• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1266-1272
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• 2011

For way-point tracking of an autonomous underwater vehicle, a state feedback controller was designed by using pole placement scheme in discrete time domain. In the controller, 4 state variables were used for regulating the depth of the vehicle in z direction, and 3 state variables, for steering the vehicle in xy plane. Assuming constant speed of AUV, we simplified the design of the way-point tracking system. The proposed controller was simulated by MATLAB/Simulink using 6 degree-of-freedom nonlinear model and its performance of way point tracking was shown to be fulfilled within 1 m, nevertheless the proposed controller is quite simple and easy to implement compared to sliding mode controller.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.17-20
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• 2002

This paper describes the synthesis of robust and non-fragile Η$^{\infty}$ output feedback controller for parameter uncertain systems with time delay. The sufficient condition of controller existence, and the design method of robust and non-fragile Η$^{\infty}$ output feedback controller are presented. The obtained conditions can be represented as parameterized LMIs, and PLMIs feasibility problems involve infinitely many LMIs hence are very hard to solve. Therefore, PLMIs are replaced by a finite set of LMIs using relaxation techniques(separated convexity concepts). This method is potentially conservative but often provide practically exploitable solutions of difficult problems with a reasonable computational effort. The compatibility of resulting controller is illustrated by numerical example.

• Journal of KSNVE
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• v.10 no.6
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• pp.1083-1093
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• 2000

This paper is concerned with the real-time automatic tuning of the positive position feedback controller for smart structures by the genetic algorithms. The genetic algorithms haute proven its effectiveness in searching optimal design parameters without falling into local minimums thus rendering globally optimal solutions. The advantage of the positive position feedback controller is that if it is tuned properly it can enhance the damping value of a target mode without affecting other modes. In this paper, we develop for the first time a real-time algorithm for determining a tuning frequency of the PPF controller based on the genetic algorithms. To this end, the digital PPF control law is downloaded to the DSP chip and a main program, which runs the genetic algorithms in real time, updates the parameter of the controller in real time. Hence, any kind of control including the positive position feedback controller can be used in adaptive fashion in real time. Experimental results show that the real-time tuning of the positive position feedback controller can be achieved successfully. so that vibrations are suppressed satisfactorily.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.39-44
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• 2022

This paper presents a development of throttle and brake controller for autonomous vehicle simulation environment. Most of 3D simulator control autonomous vehicle by throttle and brake command. Therefore additional longitudinal controller is required to calculate pedal input from desired acceleration. The controller consists of two parts, feedback controller and feedforward controller. The feedback controller is designed to compensate error between the actual acceleration and desired acceleration calculated from autonomous driving algorithm. The feedforward controller is designed for fast response and the output is determined by the actual vehicle speed and desired acceleration. To verify the performance of the controller, simulations were conducted for various scenarios, and it was confirmed that the controller can successfully follow the target acceleration.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.12-17
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• 2014

In this paper, we propose a controller capable of reducing the effect of measurement errors under AC and DC noise. Typically, the control system measures data through a sensor. If sensor noise is included in a controller via the feedback channel, the signal is distorted and the entire system cannot work normally. Therefore, some appropriate action to counter the measurement error effect is essential in the controller design. Our controller is equipped with a gain-scaling factor and a compensator to reduce the effect of measurement error in the feedback signal. Also, we use a switching control strategy to enhance the performance of the controller regarding convergence speed. Our proposed controller can therefore effectively reduce the AC and DC noise of the sensor. We analyze the proposed controller by Laplace transform technique and our control method is verified via MATLAB simulation.

• Journal of KSNVE
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• v.7 no.4
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• pp.605-612
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• 1997

A state feedback controller and an observer have been developed and analyzed for an aircraft's safe landing in the windshear called microburst. The observer estimates the ambient wind field as well as the full-order longitudinal state vector. The controller uses the wind and state estimates for guiding the control inputs for safe landing. For the observer and controller gains, the design methodologies of linear quadratic estimation and linear quadratic regulation have been exploited. Analysis shows that some of the microburst-induced aircraft accidents in the past might have been avoided with the designed autopilot.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.196-200
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• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used fur on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.176.5-176
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• 2001

This paper presents a fuzzy dynamic output feedback controller design method for Parallel Distributed Compensation (PDC)-type Takagi-Sugeno (T-S) model based fuzzy dynamic system with H$\infty$ performance and additional constraints on the closed pole placement. Design condition for these controller is obtained in terms of the linear matrix inequalities (LMIs). The proposed fuzzy controller satisfies the disturbance rejection performance and the desired transient response. The design method is verified by this method for an inverted pendulum with a cart using the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.413-417
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• 1989

The idea of expert control is to incorporate a rule based expert system in a feedback control system. In this paper, the expert system concepts are instead used as an element of the feedback loop in a single controller. The algorithms are coded in as pure form as possible and the heuristic logic is implemented as rules. This paper reports on effort to produce an implementation of an expert controller on microcomputer based system, including an industrial programmable controller.