Nonlinear Receding-Horizon Control of Rigid Link Robot Manipulators

The approximate nonlinear receding-horizon control law is used to treat the trajectory tracking control problem of rigid link robot manipulators. The derived nonlinear predictive law uses a quadratic performance index of the predicted tracking error and the predicted control effort. A key feature of this control law is that, for their implementation, there is no need to perform an online optimization, and asymptotic tracking of smooth reference trajectories is guaranteed. It is shown that this controller achieves the positions tracking objectives via link position measurements. The stability convergence of the output tracking error to the origin is proved. To enhance the robustness of the closed loop system with respect to payload uncertainties and viscous friction, an integral action is introduced in the loop. A nonlinear observer is used to estimate velocity. Simulation results for a two-link rigid robot are performed to validate the performance of the proposed controller. Keywords: receding-horizon control, nonlinear observer, robot manipulators, integral action, robustness

Perception of saturation in natural objects

The distribution of colors across a surface depends on the interaction between its surface properties, its shape, and the lighting environment. Shading, chroma, and lightness are positively correlated: points on the object that have high luminance also have high chroma. Saturation, typically defined as the ratio of chroma to lightness, is therefore relatively constant across an object. Here we explored to what extent this relationship affects perceived saturation of an object. Using images of hyperspectral fruit and rendered matte objects, we manipulated the lightness–chroma correlation (positive or negative) and asked observers which of two objects appeared more saturated. Despite the negative-correlation stimulus having greater mean and maximum chroma, lightness, and saturation than the positive, observers overwhelmingly chose the positive as more saturated. This suggests that simple colorimetric statistics do not accurately represent perceived saturation of objects—observers likely base their judgments on interpretations about the cause of the color distribution

Nonlinear Predictive Control With End Point Constraints

The optimal nonlinear predictive control structure with end point constraints is presented, which provides asymptotic tracking of smooth reference trajectories. The controller is based on a finite horizon continuous time minimization of nonlinear predicted tracking errors. A key feature of the control law is that its implementation does not need to perform an online optimization, and asymptotic tracking of smooth reference signal is guaranteed. The proposed control scheme is applied to planning motions problem of a mobile robot. Simulations results are performed to validate the tracking performance of the proposed controller.

Robust nonlinear receding-horizon control of Euler-Lagrange systems

In this note, we present a nonlinear receding-horizon controller for Euler-Lagrange systems. The derived nonlinear predictive law uses a quadratic objective function of the predicted tracking error and the predicted control signal. It is shown that this controller achieves positions tracking objectives via positions measurements. The stability convergence of the output tracking error to the origin is proved by the well-known Lyapunov method. To enhance the robustness of the closed loop system with respect to parameters variations and uncertainties, an integral action is introduced in the loop. Simulation results for a two-link rigid robot are performed to validate the performance of the proposed controlle

Nonlinear receding horizon control of production inventory systems with deteriorating items

This paper is concerned with the receding horizon control of the production rate of a deteriorating production system with a nonlinear inventory-level-dependent demand. Both continuous and periodic review policies are discussed and numerical illustrations are provided.

Finite horizon nonlinear predictive control by Taylor approximation

In control system, the practical interest is driven by the fact that today’s processes need to be operated under tighter performance specifications. Often these demands can only be met when process nonlinearities are explicitly considered in the controller. Nonlinear predictive control, the extension of well established linear predictive control to the nonlinear systems, appears to be a well suited approach for this kind of problems. In this paper the optimal nonlinear predictive control structure is presented, which provides asymptotic tracking of smooth reference trajectories. The controller is based on a finite horizon continuous time minimization of nonlinear predicted tracking errors. A key feature of the control law is that its implementation does not need to perform an on line optimization, and asymptotic tracking of smooth reference signal is guaranteed. The proposed control scheme is applied to the trajectory tracking problem of a rigid link manipulator. Simulations results are performed to validate the tracking performance and robustness of the proposed controller

Receding horizon control of a hybrid production system with deteriorating items

In this paper, a receding horizon control strategy is applied to a dynamic hybrid production system with deteriorating items. Given the current inventory level, we determine the optimal production rates to be implemented at the beginning of each of the following periods over the control horizon. The effectiveness of this approach was in the use of future information of the inventory target level and the desired production rate, which are available. Both the continuous and periodic review policies are investigated. The performances of the proposed control algorithms are illustrated by simulation

Predictive control of periodic-review production inventory systems with deteriorating items

In this paper a predictive control strategy is applied to a periodic-review dynamic inventory system with deteriorating items. Given the current inventory level, we determine the optimal production rates to be implemented at the beginning of each of the following periods over the control horizon. The effectiveness of this approach is the use of future information of the inventory target level and the desired production rate, which are available, along the fixed horizon. The deterioration coefficient may be known or unknown and both cases are considered. In the case where it is unknown, the self-tuning predictive control is applied. The proposed control algorithms are illustrated by simulations