This paper discusses the design of a controller for teleoperation systems which operate in contact with unknown environments. For this purpose, the controller in the slave part is designed to use a classical controlle...
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This paper discusses the design of a controller for teleoperation systems which operate in contact with unknown environments. For this purpose, the controller in the slave part is designed to use a classical controller and a neuro-fuzzy controller. To have a haptic teleoperation, the force feedback from the contact point to the master side is needed. In this study virtual force is fed back to the master instead of the measured contact force and the feedback force is the position error i.e. the difference between the position of master and slave robots. Primary, classical controller was designed for this object, the gains of this controller is obtained by standard genetic algorithm (GA), in offline and optimization method at nominal operating point. In master-slave robot with classical controller, when, robot works in soft or hard environment and the operating point was changed, the obtained controller are not suitable. For this reason, online neuro - fuzzy gain scheduling controller are designed for this system to tune the controller's parameters. The NF trained in offline mode based on GA and in online application tuned the controller based on monitoring of operating point of system. This method makes the system robust to uncertainties such as modeling errors, parameter mismatches and disturbances. The simulation results show the ability of proposed method in compare of other controllers such as Kalman filter.
control-relevance is a paradigm that interconnects identification with successive model-based control design. Hereby, the current controller, used to conduct identification experiments, is an important factor to succe...
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ISBN:
(纸本)9781424474264
control-relevance is a paradigm that interconnects identification with successive model-based control design. Hereby, the current controller, used to conduct identification experiments, is an important factor to success in the design of a new, improved controller. The aim of this paper is to investigate the role of the experimental controller in robust-control-relevant modeling. Such a study is sensible only when unnecessary conservatism is prevented in the construction of perturbed model sets. Hereto, this paper establishes a model uncertainty description that transparently connects to the imposed robust performance criterion. By confronting the developed approach with a next-generation industrial wafer stage, the important role of the experimental controller during modeling for robust control is clarified indeed. It turns out that only after an increase of performance in successive control design iterations, construction of higher order model sets becomes both feasible and significant. As such, in pursuit of performance optimization up to fundamental limits, the experimental controller ensures a gradual extrapolation of the current experimental conditions.
This paper presents a method for the estimation of thrust model parameters of uninhabited airborne systems using specific flight tests. Particular tests are proposed to simplify the estimation. The proposed estimation...
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This paper presents a method for the estimation of thrust model parameters of uninhabited airborne systems using specific flight tests. Particular tests are proposed to simplify the estimation. The proposed estimation method is based on three steps. The first step uses a regression model in which the thrust is assumed constant. This allows us to obtain biased initial estimates of the aerodynamic coefficients of the surge model. In the second step, a robust nonlinear state estimator is implemented using the initial parameter estimates, and the model is augmented by considering the thrust as random walk. In the third step, the estimate of the thrust obtained by the observer is used to fit a polynomial model in terms of the propeller advanced ratio. We consider a numerical example based on Monte-Carlo simulations to quantify the sampling properties of the proposed estimator given realistic flight conditions.
A novel direct-drive rotary-linear switched reluctance motor (RLSRM) is proposed. The motor has the advantages of robust mechanical structure, low manufacture cost and capability of operation under hostile environment...
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A novel direct-drive rotary-linear switched reluctance motor (RLSRM) is proposed. The motor has the advantages of robust mechanical structure, low manufacture cost and capability of operation under hostile environments. Simulation tools such as finite element analysis (FEA) are applied for the verification of motor performance and corresponding experiments are carried out to testify the simulation results. The authors expect that the innovative actuator design can be applied in industrial applications that require both rotary and linear motions.
The shape optimization using the multi-objective genetic algorithm and the analysis of the 3-dimensional wings in ground effect has been performed. Unlike an airplane flying out of ground effect, wings in ground effec...
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ISBN:
(纸本)9781600867392
The shape optimization using the multi-objective genetic algorithm and the analysis of the 3-dimensional wings in ground effect has been performed. Unlike an airplane flying out of ground effect, wings in ground effect have to satisfy static height stability to cruise steadily above the ground or water surface. In this study, both aerodynamic performance and static height stability are considered as objectives. Due to the multi-objective optimization, the optimal solutions are not unique but a set of non-dominated potential solutions: Pareto optima. After twenty evolutions, 74 non-dominated Pareto optima can be obtained. Three objectives, lift coefficient, lift-to-drag ratio and static height stability, are strongly tradeoff each other so that the unique and dominate solution is not existed. The Pareto optima obtained in this study are placed on the edge of the objective space. From the analysis of the Pareto optima, static height stability was improved at the lower height and the higher angle of attack. The lift-to-drag ratio increases with the lower height since the pressure on the lower wing surface increases while that on the upper surface remains constant. The influence of the tip vortex reduces as ground approached.
Researchers have developed a new type of medical micropump for an endoscopic robot, which is driven by a linear actuator based on a direct current (DC) motor. This micropump consists of two active one-way valves and a...
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Researchers have developed a new type of medical micropump for an endoscopic robot, which is driven by a linear actuator based on a direct current (DC) motor. This micropump consists of two active one-way valves and a cylindrical air drum. The overall size of the pump prototype is 12.5 mm in diameter and 56 mm in length. This paper describes the structure of the micropump and linear actuator and analyzes the inflation mechanism of the micropump. The experimental results show that the driving force of the linear actuator can reach up to 2.55 N, which fulfills the need of the micropump. The rated output flow of the micropump is 16 mL/min, which can rapidly supply the gas bag with enough air with minimal noise and vibration.
This paper deals with the conceptual design and kinematic analysis of a novel 2-DOF translational parallel robot for pick-and-place operations named as T2. The proposed architecture is composed of two actuated kinemat...
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The on-line least-square identification method is applied for the linear switched reluctance motor (LSRM) based closed-loop control system. Since switched reluctance motors have severe nonlinear characteristics, on-li...
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The on-line least-square identification method is applied for the linear switched reluctance motor (LSRM) based closed-loop control system. Since switched reluctance motors have severe nonlinear characteristics, on-line system identification for model parameters is the basis for precise modeling and effective control algorithms. By on-line parameter identification of LSRM with least-square scheme, the parameter characteristics can be predicted despite of operation variations. The experimental results demonstrate the identification scheme has fast response and converging speed and the capability of effective parameter prediction under different conditions.
In the paper, we formulate a new energy function followed by the use of graph cuts to refine the disparity map which takes segment as node. Firstly, the robust disparity plane fitting is modeled and the method of Sing...
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In the paper, we formulate a new energy function followed by the use of graph cuts to refine the disparity map which takes segment as node. Firstly, the robust disparity plane fitting is modeled and the method of Singular Value Decomposition (SVD) is used to solve least square. In order to ensure reliable pixel sets for the segment, we filter out outliers through three main rules, namely; cross-checking, judging reliable area and measuring the distance between previous disparity to the computed disparity plane. Secondly, we apply improve hierarchical clustering algorithm to merge neighbor. Finally, the final disparity map is obtained. Experimental results demonstrate that our approach is effective in improving the state of the art.
Based on the sensitivity-based approach, we discuss the reinforcement learning problem of semi-Markov decision processes (SMDPs) with average reward. First, we provide a new Bellman optimality equation. On this basis,...
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Based on the sensitivity-based approach, we discuss the reinforcement learning problem of semi-Markov decision processes (SMDPs) with average reward. First, we provide a new Bellman optimality equation. On this basis, we propose a relative value iteration (RVI) reinforcement learning algorithm. The new RVI reinforcement learning algorithm may avoid the estimation of optimal average reward in the process of learning and has a good convergence rate.
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