An eecient numerical solution scheme entitled adaptive diierential dynamic programming is developed in this paper for multiobjective optimal control problems with a general separable structure. For a multiobjective control problem with a general separable structure, the \optimal" weighting coeecients for various performance indices are time-varying as the system evolves along any noninferior tr...

an adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. another key feature of this structure is the fact that, it does not need model of the system. in this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  online training of neurolinearizer is compared to model predictive recurrent training...

this paper introduces an indirect adaptive fuzzy sliding mode controller as a power system stabilizer for damping local and inter-area modes of oscillations of multi-machine power systems. this controller is designed based on the combination of sliding mode controller and the fuzzy logic systems. the fuzzy systems are used to approximate the unknown functions of power system model. generator sp...

optimal balancing is a new approach based on optimal control a which modes, controls and the amount of equilibrium scale are calculated simultaneously. this approach has a significant effect on increasing performance of industrial robots which causes maximum decreasing of energy consumption. despite many applications of robotics such as welding and gluing, the path is predefined , but other unk...

We describe the results of analytic calculations and computer simulations of adaptive predictors (predictive agents) responding to an evolving chaotic environment and to one another. Our simulations are designed to quantify adaptation and to explore co-adaptation for a simple calculable model of a complex adaptive system. We first consider the ability of a single agent, exposed to a chaotic env...

this paper concerns a study on the optimal control for nonlinear systems. an appropriate alternative in order to alleviate the nonlinearity of a system is the exact linearization approach. in this fashion, the nonlinear system has been linearized using input-output feedback linearization (iofl). then, by utilizing the well developed optimal control theory of linear systems, the compensated nonl...

It is the purpose of this study to develop an economical Robust design methodology for micro-scale secondary flow control in compact inlet diffusers. To illustrate the potential of economical Robust Design methodology, two different mission strategies were considered for the subject inlet, namely (1) Maximum Performance, and (2) Maximum HCF Life Expectancy. The Maximum Performance mission maxim...

magnetorheological (mr) dampers have the advantage of being tuned by low voltages. this has attracted many researchers to develop semi-active control of structures in theory and practice. most of the control strategies first obtain the desired forces of dampers without taking their dynamics into consideration and then determine the input voltages according to those forces. as a result, these st...

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

in recent years, researches on reinforcement learning (rl) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. neural network reinforcement learning (nnrl) is among the most popular algorithms in the rl framework. the advantage of using neural networks enables the rl to search for optimal policies more efficiently in several real-life applicat...