Control Of Complex Nonlinear Systems Research Articles

MPC using an on-line TS fuzzy learning approach with application to autonomous driving

The control of complex nonlinear systems (such as autonomous vehicles) usually requires models which might be unavailable or inaccurate. In this paper, a novel data-driven Model Predictive Control (MPC) framework is proposed based on a data-driven approach to learn Takagi–Sugeno (TS) fuzzy models for nonlinear systems. To address the data TS modeling, we use the Evolving TS Fuzzy Ellipsoidal Information Granules (TS-EEFIG) approach to obtain a polytopic representation as well as a set of membership functions that allows to use efficient linear control tools to handle complex nonlinear systems. In particular, the formulated approach is applied for the autonomous driving control problem of a racing vehicle. The proposed control uses references provided by an external trajectory planner offering a high driving performance in racing mode. The control-estimation scheme is validated in a simulated racing environment based on a high fidelity vehicle model of a 1/10 scale RC car to show the potential of the proposed approach.

A multi-level fuzzy switching control method based on fuzzy multi-model and its application for PWR core power control

As the characteristics of complex system are usually multivariable, nonlinear, and wide operating range, it is difficult to achieve ideal control of complex nonlinear systems. To solve the problem, a multi-level fuzzy switching control method based on fuzzy multi-model is designed. The PD controller, the fuzzy controller and the fuzzy-PID controller are selected as the sub-controllers, and T-S fuzzy rules are used to realize smooth switching between sub-controllers. The multi-level fuzzy switching controller is designed as the parent controller. In this paper, the local model of PWR core is established by selecting five linear models at power levels of 20% FP, 40% FP, 60% FP, 80% FP and 100% FP, and the fuzzy multi-model of PWR core is established by weighting the five local models with triangular membership function. Then, the multi-level fuzzy switching controller with the core fuzzy multi-model is used for core power control, and the power control simulation of PWR core is carried out. The results show that the multi-level fuzzy switching controller designed based on the core fuzzy multi-model is suitable for PWR core power control.

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

Type-2 Fuzzy Logic Control [Book Review

This book presents the first unified and thorough discussion of Type-2 FLCs and Type-2 fuzzy logic control systems, providing the necessary tools for the design of the control of complex nonlinear systems. The analysis includes 1) the mathematical structure of some Type-2 FLCs, 2) stability of Type-2 fuzzy logic control systems, and 3) robustness of the Type-2 fuzzy logic control systems. This book, the first one entirely on Type-2 FLCs, shows the reader how to design Type-2 fuzzy logic control systems based on a variety of choices for the Type-2 FLC components and also demonstrates how to apply Type-2 fuzzy logic control theory to applications. It is written by five of the leading experts on Type-2 fuzzy sets, systems, and control. This book consists of seven chapters. This book has been written with an educational focus rather than a pure research focus. Each chapter includes worked examples, and most refer to computer programs accessible through the book’s Web site, outlining how to use them at a high level. Therefore, this book is appropriate for classroom use in graduate courses in electrical engineering, computer engineering, and computer science. This book also is a self-contained reference suitable for engineers, researchers, and graduate students who want to gain deep insights about Type-2 FLCs.

Modeling and Control of Complex Nonlinear Systems Based on TS Model

The current theory of nonlinear systems is still not perfect. The modeling and control of nonlinear system problem has always been the difficulty. In a variety of methods of its study, fuzzy system theory because of having the language descriptive way similar to the human mind, can obtain and deal with the qualitative information intelligently. The theory itself also has non-linear characteristics. Therefore the use of fuzzy systems theory to establish the fuzzy model of nonlinear system can well describe the nonlinear characteristics. T-S fuzzy systems, due to the combination of the good performance of the fuzzy system to deal with nonlinear problems with the simple linear expressions, are not only suitable for modeling the nonlinear system, but also use T-S fuzzy model and the linear control theory method to design the controller. So it has been widely used in nonlinear system control problems, and has also greatly developed the T-S fuzzy system theory, appearing a lot of methods of structural and parameter identification. However, this study of T-S fuzzy rules makes us have to face the difference of different ways to select the number of rules as well as online self-adaptability of the number of rules which off-line method lacks when using T-S fuzzy model to deal with nonlinear system modeling and control problem. In view of this, this paper researches on modeling and controlling of complex nonlinear systems based on TS model from different perspectives.

Interval type-2 fuzzy inverse controller design in nonlinear IMC structure

In the recent years it has been demonstrated that type-2 fuzzy logic systems are more effective in modeling and control of complex nonlinear systems compared to type-1 fuzzy logic systems. An inverse controller based on type-2 fuzzy model can be proposed since inverse model controllers provide an efficient way to control nonlinear processes. Even though various fuzzy inversion methods have been devised for type-1 fuzzy logic systems up to now, there does not exist any method for type-2 fuzzy logic systems. In this study, a systematic method has been proposed to form the inverse of the interval type-2 Takagi–Sugeno fuzzy model based on a pure analytical method. The calculation of inverse model is done based on simple manipulations of the antecedent and consequence parts of the fuzzy model. Moreover, the type-2 fuzzy model and its inverse as the primary controller are embedded into a nonlinear internal model control structure to provide an effective and robust control performance. Finally, the proposed control scheme has been implemented on an experimental pH neutralization process where the beneficial sides are shown clearly.

Applications of hybrid reachability analysis to robotic aerial vehicles

The control of complex non-linear systems can be aided by modeling each system as a collection of simplified hybrid modes, with each mode representing a particular operating regime defined by the system dynamics or by a region of the state space in which the system operates. Guarantees on the safety and performance of such hybrid systems can still be challenging to generate, however. Reachability analysis using a dynamic game formulation with Hamilton—Jacobi methods provides a useful way to generate these types of guarantees, and the technique is flexible enough to analyze a wide variety of systems. This paper presents two applications of reachable sets, both focused on guaranteeing the safety and performance of robotic aerial vehicles. In the first example, reachable sets are used to design and implement a backflip maneuver for a quadrotor helicopter. In the second, reachability analysis is used to design a decentralized collision avoidance algorithm for multiple quadrotors. The theory for both examples is explained, and successful experimental results are presented from flight tests on the STARMAC quadrotor helicopter platform.

Adaptive observer-based non-linear stochastic system control with sliding mode schemes

In this paper, an adaptive sliding mode observer is designed to reconstruct the states of non-linear stochastic systems with uncertainties from the measurable system output and the reconstructed states are employed to construct a sliding mode controller for the stabilization control of complex non-linear systems. It takes the advantages of the sliding mode schemes to design both the observer and the controller. The convergence of the observer and the globally asymptotical stability of the controller are analysed in terms of stochastic Lyapunov stability, and the effectiveness of the control strategy is verified with numerical simulation studies.

Control of chaotic dynamical systems using support vector machines

This work provides a methodology for the control of complex non-linear systems by compensating for the non-linear part using support vector machines (SVM) and subsequently developing simple linear feedback law for control. The method tested for the benchmark Rössler and Lorenz chaotic oscillators shows excellent performance.

Fuzzy Supervisory Control

Multimodel control methodology can be a good alternative in control of complex nonlinear systems. In this case, the selection of good controllers and the bump less transfer between different controllers are important. This paper presents a supervisory control based on the Takagi-Sugeno fuzzy model used as a two level controller. A learning phase is added to change the T-S fuzzy model parameters, shapes of membership functions of reference fuzzy sets, to achieve a better performance of the control system.

Universal Approach to Neural Process Control Illustrated on A Biomass Growth Model

The article presents a control scheme, based on use of neural networks, which can be applied to a control of complex nonlinear systems. The emphasis is on producing methodology and appropriate software which could later be used to construct control systems in a standard way. As usual when neural networks are used many experiments are required before desired results are reached. Therefore, a NeuroControl Workbench has been constructed which allows one to construct numerous experiments with a minimum of effort This article presents some results of control experiments with a bioreactor simulation.