Underactuated robotics: A review <s> a. FNN control Krishnamurthy and Ward <s> Double inverted pendulum on a cart (DIPC) is a highly nonlinear system. Due to its complex dynamics, it is widely used as a test-bed plant for the verification of newly designed controllers. In DIPC, two pendulums are kept upward by linear movements of cart. Because of this linear motions and frequent switching of velocity directions, another nonlinearity caused by friction becomes dominant around the equilibrium point. Friction introduces limit cycles to the system and results in a poor steady-state response. To eliminate these negative effects, the locally linear neuro-fuzzy (LLNF) approach is used to build an inverse model for friction compensation. This model is compared with multilayer perceptron network in order to demonstrate the better performance of LLNF. To stabilize DIPC, a common optimal controller is used, and despite its limited performance, experimental results show that the application of inverse modeling for friction compensation improves the steady-state response outstandingly. <s> BIB001 </s> Underactuated robotics: A review <s> a. FNN control Krishnamurthy and Ward <s> A tracking control of a real inverted pendulum system is implemented in this paper via an adaptive self-constructing fuzzy neural network (ASCFNN) controller. The linear induction motor (LIM) has many excellent performances, such as the silence, high-speed operation and high-starting thrust force, fewer losses and size of motion devices. Therefore, the experiment is implemented by integrating the LIM and an inverted pendulum (IP) system. The ASCFNN controller is composed of an ASCFNN identifier, a computation controller and a robust controller. The ASCFNN identifier is used to estimate parameters of the real IP system and the computational controller is used to sum up the outputs of the ASCFNN identifier. In order to compensate the uncertainties of the system parameters and achieve robust stability of the considered system, the robust controller is adopted. Furthermore, the structure and parameter learning are designed in the ASCFNN identifier to achieve favorable approximation performance. The Mahalanobis distance (M-distance) method in the structure learning is also employed to determine if the fuzzy rules are generated/eliminated or not. Concurrently, the adaptive laws are derived based on the sense of Lyapunov so that the stability of the system can be guaranteed. Finally, the simulation and the actual experiment are implemented to verify the effectiveness of the proposed ASCFNN controller. <s> BIB002 </s> Underactuated robotics: A review <s> a. FNN control Krishnamurthy and Ward <s> It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult t@? analyze with conventional analytical methods. Here, a novel design of an adaptive neuro fuzzy inference system (ANFIS) for estimation contact forces of a new adaptive gripper is presented. Since the conventional analytical methods is a very challenging task, fuzzy logic based systems are considered as potential candidates for such an application. The main points of this paper are in explanation of kinetostatic analyzing of the new gripper structure using rigid body model with added compliance in every single joint. The experimental results can be used as training data for ANFIS network for estimation of gripping forces. An adaptive neuro-fuzzy network is used to approximate correlation between contact point locations and contact forces magnitudes. The simulation results presented in this paper show the effectiveness of the developed method. This system is capable to find any change in ratio of positions of the gripper contacts and magnitudes of the contact forces and thus indicates state of both finger phalanges. <s> BIB003 </s> Underactuated robotics: A review <s> a. FNN control Krishnamurthy and Ward <s> The development of universal grippers able to pick up unfamiliar objects of widely varying shapes and surfaces is a very challenging task. Passively compliant underactuated mechanisms are one way to obtain the gripper which could accommodate to any irregular and sensitive grasping objects. The purpose of the underactuation is to use the power of one actuator to drive the open and close motion of the gripper. The fully compliant mechanism has multiple degrees of freedom and can be considered as an underactuated mechanism. This paper presents a new design of the adaptive underactuated compliant gripper with distributed compliance. The optimal topology of the gripper structure was obtained by iterative finite element method (FEM) optimization procedure. The main points of this paper are in explanation of a new sensing capability of the gripper for grasping and lifting up the gripping objects. Since the sensor stress depends on weight of the grasping object it is appropriate to establish a prediction model for estimation of the grasping object weight in relation to sensor stress. A soft computing based prediction model was developed. In this study an adaptive neuro-fuzzy inference system (ANFIS) was used as soft computing methodology to conduct prediction of the grasping objects weight. The training and checking data for the ANFIS network were obtained by FEM simulations. <s> BIB004 </s> Underactuated robotics: A review <s> a. FNN control Krishnamurthy and Ward <s> The main purpose of this paper is to determine what joints are most strained in the proposed underactuated finger by adaptive neuro-fuzzy methodology. For this, kinetostatic analysis of the finger structure is established with added torsional springs in every single joint. Since the finger’s grasping forces depend on torsional spring stiffness in the joints, it is preferable to determine which joints have the most influence on grasping forces. Hence, the finger joints experiencing the most strain during the grasping process should be determined. It is desirable to select and analyze a subset of joints that are truly relevant or the most influential to finger grasping forces in order to build a finger model with optimal grasping features. This procedure is called variable selection. In this study, variable selection is modeled using the adaptive neuro-fuzzy inference system (ANFIS). Variable selection using the ANFIS network is performed to determine how the springs implemented in the finger joints affect the output grasping forces. This intelligent algorithm is applied using the Matlab environment and the performance is analyzed. The simulation results presented in this paper show the effectiveness of the developed method. <s> BIB005 </s> Underactuated robotics: A review <s> a. FNN control Krishnamurthy and Ward <s> A robust adaptive NN-based output feedback control scheme is presented for a dynamic positioning ship with uncertainties and unknown external disturbances. We tackle the problem that velocity vector of a ship is not available by employing a high-gain observer, and develop the proposed control approach by combing vectorial backstepping with dynamic surface control approach, which is simpler and easier to implement in engineering practice. The neural network (NN) approximation technique is used to compensate for the uncertainties and unknown external disturbances, and it removes the requirement for the prior knowledge about the vessel parameters and external disturbances. Also, it is demonstrated that the proposed control strategy can force the position and yaw angle of a dynamic positioning ship to approach the desired point while guaranteeing all singles of the designed closed-loop dynamic positioning system semi-globally uniformly ultimately bounded by means of the Lyapunov function. Simulation results of a supply ship illustrate the effectiveness of the proposed scheme. <s> BIB006 </s> Underactuated robotics: A review <s> a. FNN control Krishnamurthy and Ward <s> Abstract To obtain adaptive finger passive underactuation can be used. Underactuation principle can be used to adapt shapes of the fingers for grasping objects. The fingers with underactuation do not require control algorithm. In this study a kinetostatic model of the underactuated finger mechanism was analyzed. The underactuation is achieved by adding the compliance in every finger joint. Since the contact forces of the finger depend on contact position of the finger and object, it is suitable to make a prediction model for the contact forces in function of contact positions of the finger and grasping objects. In this study prediction of the contact forces was established by a soft computing approach. Adaptive neuro-fuzzy inference system (ANFIS) was applied as the soft computing method to perform the prediction of the finger contact forces. <s> BIB007 </s> Underactuated robotics: A review <s> a. FNN control Krishnamurthy and Ward <s> The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method. <s> BIB008 </s> Underactuated robotics: A review <s> a. FNN control Krishnamurthy and Ward <s> Abstract Fuzzy critic-based learning forms a reinforcement learning method based on dynamic programming. In this paper, an adaptive critic-based neuro-fuzzy system is presented for an unmanned bicycle. The only information available for the critic agent is the system feedback which is interpreted as the last action performed by the controller in the previous state. The signal produced by the critic agent is used along with the error back propagation to tune (online) conclusion parts of the fuzzy inference rules of the adaptive controller. Simulations and experiments are conducted to evaluate the performance of the proposed controller. The results demonstrate superior performance of the developed controller in terms of improved transient response, robustness to model uncertainty and fast online learning. <s> BIB009
192 defined a fuzzy nervous system. In the literature, BIB001 BIB003 BIB004 BIB007 BIB008 BIB005 BIB009 FNNs could be successfully used for the control of UR systems. Kim and Chwa 200 proposed an obstacle avoidance method in the position stabilization of the WMRs using interval type-2 FNN. This significantly improves the behavior of the robot. A robust adaptive NN-based OFB control scheme is presented for a dynamic positioning ship with uncertainties and unknown external disturbances. BIB006 A tracking control of a real IP system is implemented in Lu et al. BIB002 via an adaptive selfconstructing FNN controller. Concurrently, the adaptive laws are derived based on the sense of Lyapunov so that the stability of the system could be guaranteed. Both fuzzy control and neural network control have been applied to the control of robots. Fuzzy control uses the experience of human experts to control precise models that do not require objects. Fuzzy control could handle nonlinearities and uncertainties in robot control. Neural network control has self-learning capabilities. Applying it to the control of the robot could well overcome the effects of uncertainties, such as environmental changes and load changes. Neural network control and fuzzy control are combined. Fuzzy reasoning is realized by neural network. It could make the neural network have the ability of self-reasoning and induction. Many studies in recent years have proved the commonality of fuzzy logic inference and neural network, which provides a theoretical basis for the application of FNNs. Both the fuzzy system and the neural network system have the function of a general adaptive model-independent estimator. Then there are many commonalities between them. Because of these theoretical commonalities, neural networks could implement fuzzy logic reasoning.