The novel features of an adaptive PID-like neurocontrol scheme for nonlinear plants are presented. The controller tuning is based on an estimate of the command-error on its output by using a neural predictive model. A robust online learning algorithm, based on the direct use of sliding mode control (SMC) theory is applied. The proposed approach allows handling of the plant-model mismatches, unc...

Progress in diagnosis and treatment of movement disorders after neurological diseases like stroke, cerebral palsy (CP), dystonia and at old age requires understanding of the altered capacity to adequately respond to physical obstacles in the environment. With posture and movement disorders, the control of muscles is hampered, resulting in aberrant force generation and improper impedance regulat...

Power system is a highly interconnected nonlinear system that needs optimal and accurate control for continuous operation. Large power transfer through long transmission line between different electrical areas, stressed system and adverse interaction between local controllers, may give rise to slow frequency inter-area oscillations. The inter-area modes may not be visible from local measurement...

The efficiency of mineral processing operations, i.e. concentrating raw ores for metal extraction, impacts significantly on the operating cost of the final metal products. First, valuable minerals are liberated from the ore matrix in a comminution circuit, comprised of grinding (e.g., ball mills) and classification (e.g. hydrocyclones) units. The liberated minerals are subsequently concentrated...

One of the biggest hurdles to developing neurocontrollers is the difficulty in establishing good training data for the neural network. We propose a hybrid approach to the development of neurocontrollers that employs both evolutionary computation (EC) and neural networks (NN). EC is used to discover appropriate control actions for specific plant states. The survivors of the evolutionary process ...

The roles of plant models in adaptive critic methods for approximate dynamic programming are considered, with primary focus given to the DHP methodology. In place of complete system identification, partial, approximate, and qualitative models of plant dynamics are considered. Such models are found to be sufficient for successful controller design. As classification is in general easier than reg...

A variety of alternate training strategies for implementing the Dual Heuristic Programming (DHP) method of approximate dynamic programming in the neuro-control context are explored. The DHP method of controller training has been successfully demonstrated by a number of authors on a variety of control problems in recent years, but no unified view of the implementation details of the method has y...