The attitude-tracking problem of hypersonic morphing vehicles (HMVs) is investigated in this research. After introducing variable-span wings, the optimal aerodynamic shape is available throughout the entire flight mission. However, the morphing wings cause significant changes in aerodynamic coefficients and mass distribution, challenging the attitude control. Therefore, a complete design procedure for the flight control system is proposed to address the issue. Firstly, the original model and the control-oriented model of HMVs are built. Secondly, in order to eliminate the influence caused by the multisource uncertainties, an adaptive fixed-time disturbance observer combined with fuzzy control theory is established. Thirdly, the fixed-time control method is developed to stabilise hypersonic morphing vehicles based on a multivariable sliding mode manifold. The control input can be obtained directly. Finally, the effectiveness of the proposed method is proved with the help of the Lyapunov theory and simulation results.

Pneumatic muscle actuator (PMA) similar to biological muscle is a new type of pneumatic actuator. The flexible manipulator based on PMAs was constructed to simulate the actual movement of the human upper arm. Considering the model errors and external disturbances, the fuzzing sliding mode control based on the saturation function was proposed. Compared with other fuzzy control methods, fuzzy control and saturation function are used to adjust the robust terms to improve the tracking accuracy and reduce the high-frequency chattering.

We study a delayed fuzzy $H_{\infty }$ control problem for an offshore platform under external wave forces. First, by considering perturbations of the masses of the platform and an active mass damper, a Takagi–Sugeno fuzzy model is established. Then, by introducing time delays into the control channel, a delayed fuzzy state feedback $H_{\infty }$ controller is designed. Simulation results show that the delayed fuzzy state feedback $H_{\infty }$ controller can reduce vibration amplitudes of the offshore platform and can save control cost significantly.

The vibration control of smart structure is considered in this paper. Membrane SAR antenna structure with piezoelectric sensors and actuators is taken as an example. The dynamic model is build up based on vector form intrinsic finite element (VFIFE) method. The four nodes membrane element, sensor element and actuator element for VFIFE are presented. By decentralized control stratagem, the bending and torsional vibrations of the membrane SAR antenna can be decoupled on measurement and driving control. The fuzzy control and adaptive fuzzy control are applied to suppress the bending and torsional vibrations of the membrane SAR structure. In the numerical experiment section, form finding is first carried out, then vibration control simulations are studied. The results demonstrate that adaptive fuzzy control algorithm can suppress the vibrations more effectively than the fuzzy control algorithm.

Beam alignment depends on CCD real-time image analysis and processing. In order to improve the quality of the alignment, multiple filters are used in far-field and near-field image processings. These multiple filters are constituted of an average filter and a median filter in different connection sequences, so that they can deal with different kinds of noise. To reduce the effect of the unknown nonlinear relationship between motor running steps and deviation pixels, a feasible methodology is offered to improve this phenomenon and a fuzzy algorithm is applied to the motor feedback control process. Because of the fuzzy control it is not necessary to establish an accurate mathematical model, so the impact of the nonlinear relationship will be reduced.

This paper presents an integrated guidance and control (IGC) design method for an unmanned aerial vehicle with static stability which is described by a nonlinear six-degree-of-freedom (6-DOF) model. The model is linearized by using small disturbance linearization. The dynamic characteristics of pitching mode, rolling mode and Dutch rolling mode are obtained by analysing the linearized model. Furthermore, an IGC design procedure is also proposed in conjunction with a proportional–integral–derivative (PID) control method and fuzzy control method. A PID controller is applied in the control loop of the elevator and aileron, and the attitude angle and attitude angular velocity are used as compensation feedback, giving a simple and low-order control law. A fuzzy control method is applied to perform the cross-coupling control of rolling and yawing. Finally, the 6-DOF simulation shows the effectiveness of the developed method.

Artificial intelligence techniques applied to control processes are particularly useful when the elements to be controlled are complex and can not be described by a linear model. A trade-off between performance and complexity is the main factor in the design of this kind of system. The use of fuzzy logic is specially indicated when trying to emulate such human control actions as driving a car. This paper presents a fuzzy system that cooperatively controls the throttle and brake pedals for automatic speed control up to 50km/h. It is thus appropriate for populated areas where driving involves constant speed changes, but within a range of low speeds because of traffic jams, road signs, traffic lights, etc. The system gets the current and desired speeds for the car and generates outputs to control the two pedals. It has been implemented in a real car, and tested in real road conditions, showing good speed control with smooth actions resulting in accelerations that are comfortable for the car's occupants.

The paper introduces fuzzy linguistic logic programming, which is a combination of fuzzy logic programming, introduced by P. Vojtáš, and hedge algebras in order to facilitate the representation and reasoning on human knowledge expressed in natural languages. In fuzzy linguistic logic programming, truth values are linguistic ones, e.g., VeryTrue, VeryProbablyTrue and LittleFalse, taken from a hedge algebra of a linguistic truth variable, and linguistic hedges (modifiers) can be used as unary connectives in formulae. This is motivated by the fact that humans reason mostly in terms of linguistic terms rather than in terms of numbers, and linguistic hedges are often used in natural languages to express different levels of emphasis. The paper presents: (a) the language of fuzzy linguistic logic programming; (b) a declarative semantics in terms of Herbrand interpretations and models; (c) a procedural semantics which directly manipulates linguistic terms to compute a lower bound to the truth value of a query, and proves its soundness; (d) a fixpoint semantics of logic programs, and based on it, proves the completeness of the procedural semantics; (e) several applications of fuzzy linguistic logic programming; and (f) an idea of implementing a system to execute fuzzy linguistic logic programs.

This paper describes the research activities at the Laboratory for Control Systems and Automation (LCA) which is a section of the Production Department of Korea Advanced Institute of Science and Technology (KAIST). LCA's research activities are focused on providing fundamental technologies to cope with the current trends in manufacturing automation. The R & D activities at the laboratory cover diverse areas. The research activities at the laboratory are classified into three sections—robotics and automation, fluid servo control, and manufacturing process control. Detailed explanations are given here on robotics and its application to automation. As a laboratory belonging to a nation's leading educational institution, LCA also conducts active educational activities. Such educational programs are also introduced in detail.

This paper presents a design methodology to stabilize a class of multi-variant nonlinear system after a high disturbance occurs. It investigates application of Takagi-Sugeno type fuzzy controller (T-S-FC) to an inverted pendulum mechanism, actuated by an armature-controlled DC electrical motor.

Fuzzy controllers use heuristic information in developing design methodologies for control of non-linear dynamic systems. This approach eliminates the need for comprehensive knowledge and mathematical modeling of the system, and in cases of more complex systems, approximation and simplifications in order to achieve feasible mathematical model is not required.

The paper presents the stages of development of the Fuzzy Controller for an inverted pendulum by developing a two-input, Mamdani type system. It evaluates the performance of the system. Then a four-input T-S-FC type is developed. The research compares performances of each controller and presents the result of tests. A model for a DC motor is developed in this study, in order to measure the effect of time delays and response time caused by inherent properties of the physical system. The final part will demonstrate the complete operational system with the DC electrical motor included in the test system.

An adaptive fuzzy sliding control scheme is proposed to control a passive robotic manipulator. The motivation for the design of the adaptive fuzzy sliding controller is to eliminate the chattering and the requirement of pre-knowledge on bounds of error associated with the conventional sliding control. The stability and convergence of the adaptive fuzzy sliding controller is proven both theoretically and practically by simulations. A three-link passive manipulator model with two unactuated joints is derived to be used in the simulations. Simulation results demonstrate that the proposed system is robust against structured and unstructured uncertainties.

This paper describes a software tool to automate a design method for robotic fuzzy force control. The original method was developed to ensure robust and stable force control in situations where environmental stiffness at the robot/task interface is unknown, obviating the use of fixed-gain controllers. It did, however, involve a manual design process requiring significant knowledge of control theory and fuzzy logic. This process has been automated in the form of a Windows-based application, requiring minimal user inputs and incorporating an automatic tuning technique for improved performance in the final controller application. Results obtained from an experimental robot are presented.

This paper deals with the design and development of a new perception system for controlling a set of ultrasonic transducers working at two different frequencies generated by a digital signal processor (DSP). Data gathering speed is increased by the simultaneous firing of sonars without crosstalk problems. The software and hardware implementation are presented as well as the results from the calibration process and the integration on board the mobile robot RAM-2. Finally, a demonstration of the use of the proposed equipment to wall-following via a fuzzy controller is described.

A new approach to the design of a fuzzy continuous gain scheduled H∞ controller based on Taylor series fitting for n-joint rigid robotic manipulators, which guarantees satisfactory dynamic characteristics in the whole movement range, is presented. This scheme combines the gain scheduled H∞ theory with the Linear Matrix Inequalities (LMI) approach to design a continuous gain scheduled H∞ controller, which is applicable to systems with fast state variations so that the deficiency of the coventional gain scheduled controllers is overcome, with the use of Taylor series fitting. Fuzzy control is then incorporated so that the design controller possesses the characteristics of a fast response for large errors and a well-damped response for small errors. The system thus always has a good dynamic performance along with the variations of the system states. Simulations and experiments demonstrate the effectiveness of the designed controller.

The purpose of this study is to investigate and optimize the dynamics of state transitions during a process of multistage fuzzy control systems. A variation of the methodology for solving the Vehicle Routing Problem, known as the Savings Method, is applied to finding a sequence of controls to maximize the overall performance of multistage control processes. The Savings Method has been modified into a heuristic to traverse state precedence when transitioning from one known system state to another. In addition, the procedure allows for imposing of two different types of precedences (mandatory and optional) among transition states, as well as a pair of fuzzy constraints and goals throughout each stage of the process. As the control of the system progresses, fuzzy goals may be preset or introduced interactively.

Fuzzy control has been widely applied in industrial controls and domestic electrical equipment. The automatic learning of fuzzy rules is a key technique in fuzzy control. In this paper, a software development system for fuzzy control is presented. Since the learning of fuzzy rules can be seen as finding the best classifications of fuzzy memberships of input-output variables in a fuzzy controller, it can also be seen as the combination optimization of input-output fuzzy memberships. Multi-layer feedforward network and genetic algorithms (GA) can be used for the automatic learning of fuzzy rules. The algorithms and their characteristics are described. The software development system has been successfully used for the design of some fuzzy controllers.