A robust speed control scheme for a brushless DC (BLDC) motor using an adaptive input-output linearisation technique is presented. By using this technique, the nonlinear motor model can be linearised in Brunovski cano...
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A robust speed control scheme for a brushless DC (BLDC) motor using an adaptive input-output linearisation technique is presented. By using this technique, the nonlinear motor model can be linearised in Brunovski canonical form, and the desired speed dynamics can be obtained based on the linearised model. This control technique, however, gives an undesirable output performance under the mismatch of the system parameters and load conditions caused by the incomplete linearisation. For the robust output response, the disturbance torque and flux linkage will be estimated by a modelreferenceadaptive technique. The adaptation laws are derived by the Popov's hyperstability theory and positivity concept. The proposed scheme is implemented on a BLDC motor using the software of DSP TMS320C30 and the effectiveness is verified through the comparative simulations and experiments.
In this paper we explore the use of the model reference adaptive system (MRAS) in a dynamic socio-economic enviroment. We prove the existence of adaptation laws which ensure that the error equation in MRAS for a dynam...
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In this paper we explore the use of the model reference adaptive system (MRAS) in a dynamic socio-economic enviroment. We prove the existence of adaptation laws which ensure that the error equation in MRAS for a dynamic input-output model is asymptotically stable. The resulting theorems are appraised through some numerical simulations.
Pi‐coefficients [4] provide a quantitative measure of system stability. With the goal of finding the largest stability range of the feedback gain in a system, the max‐pproblem [9] can be formulated so that its solut...
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Pi‐coefficients [4] provide a quantitative measure of system stability. With the goal of finding the largest stability range of the feedback gain in a system, the max‐pproblem [9] can be formulated so that its solution gives an optimal set ofpi‐coefficients. This paper simplifies the max‐pproblem by analyzing the special structure of a negative semi‐definite matrix, called theM‐matrix. To find the optimalpi‐coefficients of the general second‐order systems, seven solutions corresponding to different conditions are derived. In the examples we apply the results to a model reference adaptive system, calculate the pi‐coefficients of its error dynamics model, and use the optimalpi‐coefficient curves to determine a good sampling period.
It is a key problei for the design of modelreference adaptife systems to determine the adaptife law for updating the adjustable parameters set up in the system. The generalized adaptife law has been derived for each ...
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It is a key problei for the design of modelreference adaptife systems to determine the adaptife law for updating the adjustable parameters set up in the system. The generalized adaptife law has been derived for each of the discrete and continuous time cases. However, since the conventional discrete time adaptive law does not take account of the length of the sampling period, it cannot yield the continuous time adaptive law in the limiting case when the sampling period tends to zero. This problem can be resolved by introducing the delta operator defined as δ = (z - 1)/T (T: sampling period), in place of the time shift operator z. This paper presents an expression of the generalized adaptife law using the delta operator and shows some particular algorithms derived from it. The relationship between the presented expression and both the conventional continuous and discrete time adaptive laws is also discussed.
In the present paper an optimal model reference adaptive system is proposed. A procedure is given to compute the optimal adaptive gains in model reference adaptive systems using the simple square criterion. Two numeri...
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In the present paper an optimal model reference adaptive system is proposed. A procedure is given to compute the optimal adaptive gains in model reference adaptive systems using the simple square criterion. Two numerical examples are included to demonstrate the theoretical results.
In undertaking parameter identification and adaptive control of a thermo-hydraulic system representing the core channels of nuclear reactor plants, if the flow velocity in a channel can be assumed to be the input and ...
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In undertaking parameter identification and adaptive control of a thermo-hydraulic system representing the core channels of nuclear reactor plants, if the flow velocity in a channel can be assumed to be the input and the outlet temperature the output, the system considered can be classed as bilinear on account of the multiplication terms for the temperature and flow velocity contained in the equations representing the same system. A new representation for this bilinear system is proposed, and the adaptive schemes for both parameter identification and control are developed in the pattern of the model reference adaptive system with use made of the Lyapunov method. Both schemes are shown to be stable, and to further be globally stable if the inputs possess frequencies sufficiently varied. Some successful parameter identification experiments based on the proposed method are covered, which were performed on a test section representing a simplified channel in which the input-flow rate is varied in binary pattern.
Parameter identification and adaptive control schemes are presented for a point reactor with internal feedbacks which lead to the nonlinearity of the overall system. Both are shown stable with new representation of th...
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Parameter identification and adaptive control schemes are presented for a point reactor with internal feedbacks which lead to the nonlinearity of the overall system. Both are shown stable with new representation of the system, which corresponds to the nonminimal system representation, in the vein of the model reference adaptive system (MRAS) via the Lyapunov's method. For the sake of the parameter identification, model parameters can be adjusted adaptively as soon as measurements start, while plant parameters can also adaptively be compensated through control input to reduce the output error between the model and the plant for the case of the adaptive control. In the case of the adaptive control, control schemes are presented for two cases, the case of the unknown decay constant of the delayed neutron and the case of the known constant. The adaptive control scheme for the latter case is shown extremely simpler than that for the former. Furthermore, when plant parameters vary slowly with time, computer simulations show that the proposed adaptive control scheme works satisfactorily enough to stabilize an unstable reactor and that it does even in the noise with small variance.
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