156 research outputs found
Integrated fault estimation and accommodation design for discrete-time Takagi-Sugeno fuzzy systems with actuator faults
This paper addresses the problem of integrated robust
fault estimation (FE) and accommodation for discrete-time
Takagi–Sugeno (T–S) fuzzy systems. First, a multiconstrained
reduced-order FE observer (RFEO) is proposed to achieve FE for
discrete-time T–S fuzzy models with actuator faults. Based on the
RFEO, a new fault estimator is constructed. Then, using the information
of online FE, a new approach for fault accommodation
based on fuzzy-dynamic output feedback is designed to compensate
for the effect of faults by stabilizing the closed-loop systems. Moreover,
the RFEO and the dynamic output feedback fault-tolerant
controller are designed separately, such that their design parameters
can be calculated readily. Simulation results are presented to
illustrate our contributions
Control of autonomous multibody vehicles using artificial intelligence
The field of autonomous driving has been evolving rapidly within the last few years and
a lot of research has been dedicated towards the control of autonomous vehicles, especially
car-like ones. Due to the recent successes of artificial intelligence techniques, even
more complex problems can be solved, such as the control of autonomous multibody vehicles.
Multibody vehicles can accomplish transportation tasks in a faster and cheaper way
compared to multiple individual mobile vehicles or robots.
But even for a human, driving a truck-trailer is a challenging task. This is because of the
complex structure of the vehicle and the maneuvers that it has to perform, such as reverse
parking to a loading dock. In addition, the detailed technical solution for an autonomous
truck is challenging and even though many single-domain solutions are available, e.g. for
pathplanning, no holistic framework exists. Also, from the control point of view, designing
such a controller is a high complexity problem, which makes it a widely used benchmark.
In this thesis, a concept for a plurality of tasks is presented. In contrast to most of the existing
literature, a holistic approach is developed which combines many stand-alone systems
to one entire framework. The framework consists of a plurality of modules, such as modeling,
pathplanning, training for neural networks, controlling, jack-knife avoidance, direction
switching, simulation, visualization and testing. There are model-based and model-free
control approaches and the system comprises various pathplanning methods and target
types. It also accounts for noisy sensors and the simulation of whole environments.
To achieve superior performance, several modules had to be developed, redesigned and
interlinked with each other. A pathplanning module with multiple available methods optimizes
the desired position by also providing an efficient implementation for trajectory following.
Classical approaches, such as optimal control (LQR) and model predictive control
(MPC) can safely control a truck with a given model. Machine learning based approaches,
such as deep reinforcement learning, are designed, implemented, trained and tested successfully.
Furthermore, the switching of the driving direction is enabled by continuous
analysis of a cost function to avoid collisions and improve driving behavior.
This thesis introduces a working system of all integrated modules. The system proposed
can complete complex scenarios, including situations with buildings and partial trajectories.
In thousands of simulations, the system using the LQR controller or the reinforcement
learning agent had a success rate of >95 % in steering a truck with one trailer, even with
added noise. For the development of autonomous vehicles, the implementation of AI at
scale is important. This is why a digital twin of the truck-trailer is used to simulate the full
system at a much higher speed than one can collect data in real life.Tesi
Stability design of TS model based fuzzy systems
Author name used in this publication: F. H. F. LeungAuthor name used in this publication: P. K. S. TamVersion of RecordPublishe
Robust H
The paper mainly investigates the H∞ fuzzy control problem for a class of nonlinear discrete-time stochastic systems with Markovian jump and parametric uncertainties. The class of systems is modeled by a state space Takagi-Sugeno (T-S) fuzzy model that has linear nominal parts and norm-bounded parameter uncertainties in the state and output equations. An H∞ control design method is developed by using the Lyapunov function. The decoupling technique makes the Lyapunov matrices and the system matrices separated, which makes the control design feasible. Then, some strict linear matrix inequalities are derived on robust H∞ norm conditions in which both robust stability and H∞ performance are required to be achieved. Finally, a computer-simulated truck-trailer example is given to verify the feasibility and effectiveness of the
proposed design method
Stable and robust fuzzy control for uncertain nonlinear systems based on a grid-point approach
Author name used in this publication: F. H. F. LeungAuthor name used in this publication: P. K. S. TamVersion of RecordPublishe
Fuzzy Model-based Pitch Stabilization and Wing Vibration Suppression of Flexible Wing Aircraft.
This paper presents a fuzzy nonlinear controller to regulate the longitudinal dynamics of an aircraft and suppress the bending and torsional vibrations of its flexible wings. The fuzzy controller utilizes full-state feedback with input constraint. First, the Takagi-Sugeno fuzzy linear model is developed which approximates the coupled aeroelastic aircraft model. Then, based on the fuzzy linear model, a fuzzy controller is developed to utilize a full-state feedback and stabilize the system while it satisfies the control input constraint. Linear matrix inequality (LMI) techniques are employed to solve the fuzzy control problem. Finally, the performance of the proposed controller is demonstrated on the NASA Generic Transport Model (GTM)
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