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An adaptive controller system using mnSOM
International Congress Series 1291 (2006) 181 – 184
www.ics-elsevier.com
An adaptive controller system using mnSOM Shuhei Nishida *, Kazuo Ishii, Tetsuo Furukawa Department of Brain Science and Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan
Abstract. Underwater vehicles are expected to be attractive tools in the deep ocean. In order to realize useful and practical robots, underwater vehicles should take their action by judging the changing condition from their own sensors and actuators, and are desirable to make their behavior by themselves in the hazardous working environment. We have investigated the application of neural networks into the Autonomous Underwater Vehicles (AUVs). AUVs have complicated nonlinear dynamics in six degrees of freedom. In our previous adaptive control method using NNs, the information of initial states is getting lost gradually during the process of adaptation. If the environment of the robot is changed, the former environmental information was not effectively reflected in our previous NNs controller. Therefore, the new method which keeps the information of initial state or previous environment and adapt to the new environment should be developed to increase the efficiency of learning and reduce the learning cost with the use of the former environmental information which the robot had learned. The new adaptive control system for AUV using modular network SOM by K. Tokunaga et al. has been proposed. The efficiency of the system is investigated through identification of dynamics of an underwater robot. D 2006 Published by Elsevier B.V. Keywords: Adaptive controller; mnSOM; AUV
1. Introduction Autonomous Underwater Vehicles (AUVs) have great advantages for activities in deep oceans, and are expected to be an attractive tool. However, AUVs have various problems which should be solved such as motion control, acquisition of sensors’ information, determination of action, navigation without collision, self-localization and so on. We have been investigating the application of neural network technology into the AUVs focusing * Corresponding author. Tel./fax: +81 93 695 6102. E-mail address: [email protected] (S. Nishida). 0531-5131/ D 2006 Published by Elsevier B.V. doi:10.1016/j.ics.2005.12.071
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on the capability of neural networks (NNs) such as learning, nonlinear mapping. In order to realize the useful and practical robots that can work in the ocean, underwater vehicles should take their action by judging the changing condition from their own sensors and actuators, and are desirable to make their behaviors with limited efforts of the operators in the hazardous working environment. Therefore, the AUVs should be autonomous and adaptive to their environment. Considering the above features, we proposed an adaptive control system [1] and a collision avoidance system [2] using Self-Organizing Map (SOM) [3], and applied them to an AUV called bTwin-BurgerQ [4]. AUVs have non-liner dynamics in six degrees of freedom, and the changes of the equipment of robots have influence on the control system. In the adaptive control method [1], the initial state of controller is getting less and less during the process of adaptation. Therefore, a method which keeps the information of previous environment and adapts to new environment is needed. This paper describes an adaptive control method and its application to an AUV, using modular network Self-Organizing Map called mnSOM [5] proposed by Tokunaga et al. 2. Control of AUVs using mnSOM What is mnSOM? The mnSOM shown in Fig. 1 can be regarded as one of generalizations as such that each vector unit of the conventional SOM is replaced by a function module such as Multi-Layer Perceptron (MLP), SOM, etc. In addition, mnSOM keeps SOM characteristics such as the interpolation and topology preservation. Application into AUV control. The proposed controller of the robot consists of Recurrent Neural Network (RNN). As shown in Fig. 2, the adaptive controller is realized using two layers architecture. The making processes of control system have three steps: (i) identification by learning; (ii) controller adaptation; and (iii) actual control of robot. Identification by Forward Model Modules (Fig. 2(a)). Forward Model Modules (FMMs) are acquired. Several time series of motion data which represent different dynamics corresponding to the relationship of control signal and states of the robot such
Fig. 1. Architecture of mnSOM.
S. Nishida et al. / International Congress Series 1291 (2006) 181–184
183
Fig. 2. Learning processes of proposed adaptive controller.
that one module represents an option dynamics property in advance are fed into RNNmnSOM, and FMMs are obtained. Controller modules (Fig. 2(b)). Controller Network Modules (CNMs) are acquired and connected to FMMs. The target states variables are given to CNMs and output data (control signals) calculated in CNMs are given to all FMMs. The optimization of CNMs is carried out by back-propagation using the error signal between target states and estimated states of FMMs regarding an FMM and a CNM as one NN. Control of actual robot (Fig. 2(c)). The condition of the robot is determined as the bestmatching module (BMM) by feeding a certain time series data into each FMM. After the FMM is selected, the output of the CNM corresponding to the FMM is given to the robot. The adaptive controller using mnSOM is realized according to the processes (i)–(iii). 3. Identification In order to evaluate the identification capability of RNN-mnSOM, some sets of time series data are prepared by changing the parameter M and C in the following equation of motion. F ¼ M x¨ þ Cj˙x j˙x
ð1Þ
where F is the external force, x˙ is velocity, x¨ is acceleration, M is mass including added mass, and C is the drag coefficient. These time series are measured in 50 s and sampling rate is 10 Hz. Fig. 3(a) shows the result of 30,000 times learning; in this figure each square expresses module. The BMM for teaching data (C = 25, 50, 100) are located in the positions (3,1), (1,1) and (3,2), respectively. It is shown that these modules are arranged to
184
S. Nishida et al. / International Congress Series 1291 (2006) 181–184
Fig. 3. Simulation resulted map and estimated parameters of equation.
be from other BMMs. Fig. 3(b) shows the time series of limit cycle which is obtained using FMMs in Fig. 3(a). The solid line means force, the heavy line is acceleration, and the dashed line is velocity. The estimated parameters M and C in each module are represented in Fig. 3(c). The estimated parameters take similar values with those in Eq. (1) and the interpolations among modules are carried out successfully. 4. Conclusions In this paper, the adaptive controller using mnSOM is proposed. This method is able to interpolate teaching data, therefore to obtain dynamic model and controller without teaching data for unknown states is expected. It has the ability to adapt quickly for unknown states. The performances remain to be evaluated in future experiments. References [1] [2] [3] [4] [5]
K. Ishii, T. Fujii, T. Ura, IEEE Journal of Oceanic Engineering 20 (3) (1995) 221 – 228. K. Ishii, S. Nishida, T. Ura, Proceeding of ICRA’04, 2004, pp. 4466 – 4471. T. Kohonen, Biological Cybernetics 43 (1982) 59 – 69. T. Fujii, et al., Proceeding of OCEANS’93, 1993, pp. I186 – I191. K. Tokunaga, T. Furukawa, S. Yasui, Proceeding of WSOM’03, 2003, pp. 173 – 178.
www.ics-elsevier.com
An adaptive controller system using mnSOM Shuhei Nishida *, Kazuo Ishii, Tetsuo Furukawa Department of Brain Science and Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan
Abstract. Underwater vehicles are expected to be attractive tools in the deep ocean. In order to realize useful and practical robots, underwater vehicles should take their action by judging the changing condition from their own sensors and actuators, and are desirable to make their behavior by themselves in the hazardous working environment. We have investigated the application of neural networks into the Autonomous Underwater Vehicles (AUVs). AUVs have complicated nonlinear dynamics in six degrees of freedom. In our previous adaptive control method using NNs, the information of initial states is getting lost gradually during the process of adaptation. If the environment of the robot is changed, the former environmental information was not effectively reflected in our previous NNs controller. Therefore, the new method which keeps the information of initial state or previous environment and adapt to the new environment should be developed to increase the efficiency of learning and reduce the learning cost with the use of the former environmental information which the robot had learned. The new adaptive control system for AUV using modular network SOM by K. Tokunaga et al. has been proposed. The efficiency of the system is investigated through identification of dynamics of an underwater robot. D 2006 Published by Elsevier B.V. Keywords: Adaptive controller; mnSOM; AUV
1. Introduction Autonomous Underwater Vehicles (AUVs) have great advantages for activities in deep oceans, and are expected to be an attractive tool. However, AUVs have various problems which should be solved such as motion control, acquisition of sensors’ information, determination of action, navigation without collision, self-localization and so on. We have been investigating the application of neural network technology into the AUVs focusing * Corresponding author. Tel./fax: +81 93 695 6102. E-mail address: [email protected] (S. Nishida). 0531-5131/ D 2006 Published by Elsevier B.V. doi:10.1016/j.ics.2005.12.071
182
S. Nishida et al. / International Congress Series 1291 (2006) 181–184
on the capability of neural networks (NNs) such as learning, nonlinear mapping. In order to realize the useful and practical robots that can work in the ocean, underwater vehicles should take their action by judging the changing condition from their own sensors and actuators, and are desirable to make their behaviors with limited efforts of the operators in the hazardous working environment. Therefore, the AUVs should be autonomous and adaptive to their environment. Considering the above features, we proposed an adaptive control system [1] and a collision avoidance system [2] using Self-Organizing Map (SOM) [3], and applied them to an AUV called bTwin-BurgerQ [4]. AUVs have non-liner dynamics in six degrees of freedom, and the changes of the equipment of robots have influence on the control system. In the adaptive control method [1], the initial state of controller is getting less and less during the process of adaptation. Therefore, a method which keeps the information of previous environment and adapts to new environment is needed. This paper describes an adaptive control method and its application to an AUV, using modular network Self-Organizing Map called mnSOM [5] proposed by Tokunaga et al. 2. Control of AUVs using mnSOM What is mnSOM? The mnSOM shown in Fig. 1 can be regarded as one of generalizations as such that each vector unit of the conventional SOM is replaced by a function module such as Multi-Layer Perceptron (MLP), SOM, etc. In addition, mnSOM keeps SOM characteristics such as the interpolation and topology preservation. Application into AUV control. The proposed controller of the robot consists of Recurrent Neural Network (RNN). As shown in Fig. 2, the adaptive controller is realized using two layers architecture. The making processes of control system have three steps: (i) identification by learning; (ii) controller adaptation; and (iii) actual control of robot. Identification by Forward Model Modules (Fig. 2(a)). Forward Model Modules (FMMs) are acquired. Several time series of motion data which represent different dynamics corresponding to the relationship of control signal and states of the robot such
Fig. 1. Architecture of mnSOM.
S. Nishida et al. / International Congress Series 1291 (2006) 181–184
183
Fig. 2. Learning processes of proposed adaptive controller.
that one module represents an option dynamics property in advance are fed into RNNmnSOM, and FMMs are obtained. Controller modules (Fig. 2(b)). Controller Network Modules (CNMs) are acquired and connected to FMMs. The target states variables are given to CNMs and output data (control signals) calculated in CNMs are given to all FMMs. The optimization of CNMs is carried out by back-propagation using the error signal between target states and estimated states of FMMs regarding an FMM and a CNM as one NN. Control of actual robot (Fig. 2(c)). The condition of the robot is determined as the bestmatching module (BMM) by feeding a certain time series data into each FMM. After the FMM is selected, the output of the CNM corresponding to the FMM is given to the robot. The adaptive controller using mnSOM is realized according to the processes (i)–(iii). 3. Identification In order to evaluate the identification capability of RNN-mnSOM, some sets of time series data are prepared by changing the parameter M and C in the following equation of motion. F ¼ M x¨ þ Cj˙x j˙x
ð1Þ
where F is the external force, x˙ is velocity, x¨ is acceleration, M is mass including added mass, and C is the drag coefficient. These time series are measured in 50 s and sampling rate is 10 Hz. Fig. 3(a) shows the result of 30,000 times learning; in this figure each square expresses module. The BMM for teaching data (C = 25, 50, 100) are located in the positions (3,1), (1,1) and (3,2), respectively. It is shown that these modules are arranged to
184
S. Nishida et al. / International Congress Series 1291 (2006) 181–184
Fig. 3. Simulation resulted map and estimated parameters of equation.
be from other BMMs. Fig. 3(b) shows the time series of limit cycle which is obtained using FMMs in Fig. 3(a). The solid line means force, the heavy line is acceleration, and the dashed line is velocity. The estimated parameters M and C in each module are represented in Fig. 3(c). The estimated parameters take similar values with those in Eq. (1) and the interpolations among modules are carried out successfully. 4. Conclusions In this paper, the adaptive controller using mnSOM is proposed. This method is able to interpolate teaching data, therefore to obtain dynamic model and controller without teaching data for unknown states is expected. It has the ability to adapt quickly for unknown states. The performances remain to be evaluated in future experiments. References [1] [2] [3] [4] [5]
K. Ishii, T. Fujii, T. Ura, IEEE Journal of Oceanic Engineering 20 (3) (1995) 221 – 228. K. Ishii, S. Nishida, T. Ura, Proceeding of ICRA’04, 2004, pp. 4466 – 4471. T. Kohonen, Biological Cybernetics 43 (1982) 59 – 69. T. Fujii, et al., Proceeding of OCEANS’93, 1993, pp. I186 – I191. K. Tokunaga, T. Furukawa, S. Yasui, Proceeding of WSOM’03, 2003, pp. 173 – 178.