The control objective of interactioncontrol is to achieve force regulation and trajectory tracking. Thus, optimisation should be taken into consideration, since it is the compromise of these two objectives. There has been much research effort in literatures. The well known linear quadratic regulator (LQR) is widely acknowledged as an important solution of optimal control, which is concerned with operating a dynamic system at a minimal cost. In , the LQR is used to determine desired impedance parameters with the environmental dynam- ics known. In , a target impedance is adjusted by online solutions of the defined LQR problem based on environment stiffness and damping. Better than the fixed impedance pa- rameters obtained from LQR technique, the algorithm shows greater adaptability for a wide range of environments. How- ever, the dynamics of the environment is also assumed to be known in this paper above. As presented in , the solution of a Riccati equation could be difficult to find with the unknown dynamics of a environment. Therefore, when the dynamics of a environment is unknown, approaches proposed above may not be used. To copy with this problem, adaptive dynamic programming (ADP) has received much attention and been widely studied –. ADP is a very useful tool in solving optimization and optimal control problems. Based on the idea of ADP, a control action is modified based on the feedback information of a environment. There are many ADP approaches such as heuristic dynamic pro- gramming (HDP), Q-learning and dual-heuristic programming (DHP). The advantage of ADP is that only partial information of the system under control needs to be known. In , optimal impedance parameters are updated by employing a recursive least-square filter-based episodic natural actor-critic algorithm. In , the reinforcement learning (RL) algorithm is adopted to accomplish variable impedance control. However, in many situations, the process of learning is still needed to obtain parameters of a impedence/admittance model . The optimal control method with unknown environment proposed in  is to be applied and developed. The environment model is considered to be a damping-stiffness model, which is a linear system with unknown dynamics.
The problem of optimal tracking control for robot–environmentinteraction is studied in this article. The environment is regarded as a linear system and an admittancecontrol with iterative linear quadratic regulator method is obtained to guarantee the compliant behaviour. Meanwhile, an adaptive dynamic programming-based controller is proposed. Under adaptive dynamic programming frame, the critic network is performed with radial basis function neural network to approximate the optimal cost, and the neural network weight updating law is incorporated with an additional stabilizing term to eliminate the requirement for the initial admissible control. The stability of the system is proved by Lyapunov theorem. The simulation results demonstrate the effectiveness of the proposed control scheme.
Machine learning techniques, instead, can help to have an adaptivecontrol of the robot limbs by estimating the spatial location and the amplitude of the applied force. To this purpose, we propose to use a neural architecture that learns the admittancecontrol of a robotic arm covered with a tactile skin to orient it safely. We use two artificial neuralnetworks that learn in an unsupervised manner (1) the topological configuration of the tactile device not known in advance and (2) the force compliance to apply when a tactile signal has been detected. The first neural network corresponds to the Kohonen self-organizing map (SOM) with neurons that learn the topology of the un-gridded tactile device; each tactile neuron will learn a specific receptive field . The second neural network, based on the neuron model of the Perceptron, will learn to estimate from the position on the tactile surface and from the pressure applied to it the actual compliant forces to apply to control our two degrees of freedom robotic arm in the four directions. Our main goal is to achieve the development of multimodal body representations, tactile and proprioceptive, in humanoid robots for physical interactions with persons and environment. It is the first step to integrating other modalities like vision and more degrees of freedom.
Junaid Zahid et.al.  was developed a controller of the DC motor for one-DOF rehabilitation robot using the Model Reference AdaptiveControl (MRAC). In this project, Model Reference Adaptivecontrol was designed to reduce the positioning error of the robot and able to cope with variations in limb stiffness of the stroke patients. The limb stiffness that are used in this paper are for the wrist stiffness only. The mean wrist stiffness of the movement are included toward flexion, toward extension, toward radial deviation, toward pronation and toward supination. From the mathematical model of the DC motor, the MRAC is designed in the Matlab by using Simulink and use as a reference model in MRAC. PID controller is low adaptability to external disturbance or load and the PID controller is tuned based on a set standard wrist limb stiffness. In this paper, the comparison between the PID controller and MRAC be made to compare the settling time, rise time, steady state error.
of autonomous mobile robot, an attempt has been made to design an intelligent control system and routing for an autonomous mobile robot in a dynamic environment. Also, by using input feedback linearization of the fuzzy system, we provide a new approach to control of mobile robots in dynamic environments. The proposed system is designed in such a way that our intelligent robot determines its own motion plan autonomously, by considering the position and the direction of moving obstacle, and also considering the position of target point. In this motion plan, by using fuzzy inference system and without human intervention, the robot starts to move from beginning point and estimates the position of obstacle sequentially by Kalman filter at any moment. with regard to direction of obstacle movement, the robot selects a direction to the target point in the way that it reaches target point without collision with moving obstacle. In this plan, the force applied to robot wheels is also determined according to the distance between robot and target by fuzzy inference system. So our _______________________________
In this paper, the problem of exponential lag synchronization for a class of neuralnetworks with mixed delays including discrete and distributed delays is investigated via adaptive intermittent control. Based on piecewise analytic method, some suﬃcient conditions for globally exponential lag synchronization are established through constructing a piecewise continuous auxiliary function. It is noted that both the control periods and the control widths in our adaptive intermittent control strategy are allowed to be nonidentical, which extends the scope of application of periodically intermittent control with ﬁxed both control period and control width employed widely in previous works. Moreover, it is shown that the derived globally exponential lag synchronization criteria are related to the control rates rather than the control periods, which facilitates the choice of the control periods for practical problems. Finally, a numerical example is given to illustrate the correctness of the obtained theoretical results.
Abstract – Most practical systems have multiple inputs and multiple outputs, and the applicability of neuralnetworks as practical adaptive identifiers and controllers will eventually be judged by their success in multivariable problems. In this paper, we design a model following adaptive controller for a class of a discrete time multivariable nonlinear systems. Radial Basis Function (RBF) neural network with Minimal Resource Allocation Network (MRAN) training algorithm is used for off-line stable identification. It implements a stable model following adaptive controller by utilizing the identification results. S imulation results demonstrate the proposed controller can drive unknown MIMO nonlinear systems to follow the desired trajectory very well.
Technology, Iran, and his M.Sc. and Ph.D. degrees From Faculty of Robotics and Automation, Moscow State Technical University (Bauman), Russia (in 2006). Now, he is associate professor of mechanical engineering at Guilan University, Rasht, Iran and working in the field of automatic control, robotics and mechatronic systems. Mohammad Ali Nekoui received his M.Sc. degree in Electrical Engineering from the University of Tehran in 1976, Diplome d ’ Espe cialisation in Instrume-ntation etMetrologie from Ecole Superieur d ’ Electricite (SUPEL EC), France, in 1979 and his Ph.D. degree at the School of Electrical and Electronic Engineering in Computer and Control Department from University of Leeds, U.K. in 1997. Since 1980, he has been with the K.N. Toosi. University of Technology. At present he is an Assistant Professor at the Faculty of Electrical and Computer Engineering of this university. His interests include linear and nonlinear optimization, linear systems, optimal control, and different aspects of mathematics in control.
A study on force-feedback interaction with a model of a neural oscillator provides insight into enhanced human- robot interactions for controlling musical sound. We provide differential equations and discrete-time computable equations for the core oscillator model developed by Edward Large for simulating rhythm perception. Using a mechanical analog parameterization, we derive a force-feedback model structure that enables a human to share control of a virtual percussion instrument with a “ robotic ” neural oscillator. A formal human subject test indicated that strong coupling (STRNG) between the force-feedback device and the neural oscillator provided subjects with the best control. Overall, the human subjects predominantly found the interaction to be “ enjoyable ” and “ fun ” or “ entertaining. ” However, there were indications that some subjects preferred a medium-strength coupling (MED), presumably because they were unaccustomed to such strong force-feedback interaction with an external agent. With related models, test subjects performed better when they could synchronize their input in phase with a dominant sensory feedback modality. In contrast, subjects tended to perform worse when an optimal strategy was to move the force-feedback device with a 90° phase lag. Our results suggest an extension of dynamic pattern theory to force-feedback tasks. In closing, we provide an overview of how a similar force-feedback scenario could be used in a more complex musical robotics setting.
structure. The training procedure was applied to a publicly available face recognition dataset, and the performance obtained was comparable to the optimised off-line method. In , the facial images are firstly preprocessed, the boundaries of the region of interest (ROI) are chosen manually between the inter-ocular distance and the distance between the eyes, and then the ROI is normalized to a size of 20 x 30 pixels; after an image is pre-processed to the size 20*30 pixels in greyscale, it is used as input to the SNN. In real time applications, many data samples are 1D feature vectors, so in , Gaussian population encoding is used to encode every input feature into a set of spike times with a population of neurons such that each neuron can spike only once and then a rank order coding learning method is employed for the learning. The learning method used to train the weights in  is based on the rank order of the incoming spikes arrival. However, in these networks , several issues are highlighted: (a) The learning method used to train the weights is based on the order of the incoming spiking arrival. The precise timing information is thrown away despite the fact that the precise times not only carry the rank order information, but also how different they are ; (b) due to the time spent on the calculation of the rank order, the simulation time of the network is slow for large datasets and networks; (c) In , it has been shown that SNN can be used to extract face images features, the network presented is suitable for 2D inputs; however, in real world application, many inputs are represented by a 1D feature vector and the pre-processing of a face image in  is time-consuming for an online system.
When discussing a scenario of human-robotinteraction, obtaining a full description of the state of the environment (specifically the state of the human counterpart) is very relevant to the robot. Evidently, an ultimate goal for HRI would be to have intelligent robots that would also consider the environment surrounding the human in addition to the human itself. However, for the level of HRI scenarios implemented today, where often the context is limited or well predefined and a priori known by the robot, it is usually more than sufficient to have the robot be able to perceive the state in which is the human. This, in fact, is exactly what was referred to as directed perception (or attention) in subsection 2.2.1, and it refers to the perception that is directed by the robot’s goals and influenced by its prospection and expectations. This kind of directed perception allows for the robot to be aware of how its actions are affecting the person with whom it’s interacting, and provides information when selecting its next course of action.
incomplete knowledge present in relation with mechanical parameters and un-modeled dynamics along with a degree of indefiniteness. Also, the unknown rate of dependence of the model to the rate of end effecter load and its load- carrying capacity causes not being able to define an appropriate compensation rate in the controller for it. As a result, the use of other controllers which lack such a limitation is taken into consideration. Even though this control method is able to provide an appropriate function, but compared to the moment disturbances and environmental indefinites, it enjoys high sensitivity. As a result, an adaptive controller can be employed. Adaptivecontrol is used to compensate for parametric uncertainties, suppress constraint uncertainties, and bounded disturbances. This controller, in addition to providing logical responses, enjoys reliability, strength, and appropriate stability in the presence of moment disturbances and uncertainties .
If the system has the user’s attention, it en- sures hearing, understanding, and acceptance, in order, according to the respective grounding cri- teria. As these sub-trees have had their chance to change the presentation agenda to address neg- ative evidence of hearing, understanding and ac- ceptance (see (Vaufreydaz et al., 2016; Aly and Tapus, 2015; Sidner et al., 2006; Skantze et al., 2014) for examples on how to measure these), the system then speaks from the agenda, driving the presentation forward. Only if the tree reaches this leaf without any previous leaf returning RUNNING does the system speak, resulting in incremental, adaptive speech synthesis in the vein of Skantze and Hjalmarsson (2010); Buschmeier et al. (2012); Kopp et al. (2014).
To enhance the stability of the controlled robot while it interacts in a complicated dynamic environment with the human operator, the possibility exists to enhance the system by implementing adaptive FLC, as suggested by Burn et al. . This technique has been developed and used successfully for robot and stable force control in unknown and varied environmental stiffness at the robot/task interface. Moreover, combined fuzzy logic control (FLC) and artificial neuron networks (ANNs), namely neuro-fuzzy control, can also be effectively used [Touati et al, 2002]. This type of control scheme integrates the advantages of both techniques, where the ANNs provides the appropriate tuning of the fuzzy sets, including shape and membership functions, and the rule-base in a controlled system. However, several considerations should be taken into account if such an option is used. For example, the design of neuro-fuzzy control requires large amounts of input and output information to be gathered for training using the learning algorithms of ANNs, and this could introduce longer processing times than when FLC is used on its own.
By learning these basic activation functions, we can do approximation convex and non-convex functions with a piecewise linear activation function with an adaptive parameter . In the current note, we consider ReLU activation function with various configurations. In most successful applications on CNN models, this type of the rectified unit family functions has been extensively used [24-27]. Meanwhile, in these models, most researchers used only basic forms of these activation functions, which is described above. Consequently, according to their simple forms of these activation functions, they have a very restricted representation ability of learning nonlinear transformations. Introducing some combinations with these activation functions, we propose new activation functions to increase the capability of learning non-linear representation and also be adaptive to all input signals. We rely on the new constructed activations techniques on the base activation functions have more flexible forms which can be defined learning from a dataset. Firstly, we introduce two approaches to join these activation functions into one which is given in (1). The first one is depicted as joined function, in which the activation operation is learned by linearly joining basic activation functions. In other word to construct activations functions be adaptable to the specific inputs, another adaptive activation function is proposed. The main idea is that the activation factor is obtained by non-linear joining the base activation functions of a rectified unit type. Secondly, to check the efficiency of the proposed approaches, we perform the several tests on CNN models on well-known benchmark datasets which is available on repository. Our contribution of the work as follows: we propose a new types of activation function on the base of rectified unit family, a joined activation and an adaptive activation to linear and non-linear joining of basic activation functions. The proposed activations functions increase the capability of learning non-linear mappings, and have ability of adaptable to the specific inputs. We constructed and investigated several deep learning CNN models, ————————————————
varying individual predictions, but when averaged in a portfolio, the overall predictive result becomes less noisy and grants a much better prediction than the historical average of the portfolio. Again, I note that microstocks have very volatile returns and using their historical returns (around 2.5%) may serve as a poor predictor, as it ends up inflating R 2 results by around 5 percentage points compared to using a zero-return prediction as Gu et al. (2018). The opposite would be true for All and Big stocks, where a zero-return prediction amplifies R 2 results by a few percentage points, which is why to keep the methodology consistent across size categories, I keep the conventional historical mean as the benchmark for R 2 calculations. The linear benchmarks also perform almost on par with the neuralnetworks, with OLS-3 having R 2 of 16.48%. This is encouraging since if there were some model or data construction error particularly in the market return variable, the predictions should be better in large stocks instead, as they follow the market movements more closely. The downside is that stronger predictability in microstocks has less economic significance as utilizing the information is more expensive in illiquid microstocks for investors. Directional accuracy follows the trend in Table 5, where all models exceed the baseline (50%) and surprisingly OLS-FM has the highest accuracy (57.64%), considering its very poor R 2 .
Abstract--- The migration to wireless network from wired network has been a global trend in the past few decades. The mobility and scalability brought by wireless network made it possible in many applications. Mobile Adhoc Network (MANET) is one of the most important and unique applications. On the contrary to traditional network architecture, MANET does not require a fixed network infrastructure; every single node works as both a transmitter and receiver. Nodes communicate directly with each other when they are both within the same communication range. Otherwise, they rely on their neighbors to relay messages. With the improvements of the technology and cut in hardware costs, we are witnessing a current trend of expanding MANETs into industrial applications. To adjust to such trend, we strongly believe that it is vital to address its potential security issues. In this paper, we propose and implement a new intrusion-detection system named EnhancedAdaptive ACKnowledgment (EAACK) specially designed for MANETs. Compared to contemporary approaches, EAACK demonstrates higher malicious- behaviour-detection rates in certain circumstances while does not greatly affect the network performances.
A very simple example of learning at the organism level which has been worked out a neural level (Fig 3.) is that of sensitization of the gill withdrawal reflex in Alyssa. In the sea mollusk aplysia, a light touch to the animal's siphon results in gill withdrawal. Hebbian learning rule: The formulation of associative learning that has gathered the most attention for those studying the brain was due to Donald Hebb (see quote above). This proposition has led to a number of mathematical rules, the simplest of which is:
The Enhanced ENNs were trained, but in any case learning didnt improve, initially tested the whole set. The ratio of error should not be acceptable; the knowledge learned by the network is not good, the error is too large. The evolutions of the values of the weights, in different division intervals for all patterns, the first division in positives and negatives outputs in, finally four networks have been trained. The error is less when the total pattern set is divided in subsets and one ENN is trained for each subset of pattern. In this example, finally 4 neuralnetworks were constructed: one for each set of patterns S1 · · · S4 obtained, which outputs are I1 · · · I4, .One neural network is trained for each interval. Now, in each one of the sets of patterns obtained, the most important input variables, in each one of the subsets is sought, using the algorithm for extraction (EM). Table 3 shows the weights of the four trained networks obtained in the first phase BM.