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To devise a new technique to synthesise optimal feedback control law for non‐linear dynamic systems through fuzzy logic.

The proposed methodology utilizes the open‐loop optimal control solutions (OCSs) of the non‐linear systems for the training of the fuzzy system in the process of developing closed‐loop fuzzy logic guidance (FLG). This is achieved through defining a set of non‐dimensionalised variables related to the system states.

FLG is capable of generating closed‐loop control law for the non‐linear problem investigated. Since the proposed fuzzy structure is independent of the system equations, the approach is potentially applicable to other non‐linear system. Introduction of the non‐dimensional variables in place of the regular states has effectively increased the fuzzy training performance and greatly reduced the number of fuzzy rule bases required to produce accurate solutions for other untrained scenarios.

There exist many complex non‐linear problems in guidance and control of aerospace vehicles. Determination of optimal control laws for such systems is usually a difficult task even in an open‐loop form and in a noise‐free off‐line environment. On the other hand, closed‐loop OCSs are highly desirable for their robust characteristics in actual operating environments, so are more suitable for online applications, but can seldom be realized for complex non‐linear systems. Even though a few researchers have worked in the area of non‐linear optimal control and application of fuzzy logic on such systems, non‐have dealt with closed‐loop optimal fuzzy controllers. This research proposes a new strategy for the determination of optimal feedback control laws for non‐linear systems, which can be utilized in many spacecraft mission applications.

The purpose of this paper is to propose a new non-parametric phase I control chart for the problem of non-linear profile outlier detection.

The proposed non-parametric method is based on a modified Hausdorff distance, which does not require a restrictive assumption on the form of profiles. By obtaining the distance between each profile and the baseline profile, the authors introduced an iterative optimization clustering algorithm to identify outliers by clustering distances.

The simulation results show that the proposed method can distinguish outliers for structural changes of non-linear profiles. The authors also present a real industrial case example to highlight how practitioners can implement and make use of the proposed control chart in outlier detection applications, and it achieves higher accuracy in the outlier detection of complex profiles.

The research results of this paper can be applied to any manufacturing or service system whose quality characteristics are characterized by non-linear profiles. This new approach provides quality practitioners a better decision-making tool for non-linear profile outlier detection.

Due to the complexity of real-world applications, the non-linear profiles monitoring problem is yet to be addressed. However, the related research still remains rare. And the authors’ proposed non-linear profile control chart, which does not require a restrictive assumption on the form of profiles, shows its applicability and superiority in simulation study and real-world case.

The paper's purpose is to initiate an effort that will result in a systematic approach for design of control systems for multivariable, nonlinear, and unstable space robots.

The design approach is based on multivariable describing function (DF) models of the space robot coupled with the use of factorization technique. The design approach is to obtain the multivariable DF models followed by application of a previously developed factorization‐based controller design formula. Finally, the design must be verified by a non‐linear simulation to make sure that approximations made during design are valid.

It is found that the DF approach may successfully be applied in order to control nonlinear, multivariable, and unstable systems such as space robots.

At present, the approach is verified to be applicable to rigid space robots.

The major outcome of this research is that complicated controllers of a class of space robots may be replaced by simpler controllers, taking into account the amplitude dependency features of the space robot; this amplitude dependency is the most important characteristic of a non‐linear system.

This is the first paper in the area of multivariable and unstable space robot controller design that is based on the application of the DF technique. In fact this is the first work in the area of general unstable non‐linear control system design that is based on a DF technique.

To develop a new predictive control scheme based on neural networks for linear and non‐linear dynamical systems.

The approach relies on three different multilayer neural networks using input‐output information with delays. One NN is used to identify the process under control, the other is used to predict the future values of the control error and finally the third one is used to compute the magnitude of the control input to be applied to the plant.

This scheme has been tested by controlling discrete‐time SISO and MIMO processes already known in the control literature and the results have been compared with other control approaches with no predictive effects. Transient behavior of the new algorithm, as well as the steady state one, are observed and analyzed in each case studied. Also, online and offline neural network training are compared for the proposed scheme.

The theoretical proof of stability of the proposed scheme still remains to be studied. Conditions under which non‐linear plants together with the proposed controller present a stable behavior have to be derived.

The main advantage of the proposed method is that the predictive effect allows to suitable control complex non‐linear process, eliminating oscillations during the transient response. This will be useful for control engineers to control complex industrial plants.

This general approach is based on predicting the future control errors through a predictive neural network, taking advantage of the NN characteristics to approximate any kind of relationship. The advantage of this predictive scheme is that the knowledge of the future reference values is not needed, since the information used to train the predictive NN is based on present and past values of the control error. Since the plant parameters are unknown, the identification NN is used to back‐propagate the control error from the output of the plant to the output of the controller. The weights of the controller NN are adjusted so that the present and future values of the control error are minimized.

The purpose of this paper is to provide the high‐precision robust control method for plants given by a high order of differential equations. This method is useful for linear and non‐linear plants. Considering the problem of minimization of energy consumed in the world is very important and very actual.

For theoretical solving of the problem, the functional analysis and methods of the Banach spaces H₂ and H_∞ are used. Next the conditions for controllability with ε‐accuracy are given. For the non‐linear plants additionally two methods are used – method based on van der Schaft inequality and harmonically linearization.

Provides state feedback control systems with sufficiently large gain (called Tytus feedback). Such systems can perform a high‐degree accuracy (called there ε‐accuracy).

The considerations have many practical applications. For example, solving the problem of a high‐precision robust control for a ship track‐keeping and designing of a robust controller for a non‐linear two‐benchmark problem.

This is an original theoretical method of obtaining a high‐precision performance for feedback control systems. System presented in the paper enables controlling with ε‐accuracy the stable or unstable plants P described by high‐degree differential equations. Paper regards a robust control of stable as well as unstable plants with uncertainty.

This paper aims to devise a robust controller for the non-linear aircraft model using output feedback control topology in the presence of uncertain aerodynamic parameters.

Feedback linearization-based state feedback (SFB) controller is considered along with a robust outer loop control which is designed using Lyapunov’s second method. A high-gain observer (HGO) in accordance with the separation principle is used to implement the output feedback (OFB) control scheme. The robustness of the controller and observer is assessed by introducing uncertain aerodynamics coefficients in the dynamic model. The proposed scheme is validated using MATLAB/SIMULINK.

The efficacy of the proposed scheme is authenticated with the simulation results which show that HGO-based OFB control achieves the SFB control performance for a small value of the high-gain parameter in the presence of uncertain aerodynamic parameters.

A HGO for the non-linear model of aircraft with uncertain parameters is a novel contribution which could be further used for the unmanned aerial vehicles autopilot, flight trajectory tracking and path following.

A linear‐model‐based adaptive control system is developed. Alternative identification techniques are combined with a suboptimal controller. Iterative and recursive algorithms are applied to produce minimum norm estimates of multivariable models, adequate over a range of plant operation. Parameter estimates are used to update the stage‐by‐stage suboptimal control algorithm. The techniques are applied to control a non‐linear chemical reactor model.

To show how the combined Adomian/Alienor methods for solving adaptive control problems can successfully be applied to chemotherapy.

Problem formulation is first developed and combined mathematical methods (Adomian/Alienor) are used for the solution of non‐linear differential equations/systems with unknown parameters and without discretization or linearization. The approach is applied to biological systems and in particular the drug/tumour two compartment model is addressed.

A general abstract framework for the identification and the control of a non‐linear evolution system has been developed. It was found that it is possible to identify and control a system using a powerful technique based on a combination of the Adomian/Alienor methods. This produced a methodology which showed its superiority over the traditional methods in that as a result of their implementation we can predict and optimize the individual dosage in the described application.

The combined techniques proved to be successful for the optimization of drug administration where account is taken of effectiveness, usefulness and safety. Further research collaboration between multidisciplinary scientists and practitioners directed towards more insight into drug/cancerous cells behaviour is required.

An alternative to other classical techniques to solve control/identification problems has been produced.

New combined technique given which is superior to traditional ones for certain therapeutic cases.

Improvement of the dynamic properties of DC drive in the field weakening range was the aim of investigation. The non‐linear model of the drive system was applied. In the paper results of the comparative analysis of two emf control structures are presented. The classic emf control structure with subordinated excitation current control loop was compared with this one consisting of a non‐linear compensation block. For both control structures different kinds of the parameter designing for the emf and excitation controllers are considered. Verification of the theoretical assumptions and synthesis methods of the investigated control structures are made by simulation tests using the PSpice language.

To provide an approach to vibration reduction of flexible spacecraft which operates in the presence of various disturbances, model uncertainty and control input non‐linearities during attitude control for spacecraft designers, which can help them analyze and design the attitude control system.

The new approach integrates the technique of active vibration suppression and the method of variable structure control. The design process is twofold: first design of the active vibration controller by using piezoelectric materials to add damping to the structures in certain critical modes in the inner feedback loop, and then a second feedback loop designed using the variable structure output feedback control (VSOFC) to slew the spacecraft and satisfy the pointing requirements.

Numerical simulations for the flexible spacecraft show that the precise attitude control and vibration suppression can be accomplished using the derived vibration attenuator and attitude control controller.

Studies on how to control the flywheel (motor) under the action of the friction are left for future work.

An effective method is proposed for the spacecraft engineers planning to design attitude control system for actively suppressing the vibration and at the same time quickly and precisely responding to the attitude control command.

This paper fulfills a useful source of theoretical analysis for the attitude control system design and offers practical help for the spacecraft designers.