307 research outputs found
A survey of fuzzy control for stabilized platforms
This paper focusses on the application of fuzzy control techniques (fuzzy
type-1 and type-2) and their hybrid forms (Hybrid adaptive fuzzy controller and
fuzzy-PID controller) in the area of stabilized platforms. It represents an
attempt to cover the basic principles and concepts of fuzzy control in
stabilization and position control, with an outline of a number of recent
applications used in advanced control of stabilized platform. Overall, in this
survey we will make some comparisons with the classical control techniques such
us PID control to demonstrate the advantages and disadvantages of the
application of fuzzy control techniques
Wiener modelling and model predictive control for wastewater applications
The research presented in this paper aims to demonstrate the application of predictive control to an integrated wastewater system with the use of the wiener modeling approach. This allows the controlled process, dissolved oxygen, to be considered to be composed of two parts: the linear dynamics, and a static nonlinearity, thus allowing control other than common approaches such as gain-scheduling, or switching, for series of linear controllers. The paper discusses various approaches to the modelling required for control purposes, and the use of wiener modelling for the specific application of integrated waste water control. This paper demonstrates this application and compares with that of another nonlinear approach, fuzzy gain-scheduled control
Fuzzy control turns 50: 10 years later
In 2015, we celebrate the 50th anniversary of Fuzzy Sets, ten years after the main milestones regarding its applications in fuzzy control in their 40th birthday were reviewed in FSS, see [1]. Ten years is at the same time a long period and short time thinking to the inner dynamics of research. This paper, presented for these 50 years of Fuzzy Sets is taking into account both thoughts. A first part presents a quick recap of the history of fuzzy control: from model-free design, based on human reasoning to quasi-LPV (Linear Parameter Varying) model-based control design via some milestones, and key applications. The second part shows where we arrived and what the improvements are since the milestone of the first 40 years. A last part is devoted to discussion and possible future research topics.Guerra, T.; Sala, A.; Tanaka, K. (2015). Fuzzy control turns 50: 10 years later. Fuzzy Sets and Systems. 281:162-182. doi:10.1016/j.fss.2015.05.005S16218228
On the interpretation and identification of dynamic Takagi-Sugenofuzzy models
Dynamic Takagi-Sugeno fuzzy models are not always easy to interpret, in particular when they are identified from experimental data. It is shown that there exists a close relationship between dynamic Takagi-Sugeno fuzzy models and dynamic linearization when using affine local model structures, which suggests that a solution to the multiobjective identification problem exists. However, it is also shown that the affine local model structure is a highly sensitive parametrization when applied in transient operating regimes. Due to the multiobjective nature of the identification problem studied here, special considerations must be made during model structure selection, experiment design, and identification in order to meet both objectives. Some guidelines for experiment design are suggested and some robust nonlinear identification algorithms are studied. These include constrained and regularized identification and locally weighted identification. Their usefulness in the present context is illustrated by examples
Adaptive Gain and Order Scheduling of Optimal Fractional Order PI{\lambda}D{\mu} Controllers with Radial Basis Function Neural-Network
Gain and order scheduling of fractional order (FO) PI{\lambda}D{\mu}
controllers are studied in this paper considering four different classes of
higher order processes. The mapping between the optimum PID/FOPID controller
parameters and the reduced order process models are done using Radial Basis
Function (RBF) type Artificial Neural Network (ANN). Simulation studies have
been done to show the effectiveness of the RBFNN for online scheduling of such
controllers with random change in set-point and process parameters.Comment: 6 pages, 12 figure
Active Suspension Control of Full-car Systems without Function Approximation
This paper proposes a new control approach for full-car active suspension systems with unknown nonlinearities. The main advantage of this approach is that the uncertainties and nonlinearities in the system can be handled without using any function approximator (e.g., neural networks (NNs), fuzzy logic systems (FLSs)), and the associated online adaptation. Hence, the heavy computational costs and sluggish learning phase to achieve convergence can be remedied. To maintain the transient and steady-state suspension responses, a coordinate suspension error transformation with prescribed performance functions (PPF) is adopted. Then an approximation-free control (AFC) is developed to achieve stabilization of the transformed system so as to retain predefined suspension response. Extreme Value Theorem is used together with Lyapunov theorem to prove the stability and convergence of the closed-loop control system. To validate the proposed method and show its practical applicability, a dynamic simulator is built by using a commercial vehicle software, Carsim, where an E-SUV type vehicle is configured to describe realistic vehicle dynamics. Simulation results reveal that the proposed control can achieve better suspension performance and require less model information compared with some existing approaches
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning
Recent advances in combining deep neural network architectures with
reinforcement learning techniques have shown promising potential results in
solving complex control problems with high dimensional state and action spaces.
Inspired by these successes, in this paper, we build two kinds of reinforcement
learning algorithms: deep policy-gradient and value-function based agents which
can predict the best possible traffic signal for a traffic intersection. At
each time step, these adaptive traffic light control agents receive a snapshot
of the current state of a graphical traffic simulator and produce control
signals. The policy-gradient based agent maps its observation directly to the
control signal, however the value-function based agent first estimates values
for all legal control signals. The agent then selects the optimal control
action with the highest value. Our methods show promising results in a traffic
network simulated in the SUMO traffic simulator, without suffering from
instability issues during the training process
Intelligent methods for complex systems control engineering
This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems.
The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parametersâ tuning).
Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions
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