Scientific and Technical Journal


ISSN Print 2221-3937
ISSN Online 2221-3805

Developed a method for solving the problem of multiobjective optimisation of anisotropic regulators of multimass electromechanical systems, allowing to satisfy the diverse requirements that apply to the work multimass electromechanical systems in different modes. Selection matrices by formed the stochastic robust control goal vector is carried out by solving a nonlinear programming problem, in witch the objective function and constraints that are generated by nonlinear scheme of compromise, which uses a combination of penalty function method with an internal point for local criteria and restrictions are permissible, and the penalty function method with an external point for the local criteria and restrictions that are not valid. Formulated mathematical programming problem is multiextremal, the calculation of the objective function is algorithmic in nature, including multiple solution of algebraic Riccati equations, Lyapunov equations and of special form equations for calculating the level of system anisotropy, which required for the synthesis of stochastic robust controller, which requires significant computing resources.

To solve the formulated multiextremal mathematical programming problem used bionic multiagent stochastic particle swarm optimization algorithms does not require the calculation of derivatives of optimized function reliably and allow to find the global optimum multiextremal ravine objective functions and objective functions with areas such as "plateau", significantly reducing the amount of computation of the objective function and significantly reduces the cost of computer time.

The results of comparisons of the dynamic characteristics of electromechanical multimass systems with synthesized anisotropic regulators and types regulators. It is shown, that the use of synthesized anisotropic regulators allowed to reduce the random error compensation of external disturbances, reduce regulation and reduce the system sensitivity to changes of parameters plant compared to a system with standard controllers.


  1. Gazi V., (2004), Formation Control of Mobile Robots using Decentralized Nonlinear Servomechanism, In: 12’th Meditteranean Conference on Control and Automation, Kusadasi, рр. 37 – 42 (In English).
  2. Fidan F., and Gazi V., (2010), Target Tracking using Adaptive gain Backstepping Control, In: IFAC Workshop on Adaptation and Learning in Control and Signal Processing, Antalya, Turkey, pp. 78 – 81 (In English).
  3. Duran S., and Gazi V., (2010), Adaptive Formation Control and Target Tracking in a Class of Multi-agent Systems, In: Proc. American Control Conf., Baltimore, MD, (USA), pp. 75 – 80 (In English).
  4. Diamond P., Vladimirov I.G., Kurdjukov A.P., and Semyonov A.V., (2001), Anisotropy – Based Performance Analysis of Linear Discrete Time Invariant Control Systems Int. J. Contro, Vol. 74, pр. 28 – 42 (In English).
  5. Vladimirov I.G., Kurdjukov A.P, and Semyonov A.V., (1996), State-Space Solution to Anisotropy-Based Stochastic – Optimization Problem, Proc. 13th IFAC World Congress, San-Francisco (USA), pр. 427 – 432 (In English).
  6. Semyonov A.V., Vladimirov, and Kurdjukov A.P., (1994), Stochastic Approach to – Optimization, Proc. 33rd IEEE Conf. on Decision and Control., Florida (USA), pр. 2249 – 2250 (In English).
  7. Nikitina T.B.Mnogokriterialnyiy sintez robastnogo upravleniya mnogomassovyimi sistemami [Multicriterion Synthesis of Robust Control by Multimass Systems], (2013), Kharkiv National Automobile and Highway University, Kharkov, Ukraine, 432 p. (In Russian)
  8. Clerc M., (2006), Particle Swarm Optimization, London: ISTE Ltd, 244 p. (In English)
  9. Gazi V., and Passino K.M., (2011), Swarm Stability and Optimization Springer, 318 p. (In English).
  10. Kurdukov A.P., Maximov E.A., and Tchaikovsky M.M., (2006), Computing Anisotropic Optimal Controller for System with Parametric Uncertainty via Homotopy – Based Algorithm, SicPro'06, Moscow, Russian Federation,ICS, – CD-ROM (In English).
  11. Kurdukov A.P., Maximov E.A., and Tchaikovsky M.M., (2006), Homotopy Method for Solving Anisotropy-based stochastic – Optimization Problem with Uncertainty, Proc. 5th IFAC Symposium on Robust Control Design, Toulouse (France), CD-ROM (In English).
Last download:
28 Nov 2019

[ © KarelWintersky ] [ All articles ] [ All authors ]
[ © Odessa National Polytechnic University, 2014-2018. Any use of information from the site is possible only under the condition that the source link! ]