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Practical control problems are always subject to plant state and/or input constraints, which make designing an effective controller a challenging task. This paper introduces a novel virtual control approach to handling the presence of hard constraints in control systems by utilizing virtual mechanisms in the form of nonlinear springs and dampers. The augmented virtual mechanisms are to assist in better shaping the closed‐loop responses, especially when operating near the constrained boundary. A linear quadratic regulator based model predictive control method is utilized to develop stabilizing controllers that not only achieve desired system performance, but also meet the imposed hard constraints. The basic idea is to dramatically increase control penalty by way of tuning the spring and damper effect when the constrained state/input response is close to its hard constraint. The proposed method is applied to a balancing ball problem to demonstrate its applicability and effectiveness, and the simulation results validate the proposed concept.

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In this article, two novel linear parameter–varying modeling and control techniques are proposed for active flutter suppression of a smart airfoil model. The smart airfoil model is instrumented with a moving mass that can be used to actively control the airfoil pitching and plunging motions. The first linear parameter–varying modeling approach makes use of the moving mass position as a scheduling parameter, and the hard constraint at the boundaries is imposed by proper selection of the parameter-varying function. The second modeling technique utilizes nonlinear springs and dampers, which are added to both ends of the airfoil groove to confine the motion of the moving mass. A state-feedback-based linear parameter–varying gain-scheduling controller with the guaranteed [Formula: see text] performance is proposed by utilizing the dynamics of the moving mass. In this study, both the position of the moving mass and the free-stream airspeed are considered as the scheduling parameters. The numerical simulations demonstrate the effectiveness of the proposed linear parameter–varying control architectures by significantly improving the performance, while increasing the flutter speed and reducing the control effort.

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