A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Input-to-state stabilization of dynamic neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An approach to stability criteria of neural-network control systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Control of a nonholonomic mobile robot using neural networks
IEEE Transactions on Neural Networks
A neural approach for control of nonlinear systems with feedback linearization
IEEE Transactions on Neural Networks
Control of a class of nonlinear discrete-time systems using multilayer neural networks
IEEE Transactions on Neural Networks
Stable adaptive neurocontrol for nonlinear discrete-time systems
IEEE Transactions on Neural Networks
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This paper presents the application of an evolutionary neural network controller in a stabilisation problem involving an inverted pendulum. It is guaranteed that the resulting closed-loop discrete system is asymptotically stable. The process of training the neural network controller can be treated as a constrained optimisation problem where the equality constraint is derived from the Lyapunov stability criteria. The decision variables in this investigation are made up from the connection weights in the neural network, a positive definite matrix required for the Lyapunov function and matrices for the stability constraint while the objective value is calculated from the closed-loop system performance. The optimisation technique chosen for the task is a variant of genetic algorithms called a cooperative coevolutionary genetic algorithm (CCGA). Two control strategies are explored: model-reference control and optimal control. In the model-reference control, the simulation results indicate that the tracking performance of the system stabilised by the evolutionary neural network is superior to that controlled by a neural network, which is trained via a neural network emulator. In addition, the system stabilised by the evolutionary neural network requires the energy in the level which is comparable to that found in the system that uses a linear quadratic regulator in optimal control. This confirms the usefulness of the CCGA in nonlinear discrete system stabilisation applications.