This work proposes the control of an autonomous vehicle using a Lyapunovbased technique with a LQR-LMI tuning. Using the kinematic model of the vehicle, a non-linear control strategy based on Lyapunov theory is proposed for solving the control problem of autonomous guidance. To optimally adjust the parameters of the Lyapunov controller, the closed loop system is reformulated in a linear parameter varying (LPV) form. Then, an optimization algorithm that solves the LQR-LMI problem is used to determine the controller parameters. Furthermore, the tuning process is complemented by adding a pole placement constraint that guarantees that the maximum achievable performance of the kinematic loop could be achieved by the dynamic loop. The obtained controller jointly with a trajectory generation module are in charge of the autonomous vehicle guidance. Finally, the paper illustrates the performance of the autonomous guidance system in a virtual reality environment (SYNTHIA) and in a real scenario achieving the proposed goal: to move autonomously from a starting point to a final point in a comfortable way.
This paper presents the modeling and robust low-level control design of a redundant mobile robot with four omnidirectional wheels, the iSense Robotic (iSRob) platform, that was designed to test safe control algorithms. iSRob is a multivariable nonlinear system subject to parameter uncertainties mainly due to friction forces. A multilinear model is proposed to approximate the behavior of the system, and the parameters of these models are estimated from closed-loop experimental data applying Gauss–Newton techniques. A robust control technique, quantitative feedback theory (QFT), is applied to design a proportional–integral (PI) controller for robust low-level control of the iSRob system, being this the main contribution of the paper. The designed controller is implemented, tested, and compared with a gain-scheduling PI-controller based on pole assignment. The experimental results show that robust stability and control effort margins against system uncertainties are satisfied and demonstrate better performance than the other controllers used for comparison.
This paper presents a methodology to design automatically a QFT (Quantitative Feedback Theory) robust controller for plants with model uncertainty. The method proposed has as objective to find a QFT robust controller that fulfills the control specifications for the whole set of plant models, without including parameter controller restrictions. The methodology is based on a global optimization and has been solved using a global mixed-integer nonlinear programming. The design of an active control for vibration attenuation in optical interferometers is used to validate the technique.
Abstract-This paper presents the low level control of an holonomic robot with four omnidirectional wheels. A robust control technique named Quantitative Feedback Theory (QFT), based on an uncertain linear model has been selected to design the PID speed controllers for the four-wheeled robot. A piecewise model has been estimated by means of the least squares estimation approach based on experimental results of the robot in closed loop. In particular, the control is designed using this piecewise model. The performances of the proposed approach are analyzed in real time domain.
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