The design of smooth switching control with application to V/STOL aircraft dynamics under input and output constraints

Vehicle-to-vehicle communications can be unreliable because of interference and information congestion, which leads to the dynamic information flow topology (IFT) in a platoon of connected and autonomous vehicles. Some existing studies adaptively switch the controller of cooperative adaptive cruise control (CACC) to optimize string stability when IFT varies. However, the difference of transient response between controllers can induce uncomfortable jerks at switching instances, significantly affecting riding comfort and jeopardizing vehicle powertrain. To improve riding comfort while maintaining string stability, the authors introduce a smooth-switching control-based CACC scheme with IFT optimization (CACC-SOIFT) by implementing a bi-layer optimization model and a Kalman predictor. The first optimization layer balances the probability of communication failure and control performance optimally, generating a robust IFT to reduce controller switching. The second optimization layer adjusts the controller parameters to minimize tracking error and the undesired jerk. Further, a Kalman predictor is applied to predict vehicle acceleration if communication failures occur. It is also used to estimate the states of preceding vehicles to suppress the measurement noise and the acceleration disturbance. The effectiveness of the proposed CACC-SOIFT is validated through numerical experiments based on NGSIM field data. Results indicate that the CACC-SOIFT framework can guarantee string stability and riding comfort in the environment of dynamic IFT.

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Operator-Based Control System Analysis and Design for Nonlinear System with Input and Output Constraints

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2018.p0950 ◽

2018 ◽

Vol 30 (6) ◽

pp. 950-957

Author(s):

Shuhui Bi ◽

Lei Wang ◽

Shengjun Wen ◽

Liyao Ma ◽

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...

Keyword(s):

Control System ◽

Nonlinear System ◽

System Analysis ◽

Sufficient Conditions ◽

Input Constraint ◽

Analysis And Design ◽

Input And Output ◽

Output Constraints ◽

The Stability ◽

Output Constraint

Smart material-based actuators and sensors have been widely used in practice owing to their various advantages. However, in the working process of these actuators and sensors, their output responses always deduce non-smooth nonlinear constraints. The constraint resulting from the actuator is called the input constraint and the constraint caused by the sensor is called the output constraint. These input and output constraints may induce inaccuracies and oscillations, severely degrading system performance. Therefore, the input and output constraints brought about by actuators and sensors should be considered in control system design. In this paper, system analysis for a nonlinear system with input and output constraints will be considered. The effect from the input constraint to the internal signal in the control system will be discussed. Moreover, the influence of the output constraint on the whole system will be studied. Further, the sufficient conditions for maintaining the stability of the system are obtained. Then, by using the robust right coprime factorization approach, an operator-based internal model like control structure is proposed for mitigating the input and output constraints. Finally, the effectiveness of the proposed design scheme will be confirmed through numerical simulation.

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Synthesise of MPC controller for uncertain systems subject to input and output constraints: application to anthropomorphic robot arm

International Journal of Automation and Control ◽

10.1504/ijaac.2020.10025258 ◽

2020 ◽

Vol 14 (1) ◽

pp. 80

Author(s):

Elyes Maherzi ◽

Mongi Besbes ◽

Imen Dakhli

Keyword(s):

Uncertain Systems ◽

Robot Arm ◽

Input And Output ◽

Output Constraints

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Nonlinear Model Predictive Control of a Laboratory Gas Turbine Installation

Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education ◽

10.1115/98-gt-100 ◽

1998 ◽

Cited By ~ 1

Author(s):

B. G. Vroemen ◽

H. A. van Essen ◽

A. A. van Steenhoven ◽

J. J. Kok

Keyword(s):

Model Predictive Control ◽

Gas Turbine ◽

Predictive Control ◽

Nonlinear Model ◽

Nonlinear Prediction ◽

Input And Output ◽

Output Constraints ◽

Controller Performance ◽

Standard Linear ◽

Turbine Installation

The feasibility of Model Predictive Control (MPC) applied to a laboratory gas turbine installation is investigated. MPC explicitly incorporates (input- and output-) constraints in its optimizations, which explains the choice for this computationally demanding control strategy. Strong nonlinearities, displayed by the gas turbine installation, cannot always be handled adequately by standard linear MPC. Therefore, we resort to nonlinear methods, based on successive linearization and nonlinear prediction as well as the combination of these. We implement these methods, using a nonlinear model of the installation, and compare them to linear MPC. It is shown that controller performance can be improved, without increasing controller execution-time excessively.

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