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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 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.

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.

The purpose of this paper is to show how to design effective and practical controllers that satisfy multiple simultaneous specifications (MSS) criteria concurrently.

In automatic flight control system or autopilots, MSS such as good holding (small static altitude holding error), fast response, smooth transition (less oscillation, overshoot) are needed to be satisfied concurrently. So how to design the MSS controller effectively and practically is a very significant and challenging job. An MSS controller design method is proposed. The paper further applies the method together with the fine‐tuning technique to the 6 DoF non‐linear F‐16 fighter longitudinal control channel. Simulation results show its applicability to non‐linear flight control system.

It was found that the simulation results demonstrate that the MSS design method with controller fine‐tuning can be applied to the nonlinear F‐16 fighter longitudinal control system.

The practical implementation of this research work needs further investigation.

The simplicity of the design algorithm facilitates the application of the design to other aircrafts by use of Matlab.

The simulation results presented demonstrate that the proposed MSS apply well to non‐linear F‐16 fighters.

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.

To develop a non‐linear modelling technique for modern air vehicles with an application to a twin rotor multi‐input‐multi‐output system (TRMS) which resembles the dynamics of a helicopter to a certain extent and presents formidable control challenges.Design/methodology/approach – A Non‐linear AutoRegressive process with eXternal input (NARX) approach with a feedforward neural work and a resilient propagation (RPROP) algorithm is used to model the system. The RPROP algorithm possesses direct weight update capability without considering the size of the partial derivative. The obtained model is shown to be adequate by carrying out convincing tests such as correlations, cross‐validations and prediction based on predicted output and, therefore, is deemed to be reliable.Findings – It is shown that the combination of the feedforward neural networks and RPROP algorithms is very useful and effective in modelling systems with high non‐linearity and other complex characteristics. It is always important to attain a model with minimum number of neurons in different layers of the network by overcoming the possibility of getting stuck in the shallow local minimum of error function by using RPROP algorithm.Research limitations/implications – The system is modelled off‐line. On‐line modelling will be required for real‐time control purpose.Practical implications – The non‐linear modelling approach presented in this study is shown to be appropriately applicable to model new generations' air vehicles and other complex mechatronic systems such as TRMS. So, the approach will be appealing to industrial applications.Originality/value – This paper addresses the problems of modelling modern sophisticated non‐linear systems with complex characteristics and uncertain dynamics.

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.

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.

The purpose of this paper is to propose a kind of fuzzy adaptive control method to control non‐linear system that has the characteristic of small time delay and fast respond speed.

The paper analyzes the production process and the actual condition of the preheat process of the plating zinc and painting plastic scribbled of double layer welded pipe that has the small time delay and fast respond speed, and also gives the preheat process mathematical model. Fuzzy adaptive control method with hierarchical structure is used which aims at one non‐linear system that has the characteristic of small time delay and fast responds speed. Through the simulation, it proves the mentioned method is effective to control the temperature system for double layers welded pipe in welding process.

Based on the mathematical model proposed about the production process and the actual condition of the preheat process, the fuzzy adaptive control method is effective to control the temperature system for double layers welded pipe in welding process.

The paper proposes fuzzy adaptive control method with hierarchical structure which has the basic fuzzy control grade, adaptive adjust grade, and process state judgment grade.

A very useful method in welding process for double layers welded pipe.

The new mathematical model is proposed about the production process, and the new control method is used in the temperature system for double layers welded pipe in welding process.

This paper aims to deal with application of artificial intelligence for solving real time control complication adhered with the controlled operation of a buck power converter. This type of converter finds application for power conversion at various levels for the direct current-direct current power industry to step down the input voltage.

Use of ANN-RL (Artificial Neural Networks- Reinforcement Learning)-based control algorithm to control buck power converter shows robustness against parameter and load variation. Because of non-linearity instigated by element used for switching, control of this converter becomes an arduous control predicament. All the classical control techniques are based on an approximate linear model of the step down converter and these techniques fail to handle actual non-linearity.

In this paper, a reinforcement learning-based algorithm has been used to handle and control buck power converter output voltage, without approximating the model of converter. The non-linearity instigated in converter is subjected to state of switch. Model of buck power converter is defined as a multi-step decision problem so that it can be solved using mathematical model of Markov decision process (MDP) and, in turn, reinforcement learning can be implemented. As MDP model is available for a discrete state system so model of converter has to be discretized and then value iteration is applied and output is analyzed. Load regulation and integral time absolute error analysis is done to show efficacy of this technique.

To mitigate the effect of discretization function approximation using neural network is applied. MATrix LABoratory has been used for implementation and result indicates an improvement in the overall response.