• 제어로봇시스템학회논문지
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• 제2권4호
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• pp.297-305
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• 1996

In this paper a novel robust adaptive fuzzy controller for the nonlinear system with state-dependent uncertainty is proposed. The conventional adaptive fuzzy controller determines the function of state variable bounding the state-dependent uncertain term in the system dynamics on the local state space by off-line calculation. Whereas the proposed method determines that function by the fuzzy inference so that it guarantees the stability of the closed loop system globally on the whole state space. In addition, the method is applicable to the multi-input system. We applied the proposed method to the Burn Control of the Tokamak fusion reactor whose dynamics contains the state-dependent uncertainty and proved the effectiveness of the scheme by using the simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.189-194
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• 1994

Adaptive control systems based upon the command generator tracker(CGT) approach have attracted considerable interest because of the simple structure of its adaptive controller. Some attempts to such improve the adaptive control algorithm, for the sake of the application to broader class of plants, are made. Recently, Su and Sobel(1992) proposed that those schemes can be treated by an unified theory using a metasystem representation with some types of supplementary dynamics. However, in their method, it is difficult to find the dynamic compensator, which is proper and output feedback stabilizable, for the uncertain plant. This paper proposes a new design method of such supplementary dynamics and some parameters of adaptive control system for linear time invariant SISO plants. The method gives a concrete and systematic design method using only a few priori knowledge of the plant.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.248-255
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• 2000

Whereas the linear PID controller is widely used for control of industrial servo systems a high precision positioning system is not easy to control only with the PID controller due to uncertain nonlinear dynamics such as friction backlash etc. As a viable means to overcome the difficulty a learning control scheme is proposed in this paper that is simple and straightforward to implement. The proposed learning controller takes full advantage of current feedback capability of the inner-loop of the control system in that electrical motor dynamics as the well as mechanical part of X-Y positioning system is included in the learning control scheme, The experimental results are given to demonstrate its feasibility and effectiveness in terms of convergence precision of tracking and robustness in comparison with the conventional control method.

• 제어로봇시스템학회논문지
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• 제1권2호
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• pp.88-93
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• 1995

This paper presents a study using neural networks in the design of the tracking controller of robotic systems. Our strategy is to put to use the available knowledge about the robot manipulator, such as estimation models, in the contoller design via the computed torque method, and then to add the neural network to control the remaining uncertainty. The neural network used here learns to provide the inverse dynamics of the plant uncertainty, and acts as an inverse controller. In the simulation study, we verify that the proposed neural network controller is robust not only to structured uncertainties, but also to unstructured uncertainties such as friction models.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.598-600
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• 1998

In this paper, a robust controller is proposed to achieve the accurate tracking for uncertain robot manipulators with hydraulic actuator dynamics. The parameter uncertainty can be quantified by the linear parameterization technique. A switching controller is proposed to guarantee the global asymptotic stability of the plant. In order to eliminate the chattering caused by the switching controller, a smoothing controller is proposed using the boundary layer technique around the sliding surface. It is shown that the smoothing controller guarantees the uniform ultimate boundedness of the tracking, error. The proposed controller shows good better tracking performance.

• Advances in aircraft and spacecraft science
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• 제6권5호
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• pp.371-389
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• 2019

The implementation of a robust $H_{\infty}$ Control, which is numerically efficient for uncertain nonlinear dynamics, on longitudinal and lateral autopilots is realised for a quarter scale Piper J3-Cub model accepted as an unmanned aerial vehicle (UAV) under the condition of sensor noise and disturbance effects. The stability and control coefficients of the UAV are evaluated through XFLR5 software, which utilises a vortex lattice method at a predefined flight condition. After that, the longitudinal trim point is computed, and the linearization process is performed at this trim point. The "${\mu}$-Synthesis"-based robust $H_{\infty}$ control algorithm for roll, pitch and yaw displacement autopilots are developed for both longitudinal and lateral linearised nonlinear dynamics. Controller performances, closed-loop frequency responses, nominal and perturbed system responses are obtained under the conditions of disturbance and sensor noise. The simulation results indicate that the proposed control scheme achieves robust performance and guarantees stability under exogenous disturbance and measurement noise effects and model uncertainty.

• Transactions on Control, Automation and Systems Engineering
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• 제1권2호
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• pp.94-98
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• 1999

Optical scanning systems use glavanometers to point the laser beam to the desired position on the workpiece. The angular speed of a galvanometer is typically controlled using Proportional+Integral+Derivative(PID) control algorithms. However, natural variations in the dynamics of different galvanometers due to manufacturing, aging, and environmental factors(i.e., process uncertainty) impose a hard limit on the bandwidth of the galvanometer control system. In general, the control bandwidth translates directly into efficiency of the system response. Since the optical scanning system must have rapid response, the higher control bandwidth is required. Auto-tuning PID algorithms have been accepted in this area since they could overcome some of the problems related to process uncertainty. However, when the galvanometer is attached to a larger mechanical system, the combined dynamics often exhibit resonances. It is well understood that PId algorithms may not have the capacity to increase the control bandwidth in the face of such resonances. This paper compares the achieable performance and robustness of a galvanometer control system using a PID controller tuned by the Ziegler-Nichols method and a controller designed by the Quantitative Feedback Theory(QFT) method. The results clearly indicate that-in contrast to PID designs-QFT can deliver a single, fixed controller which will supply high bandwidth design even when the dynamics is uncertain and includes mechanical resonances.

• International Journal of Control, Automation, and Systems
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• 제2권3호
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• pp.374-383
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• 2004

This paper presents a neural network adaptive controller for autonomous diving control of an autonomous underwater vehicle (AUV) using adaptive backstepping method. In general, the dynamics of underwater robotics vehicles (URVs) are highly nonlinear and the hydrodynamic coefficients of vehicles are difficult to be accurately determined a priori because of variations of these coefficients with different operating conditions. In this paper, the smooth unknown dynamics of a vehicle is approximated by a neural network, and the remaining unstructured uncertainties, such as disturbances and unmodeled dynamics, are assumed to be unbounded, although they still satisfy certain growth conditions characterized by 'bounding functions' composed of known functions multiplied by unknown constants. Under certain relaxed assumptions pertaining to the control gain functions, the proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded (UUB). Simulation studies are included to illustrate the effectiveness of the proposed control scheme, and some practical features of the control laws are also discussed.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2222-2229
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• 2023

Radon and radon progeny being natural radioactive pollutants, seriously affect the health of uranium miners. Radon reduction by ventilation is an essential means to improve the working environment. Firstly, the relational model is built between the radon exhalation rate of the loose body and the ventilation parameters in the stope with radon percolation-diffusion migration dynamics. Secondly, the model parameters of radon exhalation dynamics are uncertain and described by triangular membership functions. The objective functions of the left and right equations of the radon exhalation model are constructed according to different possibility levels, and their extreme value intervals are obtained by the immune particle swarm optimization algorithm (IPSO). The fuzzy target and fuzzy constraint models of radon exhalation are constructed, respectively. Lastly, the fuzzy aggregation function is reconstructed according to the importance of the fuzzy target and fuzzy constraint models. The optimal control decision with different possibility levels and importance can be obtained using the swarm intelligence algorithm. The case study indicates that the fuzzy aggregation function of radon exhalation has an upward trend with the increase of the cut set, and fuzzy optimization provides the optimal decision-making database of radon treatment and prevention under different decision-making criteria.

• 전기학회논문지
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• 제66권12호
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• pp.1759-1771
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• 2017

In this paper, an improved continuous integral variable structure systems(ICIVSS) with the prescribed control performance is designed for simple regulation controls of uncertain general linear systems. An integral sliding surface with an integral state having a special initial condition is adopted for removing the reaching phase and predetermining the ideal sliding trajectory from a given initial state to the origin in the state space. The ideal sliding dynamics of the integral sliding surface is analytically obtained and the solution of the ideal sliding dynamics can predetermine the ideal sliding trajectory(integral sliding surface) from the given initial state to the origin. Provided that the value of the integral sliding surface is bounded by certain value by means of the continuous input, the norm of the state error to the ideal sliding trajectory is analyzed and obtained in Theorem 1. A corresponding discontinuous control input with the exponential stability is proposed to generate the perfect sliding mode on the every point of the pre-selected sliding surface. For practical applications, the discontinuity of the VSS control input is approximated to be continuous based on the proposed modified fixed boundary layer method. The bounded stability by the continuous input is investigated in Theorem 3. With combining the results of Theorem 1 and Theorem 3, as the prescribed control performance, the pre specification on the error to the ideal sliding trajectory is possible by means of the boundary layer continuous input with the integral sliding surface. The suggested algorithm with the continuous input can provide the effective method to increase the control accuracy within the boundary layer by means of the increase of the $G_1$ gain. Through an illustrative design example and simulation study, the usefulness of the main results is verified.