The machining quality on computerized numerically controlled (CNC) machine tools is sensitive to the machining parameters. With modern machine tools, an operator still manually adjusts controlling parameters, such as feed rate and cutting speed. The adjusted values mainly depend on operator experience and knowledge. Standard machining catalogues and commercial cutting condition prediction softw...

in this paper an adaptive pid controller for wind energy conversion systems (wecs) has been developed. theadaptation technique applied to this controller is based on reinforcement learning (rl) theory. nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...

economists were interested in economic stabilization policies as early as the 1930’s but the formal applications of stability theory from the classical control theory to economic analysis appeared in the early 1950’s when a number of control engineers actively collaborated with economists on economic stability and feedback mechanisms. the theory of optimal control resulting from the contributio...

Heart chaotic system and the ability of particle swarm optimization (PSO) method motivated us to benefit the method of chaotic particle swarm optimization (CPSO) to synchronize the heart three-oscillator model. It can be a suitable algorithm for strengthening the controller in presence of unknown parameters. In this paper we apply adaptive control (AC) on heart delay model, also examine the sys...

model predictive controller is widely used in industrial plants. uncertainty is one of the critical issues in real systems. in this paper, the direct adaptive simplified model predictive control (smpc) is proposed for unknown or time varying plants with uncertainties. by estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly ca...

An Online Kernel Learning based Adaptive Control (OKL-AC) framework for discrete-time affine nonlinear systems is presented in this paper. A sparsity strategy is proposed to control the complexity of OKL identification model, meanwhile to make a trade-off between the demanded tracking precision and the complexity of the control law. The forward increasing and backward decreasing learning stages...

The decoupled control of torque and flux has made field-oriented control an attractive choice for high performance induction motor drives. However, changes in the speed tracking trajectory and external disturbances make it difficult to achieve an acceptable closed-loop tracking performance, especially when traditional linear controllers are used. This paper addresses this issue by applying dire...

control of well drilling operations poses a challenging issue to be tackled. the loss of well control could lead to the occurrence of blowout as a severe threat, involving the risk of human lives and environmental and economic consequences. conventional proportional-integral (pi) controller is a common practice in the well control operation. the small existing margin between pore pressure and f...

Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations and storage requirements in solving opt...