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A special form of a predictive controller is presented in this paper. Based on previous authors' work, a piecewise-linear neural model of nonlinear plant to be controlled is adopted to local linearization. The linearized model is then used for control action evaluation using a predictive controller. Although the linearization using piecewise-linear neural network is simple and efficient, it provides...
As well-known, model predictive control is closely related to optimal control. This paper studies relationships between them and provides a unified framework for optimality analysis of model predictive controllers (MPC). The optimality is evaluated by comparing total performance of MPC with finite and infinite horizon optimal cost. Based on relaxed value iteration method, upper and lower bounds of...
Rigid link manipulators have attracted more and more attention from robot control theorists and robot users because of its various potential advantages. However, their nonlinear dynamics present a challenging control problem, since traditional linear control approaches do not easily apply. For a while, the difficulty was mitigated by the fact that manipulators were highly geared, thereby strongly...
This paper deals with the robust predictive control of nonlinear systems. The behavior of the nonlinear system is described by an uncertainty Feedforward neural networks model, i.e. each output layer's parameter is uncertain. The control problem is formulated as a minimax optimization one which is a non convex problem. The performances of the proposed controller are illustrated and compared to a classical...
This paper presents a new tuning strategy for Generalized Predictive Controllers (GPC) based on Analysis of Variance (ANOVA). This strategy is derived for Second Order plus Dead Time (SOPDT) models of an industrial plant. Moreover, SOPDT modeling allows oscillating modes to be included in the model dynamics. The tuning strategy employs a simple expression for the tuning parameter as a function of...
Parameter governors are add-on control schemes that adjust parameters (such as gains or offsets) in the nominal control laws so that to avoid violation of pointwise-in-time state and control constraints and to improve the overall system performance. As compared to more general model predictive controllers, parameter governors tend to be more conservative but the computational effort needed to implement...
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