Hierarchical Control Of Large-scale Systems Research Articles

Fuzzy-based interaction prediction approach for hierarchical control of large-scale systems

This paper presents a fuzzy-based interaction prediction approach (F-IPA) for two-level optimal control of large-scale systems. The design procedure uses a decomposition/coordination framework of hierarchical structures. At the first level, the system is decomposed into subsystems for which subproblems are formed. At the second level, a fuzzy coordinator is used to predict the coordination parameters needed to coordinate the solutions of the first level subproblems. The fuzzy coordinator uses a critic vector to evaluate its performance and learn its parameters by minimizing an energy function. The proposed control scheme is implemented on a two-degrees-of-freedom (2DOF) model of robot manipulator with parallel links and two inverted pendulums on cars. Simulation results are obtained and compared with the gradient-based interaction prediction approach (G-IPA) and a centralized optimization.

Novel interaction prediction approach to hierarchical control of large-scale systems

In this paper, a new interaction prediction approach for hierarchical control of non-linear large-scale systems is presented. The proposed approach uses a new gradient-type coordination scheme which is robust with respect to the parameters' variation, and also has a good convergence rate. In classical coordination strategies, which can be divided into the gradient-type and substitution-type approaches, it is not possible to improve the robustness and the convergence rate at the same time, since by increasing one the other decreases. The proposed approach has the main advantages of the gradient-type algorithms in being independent of the parameter's variation and also the initial guess of the coordination vector. In addition, because of the proposed coordination strategy, in which the update of the coordination vector directly causes the reduction of the coordination error, the number of iterations is considerably reduced. The robustness and convergence rate of the proposed approach against the other interaction prediction approaches are shown through simulations of a benchmark problem. The simulation results also show the effectiveness of the proposed approach in solving constrained optimisation problems having non-quadratic cost functions. Comparisons are also made with the solutions obtained using centralised approach.

EMOTIONAL LEARNING TO CONTROL LARGE-SCALE SYSTEMS

In this paper, with a new look at emotional controller and modifying its structure; a novel approach to hierarchical control of large-scale systems is introduced. Design of controller is founded on emotional learning and the control system consists of neuro-fuzzy controller, whose weights are updated according to emotional signals. This signal is produced in a block called critic, whose job is to evaluate system behaviour. Simulation results demonstrate that the proposed learning scheme, which is applied to a nonlinear three-tank system, provides better control reliability and robustness than classic robust schemes.

Optimization of hierarchical control based on algebraic approach

A new optimal algorithm to hierarchical control for large-scale systems with time-varying coefficients has been developed based on block pulse series (BPS). The presented approach avoid the complicated calculation and singular solution

127 Tracking and feedback information in hierarchical control of large-scale systems

127 Tracking and feedback information in hierarchical control of large-scale systems

Hierarchical control for large-scale systems with general multiple linear-quadratic structure

A class of large-scale linear control systems, where the overall objective function is a nonlinear function of multiple quadratic performance indices, is investigated in this paper. This type of large-scale control problem with a general multiple linear-quadratic structure is nonseparable in the sense of conventional hierarchical control. Hierarchical control is extended in this paper to large-scale nonseparable control problems, where multiobjective optimization is used as a separation strategy. The large-scale general multiple linear-quadratic control problem is embedded, under certain conditions, into a family of the weighted Lagrangian formulation. The weighted Lagrangian formulation is separable with respect to subsystems and can be effectively solved using the interaction prediction approach at the two lower levels in the proposed three-level solution structure. At the third level, the weighting vector for the weighted Lagrangian formulation is adjusted iteratively to search the optimal weighting vector with which the optimal control of the original large-scale nonseparable control problem is attained. One feature of this hierarchical control scheme is a derived analytical solution for the large-scale general multiple linear-quadratic control.