Although constraint satisfaction approaches have achieved fruitful results,system states may lose their smoothness and there may be undesired chattering of control inputs due to switching ***,it remains a challenge wh...
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Although constraint satisfaction approaches have achieved fruitful results,system states may lose their smoothness and there may be undesired chattering of control inputs due to switching ***,it remains a challenge when there are additional constraints on control torques of robotic *** this article,we propose a novel high-order control barrier function(HoCBF)-based safety control method for robotic systems subject to input-output constraints,which can maintain the desired smoothness of system states and reduce undesired chattering vibration in the control *** our design,augmented dynamics are introduced into the HoCBF by constructing its output as the control input of the robotic system,so that the constraint satisfaction is facilitated by HoCBFs and the smoothness of system states is maintained by the augmented *** proposed scheme leads to the quadratic program(QP),which is more user-friendly in implementation since the constraint satisfaction control design is implemented as an add-on to an existing tracking control *** proposed closed-loop control system not only achieves the requirements of real-time capability,stability,safety and compliance,but also reduces undesired chattering of control ***,the effectiveness of the proposed control scheme is verified by simulations and experiments on robotic manipulators.
This study investigates the issue of stability analysis and controller synthesis for an electric vehicle based on the Takagi-Sugeno fuzzy control approach. The proposed adaptive fuzzy integral sliding mode controller ...
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This study investigates the issue of stability analysis and controller synthesis for an electric vehicle based on the Takagi-Sugeno fuzzy control approach. The proposed adaptive fuzzy integral sliding mode controller ensures that the electric vehicle system performs satisfactorily in the presence of uncertainties and exogenous disturbances, comprising low computational load, small control input amplitude, and reduced energy consumption. For efficient performance of the electric vehicle, the battery voltage (control input) and the vehicle's speed (system output) are considered restricted. Simulations of an electric vehicle powered by a brushed direct current motor are utilized to evaluate the efficacy of the proposed controller. Ultimately, the design procedure validates the robust performance and fast stabilization of the electric vehicle speed in the presence of exogenous disturbances and parametric uncertainties.
This article studies the problem of deformation reduction and attitude tracking for a rotated and extended flexible crane arm with input backlash-saturation and output asymmetrical constraint. By employing Halmilton&#...
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This article studies the problem of deformation reduction and attitude tracking for a rotated and extended flexible crane arm with input backlash-saturation and output asymmetrical constraint. By employing Halmilton's principle, the arm system model is formulated by a set of partial and ordinary differential equations (ODEs). Given the modeling inaccuracy, a radial neural network (RNN) is used to approximate system parameters. To better design the controllers, the backstepping technique is applied to the control design. For input nonlinearities with backlash and saturation, we reversely transform them as an asymmetric saturation constraint via a virtual input. A barrier Lyapunov function (BLF) containing logarithmic terms is constructed to guarantee the asymmetric outputconstraints and the uniformly ultimate boundedness and stability of the arm system are proved. Finally, to testify the effectiveness of the proposed controllers, numerical simulations are carried out, and responding simulation diagrams are displayed.
In the behavioral framework for continuous-time linear scalar systems, simple sufficient conditions for the solution of the minimum-time rest-to-rest feedforward constrained control problem are provided. The investiga...
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In the behavioral framework for continuous-time linear scalar systems, simple sufficient conditions for the solution of the minimum-time rest-to-rest feedforward constrained control problem are provided. The investigation of the time-optimal input-output pair reveals that the input or the output saturates on the assigned constraints at all times except for a set of zero measure. The resulting optimal input is composed of sequences of bang-bang functions and linear combinations of the modes associated to the zero dynamics. This signal behavior constitutes a generalized bang-bang control that can be fruitfully exploited for feedforward constrained regulation. Using discretization, an arbitrarily good approximation of the optimal generalized bang-bang control is found by solving a sequence of linear programming problems. Numerical examples are included. (C) 2009 Elsevier Ltd. All rights reserved.
In this paper stability of one-step ahead predictive controllers based on non-linear models is established. It is shown that, under conditions which can be fulfilled by most industrial plants, the closed-loop system i...
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In this paper stability of one-step ahead predictive controllers based on non-linear models is established. It is shown that, under conditions which can be fulfilled by most industrial plants, the closed-loop system is robustly stable in the presence of plant uncertainties and input-output constraints. There is no requirement that the plant should be open-loop stable and the analysis is valid for general forms of non-linear system representation including the case out when the problem is constraint-free. The effectiveness of controllers designed according to the algorithm analyzed in this paper is demonstrated on a recognized benchmark problem and on a simulation of a continuous-stirred tank reactor (CSTR). In both examples a radial basis function neural network is employed as the non-linear system model. (C) 2000 Elsevier Science Ltd. All rights reserved.
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