Search Results (6)

The experimentation of state observers for the reconstruction of the angular velocity of the links of a flexible industrial manipulator is investigated in this paper, in the presence of unmodeled or uncertain parts. Considering only one axis moving at a time, a study is done to understand how faithfully the dynamics of the machine can be reconstructed using simple single axis models, extending them to take into account the multi-variable dynamics of the system and trying to reconstruct the action of non-linear friction as well. The goal is to show how a good estimate of the interactions between the links can be obtained, with the final aim of including it into a control architecture. Various models of different complexities have been tested with both the asymptotic Luenberger observer and the steady-state Kalman filter. The presence of friction is taken into account by a feedforward compensation or by the addition of a disturbance observer synthesized as a pole placement regulator. First, the observers are tested in simulation, then on real data from a Comau Racer 7-1.0 robot. To evaluate the quality of the reconstruction, a virtual sensor obtained from the identification of the manipulator is used, and then a final test is carried out using a real Xsens gyroscope. An accurate analysis of the achieved results is provided, devoting a particular attention to the trade-off between model complexity, estimate accuracy and computational burden in view of a possible future insertion into the control architecture of an industrial robot. Full article

This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regression method. Grammar-guided genetic programming combined with regularized least squares was applied to identify a nonlinear autoregressive model with an exogenous input (NARX) prediction model of the crane dynamics from input–output data. The resulting prediction model was implemented in the NMPC scheme, using a particle swarm optimization (PSO) algorithm as a solver to find an optimal sequence of the control actions satisfying multi-objective performance requirements and input constraints. The feasibility and performance of the controller were experimentally verified using a laboratory crane actuated by AC motors and compared with a discrete-time feedback controller developed using the pole placement technique. A series of experiments proved the effectiveness of the controller in terms of robustness against operating condition variation and external disturbances. Full article

The smart transformer has been widely applied for the integration of renewables and loads. For the smart transformer application, the voltage control of low-voltage inverter is important for feeding the load. In this paper, a multi-objective optimization control design approach which comprehensively considers all aspects of indexes, such as linear quadratic (LQ) index, H_∞ norm, and closed-loop poles placement, is proposed based on the linear matrix inequality (LMI) solution. The proposed approach is able to alleviate the weight of the designer from the tedious design process of the multiple resonant controllers and the selection of the weighting matrix for the LQ control. Besides that, some excellent performances such as fast recovering time, low total harmonic distortion (THD) and high robustness are achieved by the proposed approach. The THD are 0.5% and 1.7% for linear and non-linear loads, respectively. The voltage drop for linear load step is reduced to 10 V. The proposed approach is applied to a 5 kVA three-phase inverter to yield an optimal control law. Results from the simulation and experiment presented herein will illustrate and validate the proposed approach. Full article

In this paper, a robust fuzzy controller for stochastic nonlinear systems subject to multiple performance constraints is discussed. To solve the problem of stochastic behaviors in nonlinear systems, the covariance control theory and passive control theory are applied based on the concept of energy. Additionally, the pole placement method is considered for the better transient behaviors of the system responses. However, it is known that the control inputs and maximum overshoot of the system responses may become bigger at the same time when the settling time or converge rate of the system is required to be faster. Due to this reason, the input constraint is also considered in the fuzzy controller design method to limit the value of the control gain. Moreover, an effective robust control method is applied to deal with the perturbation of the nonlinear systems. Based on the above performance constraints, the sufficient conditions can be obtained to achieve the stability in the sense of mean square and the multi-performance requirements. Finally, simulation results of the nonlinear synchronous generator system are presented to verify the feasibility and efficiency of the proposed control method. Full article

Finite control set-model predictive torque control (FCS-MPTC) depends on the system parameters and the weight coefficients setting. At the same time, since the actual load disturbance is unavoidable, the model parameters are not matched, and there is a torque tracking error. In traditional FCS-MPTC, the outer loop—that is, the speed loop—adopts a classic Proportional Integral (PI) controller, abbreviated as PI-MPTC. The pole placement of the PI controller is usually designed by a plunge-and-test, and it is difficult to achieve optimal dynamic performance and optimal suppression of concentrated disturbances at the same time. Aiming at squirrel cage induction motors, this paper first proposes an outer-loop F-ETFC-MPTC control strategy based on a feed-forward factor for electromagnetic torque feedback compensation (F-ETFC). The electromagnetic torque was imported to the input of the current regulator, which is used as the control input signal of feedback compensation of the speed loop; therefore, the capacity of an anti-load-torque-disturbance of the speed loop was improved. The given speed is quantified by a feed-forward factor into the input of the current regulator, which is used as the feed-forward adjustment control input of the speed controller to improve the dynamic response of the speed loop. The range of the feed-forward factor and feed-back compensation coefficient can be obtained according to the structural analysis of the system, which simplifies the process of parameter design adjustment. At the same time, the multi-objective optimization based on the sorting method replaces the single cost function in traditional control, so that the selection of the voltage vector works without the weight coefficient and can solve complicated calculation problems in traditional control. Finally, according to the relationship between the voltage vector and the switch state, the virtual six groups of three vector voltages can be adjusted in both the direction and amplitude, thereby effectively improving the control performance and reducing the flow rate and torque ripple. The experiment is based on the dSPACE platform, and experimental results verify the feasibility of the proposed F-ETFC-MPTC. Compared with traditional PI-MPTC, the feed-forward factor can effectively improve the stability time of the system by more than 10 percent, electromagnetic torque feedback compensation can improve the anti-load torque disturbance ability of the system by more than 60 percent, and the three-vector voltage method can effectively reduce the disturbance. Full article

This paper presents experimental identification and vibration suppression of a flexible manipulator with piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined where the move distance of the closed loop poles is the shortest. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Accordingly, multi-mode vibrations of the manipulator are well attenuated. Full article