Yan-Ru Hu

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.

Application of SMA in Membrane Structure Shape Control

Shape memory alloy (SMA) 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 actuations. A simple control strategy is presented based on the idea of adjusting the SMA wire temperature as rapidly as possible. This strategy is simple, stable, and requires no hysteresis model or thermal model. The strategy is tested first on tracking displacement outputs, and effects of updating rate and input current on control accuracy are also discussed. It is then used for active shape control of a membrane structure model by adjusting its boundary tensions. Results indicate that under the developed control strategy, SMA wire actuators can offer very good accuracy in tracking displacement outputs and tension outputs. For the membrane structure shape control, the structure shape precision is improved greatly.

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 flatness control of space membrane structures using discrete boundary SMA actuators

Maintenance of membrane structures geometry is crucial for them to be utilized in space missions, for instance, the synthetic aperture radar (SAR) missions, for which a membrane antenna system is being developed at the Canadian Space Agency. An active flatness control system is developed to control and maintain the required surface flatness of the membrane antenna. Taking a mechatronics approach, twenty shape memory alloy (SMA) actuators are installed around the membrane boundary to apply tension forces to the membrane. Wrinkling-related surface deviations have a highly nonlinear relationship with applied tension forces, which can have different forms when the membrane is subject to different thermal disturbances. In this paper, genetic algorithms are used to search for tension force combinations reducing thermally induced wrinkles to the required level. To overcome the premature convergence problem during search processes, adaptive rules are devised to regulate GA parameters so that tension force combinations could be found faster and more robustly, named as an adaptive genetic algorithm (AGA). Through experimental studies, it is demonstrated that the AGA can expedite search process and prevent premature convergence. This intelligent mechatronics solution can potentially be used in real-time active flatness control.

Experimental characterization for active flatness control of membrane structures

Maintenance of membrane structures geometry is crucial for them to be utilized in some space missions, including membrane antennae being developed at the Canadian Space Agency. In the harsh space environment, thermal loading and mechanical loading both can cause the distortion of membrane surfaces. Their effects on surface distortion will interact with each other. In order to properly design active control system, the interaction needs to be well characterized. An experimental setup is designed for this purpose. A square membrane is subjected to mechanical loading provided by corner tension forces and thermal loading provided by a ceramic heater. Surface profile of 7the membrane is measured using a photogrammetry system, which is based the out-of-plane sensitivity. In cases of symmetric thermal loading and asymmetric thermal loading, different mechanical loading cases are evaluated in terms of mitigating surface distortions. Some results are compared and explained using numerical results. Finally, the controllability of membrane structures is discussed using the results from these experiments. The conclusion has been applied to a larger membrane with multiple shape memory alloy actuators designed at the Canadian Space Agency.

Active flatness control of membrane structures using adaptive genetic algorithm

Membrane structures are attracting attention as excellent candidates for lightweight large space structures, which can be utilized to improve the performance and reduce the cost of space exploration and earth observation missions. Membrane structures can be stowed to a small volume during launch and function as large structures after deployed. For many applications, maintaining surface accuracy of membranes is extremely important to achieve satisfactory performance, especially for membrane antennas and adaptive optics. Active flatness control is a vital technology to maintain surface accuracy of membrane structures. In this research, multiple shape memory alloy (SMA) actuators around the boundary of a rectangular membrane are used to apply tension forces to membrane structures to compensate wrinkle effects. The dynamics of membrane structures is nonlinear and computationally expensive, hence unfeasible to be used in real-time active flatness control. As a parallel direct searching method, genetic algorithm (GA) is used search optimal tension force combination on a high dimensional nonlinear surface. Due to increasing number of tension forces to search, the convergence is more difficult to attain. In order to increase responsiveness and convergence of genetic algorithm, an adaptive genetic algorithm (AGA) is proposed. Adaptive rules are incorporated in a modified genetic algorithm to regulate control parameters of genetic algorithm. Through numerical simulation and experimental studies, it is demonstrated that AGA can expedite its search process and prevent premature convergence.

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.