing the controllability grammian. This is followed by the determination of parameters required for actuator location optimization through Structuring Analysis in ANSYS Finite Element Analysis Package. Genetic Algorithm is then used to implement the optimization. Finally, with the actuators bonded on optimized locations, a filtered-x LMS-based multichannel adaptive control is applied to suppress vibration response of the plate. Numerical simulations are performed in suppressing tri-sinusoidal response at three points of the plates. The results show that the developed actuator placement optimization methodology is very effective in searching for the optimal actuator locations that minimize the energy requirement of vibration control. The control algorithm is also demonstrated to be efficient and robust in the smart structure vibration control.

Testing of Membrane Space Structure Shape Control Using Genetic Algorithm

This paper investigates the application of the genetic algorithm in active control of inflatable structures. The algorithm is used to search for the optimal tensions, which minimize membrane wrinkles. A genetic algorithm-based control system is developed using MATLAB, LabView, and Automation Manager. The control system is tested on a 200 x 300 mm rectangular Kapton membrane pulled by three tensions along each edge. Different tension combinations are exerted onto the membrane through SMA actuators. All tensions are calculated through the strains of a thin aluminum strip installed in the tension links. A vision system is developed to measure the membrane flatness under different tension combinations. Testing results show that the genetic algorithm works very well in finding the optimal tensions.

Testing of Inflatable-Structure Shape Control Using Genetic Algorithms and Neural Networks

I NFLATABLE structures have attractedmuch interest in the space community due to their unique advantages in achieving lowmass and high packaging efficiency [1,2]. We are currently working on an in-house research and development project of a membrane synthetic aperture radar antenna and solar array (see Fig. 1). It is expected that the membrane will be subjected to flatness problems during its lifetime in orbit, due to the thermal variation in space. A purely passive control method may not be sufficient to keep the membrane flat. Hence, an active control system is proposed to adjust the tensions according to the thermal variation. Actuators are installed in series with the links, such that the tensions stretching the membrane can be adjusted. A genetic algorithm and neural network (GA–NN) scheme is proposed for modeling and tension optimization. The neural network model of the membrane is established as a mapping from the boundary stretching tensions and space environment to membrane flatness. After the neural network training is completed, the membrane flatness can be estimated by inputting the measured stretching tensions and space environment data to the neural network model. Based on the neural network model, the genetic algorithm is applied to search for the optimal tensions that minimize the membrane wrinkles [3]. Experimental results demonstrate the effectiveness of the proposed scheme.

Application of Shape Memory Alloy Actuators in Active Shape Control of Inflatable Space Structures

SMA (shape memory alloy) actuators have found a wide range of applications due to their unique properties such as high force, long stroke, small size, light weight, and silent operation, etc. However, their strong nonlinear properties make them a challenge to achieve accurate actuation. This paper presents a simple control strategy based on the idea of adjusting the SMA wire temperature as fast as possible. This strategy is simple, stable, and requires no hysteresis model or thermal model. This strategy is tested with displacement output, and effects of updating rate and input current on control accuracy are also discussed. This control strategy is then used for active shape control of inflatable space structures. Results indicate that under this control strategy, shape memory alloy wire actuators can offer very good accuracy, and for inflatable structure shape control, great improvement can be achieved

Development of GA-based control system for active shape control of inflatable space structures

This paper describes the development of a control system used for the active shape control of inflatable space structures. The genetic algorithm is utilized for the optimization of control variables. A vision system is implemented for the measurement of the structure shape. Shape memory alloy wire actuators are used to exert the obtained optimal tensions. The developed control system is then tested on a 200mm times 300mm rectangular Kapton membrane structure. The membrane is pulled by three tensions along each edge. Different combinations of the tensions produce various wrinkles on the membrane. Test results indicate that the developed control system works very well in improving the structure shape precision

Active Control of Inflatable Structure Membrane Wrinkles Using Genetic Algorithm and Neural Network

This paper investigates the application of genetic algorithm and neural network in active control of inflatable structure membrane wrinkles. The membrane to be controlled is a 500mm × 500mm Kapton membrane, pulled by two pairs of forces applied at the four corners along the diagonals. Different combinations of the tensions produce various wrinkles within the membrane. The genetic algorithm is introduced briefly and then used in searching for the optimal force that minimizes the amplitude of the membrane. To predict the membrane flatness in space where direct measurement of membrane flatness is difficult, a neural network model is proposed to map boundary stretching tensions and space environment to membrane flatness. Numerical simulation shows that genetic algorithm works very well in optimizing the tensions and neural network is effective to estimate the flatness of the membrane.

Shape Estimation of Inflatable Space Structures Using Radial Basis Function Neural Networks

Inflatable space structures need to maintain in a desired shape in space in order to achieve satisfactory performance. The active shape control technique has shown its advantages in solving this problem. One difficulty to realize an active control system in space is how to measure the shape of inflatable structures. This paper proposes a neural network scheme to estimate the shape of inflatable structures, instead of performing measurements directly. A radial basis function neural network is trained on the ground to map environment information and control variables into the structure shape. After the neural network training completes, an estimation of the structure shape can be obtained by inputting the measured environment data and control variables to the neural network. Some validation studies have been conducted in laboratory on the estimation of the flatness of a rectangular Kapton membrane. The results showed the proposed scheme gave very good estimations of the membrane flatness

Shape estimation of inflatable space structures using neural network

Inflatable space structures need to maintain in a desired shape in space in order to achieve satisfactory performance. The active shape control technique has shown its advantages in solving this problem. One difficulty to realize an active control system in space is how to establish a model that reflects the structure shapes under different environment and boundary tensions. This paper proposes a neural network scheme to estimate the shape of inflatable structures. A neural network is trained to map environment information and control tensions into the structure shape. After the neural network training completes, an estimation of the structure shape can be obtained by inputting the measured environment data and control variables to the neural network. Some validation studies have been conducted in laboratory on the estimation of the flatness of a rectangular Kapton membrane. The results showed the proposed scheme gave very good estimations of the membrane flatness

Adaptive Vibration Control of Flexible Plate Structures With Actuator Placement Optimization

This paper investigates piezoelectric actuator placement optimization on a rectangular plate structure and the vibration control of the structure. In the first part, an actuator placement optimization method is developed based on maximizing the controllability grammian. It is then implemented using Structuring Analysis in ANSYS Finite Element Analysis Package and Genetic Algorithm. In the second part, a filtered-x LMS-based multi-channel adaptive control is used to suppress vibration response of the plate. Numerical simulations are performed in suppressing tri-sinusoidal response at three points of the plates and the results show the control algorithm is efficient and robust in reducing the plate’s vibration. The results also demonstrate that the developed actuator placement optimization method is effective to reduce the energy required for achieving significant vibration reduction.Copyright