This paper presents a disturbance-rejection method for a modified repetitive control system with a nonlinearity. Taking advantage of stable inversion, an improved equivalent-input-disturbance (EID) estimator that is more relaxed for system design is developed to estimate and cancel out the influence of the disturbance and nonlinearity in the low-frequency domain. The high-frequency influence is filtered owning to the low-pass nature of the linear part of the closed-loop system. To avoid the restrictive commutative condition and choose a Lyapunov function of a more general form, a new design algorithm, which takes into account the relation between the feedback control gains and the observer and improved EID estimator gains, is developed for the nonlinear system. Furthermore, comparisons with the generalized extended-state observer (GESO) and conventional EID methods are conducted. A clear relation between the developed estimator and the GESO is also clarified. Finally, simulations show the effectiveness and the advantage of the developed method.
This paper deals with the vector control, including both the direct vector control (DVC) and the indirect vector control (IdVC), of induction motors. It is well known that the estimation of rotor flux plays a fundamental role in the DVC and the estimation of rotor resistance is vital in the slip compensation of the IdVC. In these estimations, the precision is significantly affected by the motor resistances. Therefore, online estimation of motor resistances is indispensable in practice.For a fast estimation of motor resistances, it is necessary to slow down the convergence rate of the current estimate. On the other hand, for a fast estimation of the rotor flux, it is necessary to speed up its convergence rate. It is very difficult to realize such a trade-off in convergence rates in a full order observer.In this paper, we propose to decouple the current observer from the flux observer so as to realize independent convergence rates. Then, the resistance estimation algorithm is applied to both DVC and IdVC. In particular, in the application to IdVC the flux observer needs not be used, which leads to a simpler structure. Meanwhile, independent convergence rates of current observer and flux observer yield an improved performance. A superior performance in the torque and flux responses in both cases is verified by numerous simulations.
The objective of this study is to develop a 3D ankle-foot model containing toe expression for designing an AFO (ankle-foot orthosis) with a training function. Two experiments were conducted to (1) show the influence of toes by comparing walking with and without an AFO, and (2) clarify the functions of toes during walking by correlating the activity of the major muscles controlling the ankle and the toes to the sole pressure data during walking. By analyzing the results of these two experiments, the necessary components and conditions of a detailed 3D foot-ankle model for developing an AFO with a training effect were clarified. A model was built and examined with empirical facts, and data were collected from the AFO simulation.
Today, microprocessors are easily obtained, and many digital control methods can be considered and realized. In this paper, at first, we propose a new optimal control method using only input/output data. The result coincides with the series compensator which can be constructed by optimal regulator incorporated with minimal order observer. However, in the proposed method, the total optimality of closed loop system is derived directly from modified optimal regulator theory. However, in the above case, there are some cases where the strong stability are not guaranteed. So, we reformulate the method so that it uses the intersample output data as well, then the strong stability is assured for almost all plants with mild restrictions. Further, the final compensator form is given by an optimal output feedback control. Especially, in the case where the plant is given by the linearlized mechanical system, the above optimal output feedback is always realizable using only 2 samples output data prior to current sample. Therefore, it is a very attractive method for the control of robots, for example.
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