Learning fuzzy inference systems using an adaptive membership function scheme

An adaptive membership function scheme for general additive fuzzy systems is proposed in this paper. The proposed scheme can adapt a proper membership function for any nonlinear input-output mapping, based upon a minimum number of rules and an initial approximate membership function. This parameter adjustment procedure is performed by computing the error between the actual and the desired decision surface. Using the proposed adaptive scheme for fuzzy system, the number of rules can be minimized. Nonlinear function approximation and truck backer-upper control system are employed to demonstrate the viability of the proposed method

Workshop on Fuzzy Control Systems and Space Station Applications

The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented

Multiobjective control of a vehicle with triple trailers

We consider the backing-up control of a vehicle with triple trailers using a model-based fuzzy-control methodology. First, the vehicle model is represented by a Takagi-Sugeno fuzzy model. Then, we employ the so-called "parallel distributed compensation" design to arrive at a controller that guarantees the stability of the closed-loop system consisted of the fuzzy model and controller. The control-design problem is cast in terms of linear matrix inequalities (LMIs). In addition to stability, the control performance considerations such as decay rate, constraints on input and output, and disturbance rejection are incorporated in the LMI conditions. In application to the vehicle with triple trailers setup, we utilize these LMI conditions to explicitly avoid the saturation of the steering angle and the jackknife phenomenon in the control design. Both simulation and experimental results are presented. Our results demonstrate that the fuzzy controller effectively achieves the backing-up control of the vehicle with triple trailers while avoiding the saturation of the actuator and "jackknife" phenomenon

Sensor Reduction for Backing-Up Control of a Vehicle With Triple Trailers

This paper presents a cost-effective design based on sensor reduction for backing-up control of a vehicle with triple trailers. To realize a cost-effective design, we newly derive two linear-matrix-inequality (LMI) conditions for a discrete Takagi-Sugeno fuzzy system. One is an optimal dynamic output feedback design that guarantees desired control performance. The other is an avoidance of jackknife phenomenon for the use of the optimal dynamic output feedback controller. Our results demonstrate that the proposed LMI-based design effectively achieves the backing-up control of the vehicle with triple trailers while avoiding the jackknife phenomenon. More importantly, we demonstrate that the designed optimal control can achieve the backing-up control without, at least, two potentiometers that were employed to measure the relative angles (of a vehicle with triple trailers) in our previous experiments. Since the relative angles directly relate to the jackknife phenomenon, the successful control results without two potentiometers are very interesting and important from the cost-effective design point of view

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

Model-reference adaptive control based on neurofuzzy networks

Model reference adaptive control (MRAC) is a popular approach to control linear systems, as it is relatively simple to implement. However, the performance of the linear MRAC deteriorates rapidly when the system becomes nonlinear. In this paper, a nonlinear MRAC based on neurofuzzy networks is derived. Neurofuzzy networks are chosen not only because they can approximate nonlinear functions with arbitrary accuracy, but also they are compact in their supports, and the weights of the network can be readily updated on-line. The implementation of the neurofuzzy network-based MRAC is discussed, and the local stability of the system controlled by the proposed controller is established. The performance of the neurofuzzy network-based MRAC is illustrated by examples involving both linear and nonlinear systems. © 2004 IEEE.published_or_final_versio