Intelligent Self-Organized Robust Control Design based on Quantum/Soft Computing Technologies and Kansei Engineering

System of systems engineering technology describes the possibility of ill-defined (autonomous or hierarchically connected) dynamic control system design that includes human decision making in unpredicted (unforeseen) control situations. Kansei/Affective Engineering technology and its toolkit include qualitative description of human being emotion, instinct and intuition that are used effectively in design processes of smart/wise robotics and intelligent mechatronics. In presented report the way how these technologies can be married using new types of unconventional computational intelligence is described. System analysis of interrelations between these two important technologies is discussed. The solution of an important problem as robust intelligent control system design based on quantum knowledge base self-organization in unpredicted control situations and information risk is proposed. The background of applied unconventional computational intelligence is soft and quantum computing technologies. Applications of the developed approach in robust integrated fuzzy intelligent control systems are considered using concrete Benchmarks

Intelligent robust control of redundant smart robotic arm Pt I: Soft computing KB optimizer - deep machine learning IT

Redundant robotic arm models as a control object discussed. Background of computational intelligence IT based on soft computing optimizer of knowledge base in smart robotic manipulators introduced. Soft computing optimizer is the toolkit of deep machine learning SW platform with optimal fuzzy neural network structure. The methods for development and design technology of intelligent control systems based on the soft computing optimizer presented in this Part 1 allow one to implement the principle of design an optimal intelligent control systems with a maximum reliability and controllability level of a complex control object under conditions of uncertainty in the source data, and in the presence of stochastic noises of various physical and statistical characters. The knowledge bases formed with the application of a soft computing optimizer produce robust control laws for the schedule of time dependent coefficient gains of conventional PID controllers for a wide range of external perturbations and are maximally insensitive to random variations of the structure of control object. The robustness of control laws is achieved by application a vector fitness function for genetic algorithm, whose one component describes the physical principle of minimum production of generalized entropy both in the control object and the control system, and the other components describe conventional control objective functionals such as minimum control error, etc. The application of soft computing technologies (Part I) for the development a robust intelligent control system that solving the problem of precision positioning redundant (3DOF and 7 DOF) manipulators considered. Application of quantum soft computing in robust intelligent control of smart manipulators in Part II described

Port-Hamiltonian Modeling for Control

This article provides a concise summary of the basic ideas and concepts in port-Hamiltonian systems theory and its use in analysis and control of complex multiphysics systems. It gives special attention to new and unexplored research directions and relations with other mathematical frameworks. Emergent control paradigms and open problems are indicated, including the relation with thermodynamics and the question of uniting the energy-processing view of control, as emphasized by port-Hamiltonian systems theory, with a complementary information-processing viewpoint.</p

An event-triggered observation scheme for systems with perturbations and data rate constraints

International audienceIn this paper, an event-triggered observation scheme is considered for a perturbed nonlinear dynamical system connected to a remote location via a communication channel, which can only transmit a limited amount of data per unit of time. The dynamical system, which is supposed to be globally Lipschitz, is subject to bounded state perturbations. Moreover, at the system’s location, the output is measured with some bounded errors. The objective is to calculate estimates of the state at the remote location in real-time with maximum given error, whilst using the communication channel as little as possible. An event-triggered communication strategy is proposed in order to reduce the average number of communications. An important feature of this strategy is to provide an estimation of the relation between the observation error and the communication rate. The observation scheme’s efficiency is demonstrated through simulations of unicycle-type robots