Aneural-learning fuzzy technique is proposed for T–S fuzzy-model identification ofmodel-free physical systems. Further, an algorithm with a defined modelling index is proposed to integrate and to guarantee that the proposed neural-based optimal fuzzy controller can stabilize physical systems; the modelling index is defined to denote the modelling-error evolution, and to ensure that the training...

Formation control of network of multi-agent systems with heterogeneous nonlinear dynamics is formulated as an optimal tracking problem and a decentralized controller is developed using the framework of ‘adaptive critics’ to solve the optimal control problem. The reference signal is assumed available only in online implementation so its dynamics is unavailable for offline training of the neuroco...

The purpose of this paper is to relax the terminal conditions typically used to ensure stability in model predictive control, thereby enlarging the domain of attraction for a given prediction horizon. Using some recent results, we present novel conditions that employ, as the terminal cost, the finite-horizon cost resulting from a nonlinear controller u = −sat(Kx) and, as the terminal constraint...

We present a memory-bounded approximate algorithm for solving infinite-horizon decentralized partially observable Markov decision processes (DEC-POMDPs). In particular, we improve upon the bounded policy iteration (BPI) approach, which searches for a locally optimal stochastic finite state controller, by accompanying reachability analysis on controller nodes. As a result, the algorithm has diff...

this paper presents a new observer-based control scheme for a class of nonlinear systems. in the proposed method, nonlinear observer and twisting algorithm controller are employed to realize a sensor-less control strategy for complex systems which makes use of non measurable process information instead of installing as many sensors as possible. due to lack of availability of the complex system ...

In this paper, hybrid impedancehime-delay control is used for a robot to successfully achieve contact tasks without changing a control algorithm or controller gains throughout all three modes: free space, contact transition and constrained motion. In order to absorb impact forces and stabilize the system upon collision with a stiff environment, a nonlinear bang-bang impact controller is develop...

This paper presents a systematic approach for the design of temperature controller using genetic algorithms (GAs) for thermal power plant subsystems and investigates the robustness of the designed control law. The proposed approach employs GA search for determination of the optimal PI controller parameters for a previously identified nonlinear de-superheater of a 4X 325 MW thermal power plant. ...

Abstract: Due to the difficulty in solving the Hamilton-Jacobi-Isaacs equation, the nonlinear optimal control approach is not very practical in general. To overcome this problem, Ezal et al. (2000) first solved a linear optimal control problem for the linearized model of a nonlinear system given in the strict-feedback form. Then, using the backstepping procedure, a nonlinear feedback controller...

Predictive fuzzy PID control theory is developed in this paper, which o€ers a new approach for robust control of time-delay systems. The paper describes the functional structure, design principle, and stability analysis of a new predictive fuzzy PID controller. Sucient computer simulations are provided for illustration and veri®cation. First, the structure of the controller is derived from bot...