Active Inference for Integrated State-Estimation, Control, and Learning

This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators. It is based on the active inference framework, prominent in computational neuroscience as a theory of the brain, where behaviour arises from minimizing variational free-energy. The robotic manipulator shows adaptive and robust behaviour compared to state-of-the-art methods. Additionally, we show the exact relationship to classic methods such as PID control. Finally, we show that by learning a temporal parameter and model variances, our approach can deal with unmodelled dynamics, damps oscillations, and is robust against disturbances and poor initial parameters. The approach is validated on the `Franka Emika Panda' 7 DoF manipulator.Comment: 7 pages, 6 figures, accepted for presentation at the International Conference on Robotics and Automation (ICRA) 202

An intelligent recommendation system framework for student relationship management

In order to enhance student satisfaction, many services have been provided in order to meet student needs. A recommendation system is a significant service which can be used to assist students in several ways. This paper proposes a conceptual framework of an Intelligent Recommendation System in order to support Student Relationship Management (SRM) for a Thai private university. This article proposed the system architecture of an Intelligent Recommendation System (IRS) which aims to assist students to choose an appropriate course for their studies. Moreover, this study intends to compare different data mining techniques in various recommendation systems and to determine appropriate algorithms for the proposed electronic Intelligent Recommendation System (IRS). The IRS also aims to support Student Relationship Management (SRM) in the university. The IRS has been designed using data mining and artificial intelligent techniques such as clustering, association rule and classification

Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed

Backstepping Sliding Mode Control for Inverted Pendulum System with Disturbance and Parameter Uncertainty

The inverted pendulum system is highly popular in control system applications and has the characteristics of unstable, nonlinear, and fast dynamics. A nonlinear controller is needed to control a system with these characteristics. In addition, there are disturbances and parameter uncertainty issues to be solved in the inverted pendulum system. Therefore, this study uses a nonlinear controller, which is the backstepping sliding mode control. The controller is robust to parameter uncertainty and disturbances so that it is suitable for controlling an inverted pendulum system. Based on testing with step and sine reference signals without interference, the controller can stabilize the system well and has a fast response. In testing with disturbances and mass uncertainty, the backstepping sliding mode controller is robust against these changes and able to make the system reach the reference value. Compared with sliding mode control, backstepping sliding mode control has a better and more robust response to disturbances and parameter uncertainty

INTELLIGENT CONTROLLING THE GRIPPING FORCE OF AN OBJECT BY TWO COMPUTER-CONTROLLED COOPERATIVE ROBOTS

This paper presents a Multiple Adaptive Neuro-Fuzzy Inference System (MANFIS)-based method for regulating the handling force of a common object. The foundation of this method is the prediction of the inverse dynamics of a cooperative robotic system made up of two 3-DOF robotic manipulators. Considering the no slip in contact between the tool and the object, an object is moved. to create and feed the MANFIS database, the inverse kinematics and dynamic equations of motion for the closed chain of motion for both arms are established in Matlab. Results from a SimMechanic simulation are given to demonstrate how well the suggested ANFIS controller works. Several manipulated object movements covering the shared workspace of the two manipulator arms are used to test the proposed control strategy

Intelligent robust control of redun-dant smart robotic arm Pt II: Quantum computing KB optimizer

In the first part of the article, two ways of fuzzy controller’s implementation showed. First way applied one controller for all links of the manipulator and showed the best performance. However, such an implementation is not possible in complex control objects, such as a planar redundant manipulator with seven degrees of freedom (DoF). The second way use of separated control when an independent fuzzy controller controls each link. The decomposition control due to a slight decrease in the quality of management has greatly simplified the processes of creating and placing knowledge bases. In this paper (Part II), the advantages and limitations of intelligent control systems based on soft computing technology described. To eliminate the mismatch of the work of separate independent fuzzy controllers, methods for self-organizing coordination control based on quantum computing technologies to create and design robust intelligent control systems for robotic manipulators with 3DOF and 7DOF described. Quantum fuzzy inference as quantum self-organization algorithm of imperfect KBs introduced. Quantum computational intelligence smart toolkit QCOptKBTMbased on quantum fuzzy inference applied. QCOptKBTM toolkit include quantum deep machine learning in on line. Successful engineering application of end-to-end quantum computing information technologies (as quantum sophisticated algorithms and quantum programming) in searching of solutions of algorithmic unsolved problems in classical dynamic intelligent control systems, artificial intelligence (AI) and intelligent cognitive robotics discussed. Quantum computing supremacy in efficient solution of intractable classical tasks as global robustness of redundant robotic manipulator in unpredicted control situations demonstrated. As result, the new synergetic self-organization information effect of robust KB design from responses of imperfect KBs (partial KB robustness cretead on toolkit SCOptKBTM in Pat I) fined

Waypoint Navigation of AR.Drone Quadrotor Using Fuzzy Logic Controller

In this paper, AR.Drone is flown autonomously from the initial position (x,y,z) to the desired position called waypoint (xdes,ydes,zdes) using Fuzzy Logic Controller (FLC). The FLC consists of three control loops which are pitch control loop, roll control loop and vertical rate control loop. Pitch control loop is used to control the x-position of the AR.Drone; the inputs are the desired x-position and current value of x-position, while its output is the pitch.  Roll control loop is used to control the y-position of the AR.Drone; the inputs are the desired y-position and current value of y-position, while its output is the roll. Vertical rate control loop is used to control the z-position of the AR.Drone; the inputs are the desired z-position and current value of z-position and its output is the vertical rate. The algorithm is realized in three flight schemes and the navigation data is recorded. The first flight scheme: a desired x-position, xdes, of AR.Drone will be reached first followed by a desired y-position, ydes, and lastly a desired z-position, zdes.  The second flight scheme: a desired x-position and y-position, (xdes,ydes), will be reached simultaneously followed by a desired z-position, zdes. The third flight scheme: AR.Drone flies towards to desired position (xdes,ydes,zdes) simultaneously. The results show that the AR.Drone can reach the waypoint with the three schemes well. However, the flight scheme straight towards the waypoint with the FLC working simultaneously is the most satisfying one