Computer process control with advanced control applications
Computer process control with advanced control applications
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Decoupled fuzzy controller design with single-input fuzzy logic
Fuzzy Sets and Systems - Control and applications
Application of Evolution Strategy in Parallel Populations
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Adaptive Hierarchical Fair Competition (AHFC) Model For Parallel Evolutionary Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Nonlinear control of underactuated mechanical systems with application to robotics and aerospace vehicles
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Evolutionary Computation
Stable and optimal fuzzy control of linear systems
IEEE Transactions on Fuzzy Systems
A robust self-tuning scheme for PI- and PD-type fuzzy controllers
IEEE Transactions on Fuzzy Systems
Clustering-based hierarchical genetic algorithm for complex fitness landscapes
International Journal of Intelligent Systems Technologies and Applications
Genetic regulatory network-based symbiotic evolution
Expert Systems with Applications: An International Journal
Constructing a novel mortality prediction model with Bayes theorem and genetic algorithm
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
In this study, we present a design of an optimized fuzzy cascade controller based on Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) for a rotary inverted pendulum system. In this system, one controls the movement of a pendulum through the adjustment of a rotating arm. The objective is to control the position of the rotating arm and to make the pendulum maintain the unstable equilibrium point at vertical position. To control the system, we design a fuzzy cascade controller scheme which consists of two fuzzy controllers arrange in a cascaded topology. The parameters of the controller are optimized by means of the HFCGA algorithm. The fuzzy cascade scheme comprises two controllers located in two loops. An inner loop controller governs the position of the rotating arm while an outer controller modifies a set point of the inner controller implied by the changes of the angle of pendulum. The HFCGA being a computationally effective scheme of the Parallel Genetic Algorithm (PGA) has been developed to eliminate an effect of premature convergence encountered in Serial Genetic Algorithms (SGA). It has emerged as an effective optimization vehicle to deal with very large search spaces. A comparative analysis involving computing simulations and practical experiment demonstrates that the proposed HFCGA based fuzzy cascade controller comes with superb performance in comparison with the conventional Linear Quadratic Regulator (LQR) controller as well as HFCGA-based PD cascade controller.