Computational Intelligence for Robust Control Algorithms of Complex Dynamic Systems with Minimum Entropy Production

Abstract

Our thermodynamic approach to the study and design of robust optimal control processes in nonlinear (in general global unstable) dynamic systems used soft computing based on genetic algorithms with a fitness function as minimum entropy production. Control objects were nonlinear dynamic systems involving essentially nonlinear stochastic differential equations. An algorithm was developed for calculating entropy production rate in control object motion and in control systems. Part 1 discusses relation of the Lyapunov function (measure of stability) and the entropy production rate (physical measure of controllability). This relation was used to describe the following qualitative properties and important relations: dynamic stability motion (Lyapunov function), Lyapunov exponent and Kolmogorov-Sinai entropy, physical entropy production rates, and symmetries group representation in essentially nonlinear systems as coupled oscillator models. Results of computer simulation are presented for entropy-like dynamic behavior for typical benchmarks of dynamic systems such as Van der Pol, Duffing, and Holmes-Rand, and coupled oscillators. Parts 2 and 3 discuss the application of this approach to simulation of dynamic entropy-like behavior and optimal benchmark control as a 2-link manipulator in a robot for service use and nonlinear systems under stochastic excitation.

Soft computing simulation design of intelligent control systems in micro-nano-robotics and mechatronics

“…A quantum robot in general case is a mobile quantum system that includes an on-board quantum computer and ancillary systems [1]. The study of quantum computation based on the increased ef®ciency of quantum computation compared to classical computation for solving some important intelligent control problems in [18] are discussed. The simulation results of MNR's motion are examined.…”

Principle of minimum entropy production in applied soft computing for advanced intelligent robotics and mechatronics

“…The structure of the intelligent robust control algorithms in general form for unstable dynamic control objects in Fig. 2 This approach was ®rstly presented in [2,3] as a new physical measure of control quality for complex non-linear controlled objects described as non-linear dissipative models. This physical measure of control quality is based on the physical law of minimum entropy production rate in intelligent control system and in the dynamic behavior of complex control object.…”

“…The basic feature of the given method is the necessity of approximate model investigation for control object and the calculation of the entropy production rate through the parameters of the developed model. The method of the accuracy evaluation for a model approximation using the entropy approach in [3] is presented. The integration of joint systems of equations (the equations of mechanical model motion and the equations of entropy production rate) enable to use the result as the ®tness function in GA.…”