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In order to solve the problems of the large number of the power switches, high voltage and current stress, and double-line-frequency ripple on the dc-side current in the existing single-stage high-frequency isolated dc-ac converters, the novel single-stage isolated dc-ac topology is proposed in this paper. The interleaved parallel structures are used on both the dc and grid sides. The harmonic components of the currents in both the dc and grid sides are suppressed and the number of the power switches is reduced. Furthermore, based on the dual PWM method with the phase-shift modulation strategy, the operation principles, and characteristics of the proposed converter are analyzed thoroughly. In order to further achieve the suppression of the harmonic currents on both the dc and grid sides, the composite controllers based on the P-type iterative learning are proposed. By the composite controller, each harmonic current is effectively suppressed regardless of the harmonic frequency. The stability, convergence, and operation characteristics of the controllers are analyzed meticulously. The correctness and effectiveness of the proposed converter and closed-loop control strategy are validated through the experimental results.

This paper centers around an experimental investigation into the effectiveness of an innovative hybrid control approach based on an intelligent active force control (IAFC) strategy to stabilize a twin-rotor helicopter model and improve its ability to reject external disturbances efficiently. The intelligent algorithm was based on an iterative learning (IL) method integrated into the main control loop to estimate control parameters automatically while on-line. A mechatronic test rig with the IAFC-based control algorithm was incorporated into a Quanser Aero twin-rotor model in a laboratory setting as a verification platform to evaluate the applicability and efficacy of the proposed control algorithm via a practical real-time implementation. The hybrid IAFC-based control design was rigorously examined to test its feasibility and durability in countering various forms of external disturbances while executing the trajectory tracking tasks. Notably, the efficiency of the IAFC-based control unit was mainly studied and compared with other control plans under different operating conditions for benchmarking. The experimental results show the ability of the controller based on the IAFC strategy to effectively improve the disturbance rejection capability compared to the other control schemes considered in the study. About 27% improvement of the system performance in terms of lowering the root mean square error (RMSE) was observed compared to the other control systems counterparts.

This work presents an innovative hybrid control scheme for a quadrotor unmanned aerial vehicle (UAV) model to improve the disturbances rejection capability and body jerk performance by utilizing an active force control (AFC)-based robust intelligent control system via a simulation study. The proposed intelligent control approach incorporates a proportional-integral-derivative (PID) and an intelligent active force control (IAFC) element yielding a robust PID-IAFC scheme. A detailed mathematical model of a quadrotor system with six degrees of freedom (DOFs) was first derived using the Newton-Euler method taking into consideration the gyroscopic terms, disturbances, aerodynamics, and friction effects. In the derived model, the PID controller was first designed to stabilize the quadrotor model and achieve the required altitude and attitude motions. In addition, different types of external disturbances in the form of sinusoidal waves and repeated impulses (pulsating) were added. An AFC strategy, known as PID-AFC, was designed and incorporated into the PID controller, and was initially tuned heuristically. Then, an artificial intelligence (AI)-based method employing an iterative learning (IL) algorithm was designed and implemented into the AFC (ILAFC) to estimate the control parameters automatically while on-line. Thereafter, the performance of the ILAFC was compared to the AFC with fuzzy logic (FL) which became known as FLAFC. Also, a self-tuning (ST) PID controller was designed and employed based on the FL method to automatically tune the PID gains based on the prescribed operating and loading conditions. Moreover, a comparative study of the system performance was carried out utilizing the PID, PID-AFC, ILAFC, FLAFC, and ST-FPID-AFC schemes to analyze the system characteristics. Furthermore, the effectiveness of the AFC-based intelligent controller was investigated in connection with the body jerk performance in the presence of external disturbances. The simulated results reveal the effectiveness and robustness of the proposed control strategy based on the IAFC technique in improving the disturbance rejection capability and body jerk performance by 17% in the presence of uncertainties and external disturbances.

In the process of multi-axis contour tracking control, the traditional time-invariant method could lead to a significant error in contour tracking due to the existence of two different motion conditions, namely single-axis independent motion and multi-axis coupled motion. In order to tackle this issue, a time-varying weighting matrix has been developed considering the trajectory and time-varying random disturbance. In this paper, a time-varying control method for multi-axis motion based on norm optimal cross-coupling iterative learning is proposed. Compared to the time-invariant control method, the simulation and experiment results demonstrate that the proposed method can effectively reduce the contour error improving the multi-axis control precision.

This paper investigates the interaction phenomena of the coupled axons while the mutual coupling factor is presented as a pairwise description. Based on the Hodgkin-Huxley model and the coupling factor matrix, the membrane potentials of the coupled myelinated/unmyelinated axons are quantified which implies that the neural coupling can be characterised by the presented coupling factor. Meanwhile the equivalent electric circuit is supplied to illustrate the physical meaning of this extended model. In order to estimate the coupling factor, a data-based iterative learning identification algorithm is presented where the Rényi entropy of the estimation error has been minimised. The convergence of the presented algorithm is analysed and the learning rate is designed. To verified the presented model and the algorithm, the numerical simulation results indicate the correctness and the effectiveness. Furthermore, the statistical description of the neural coupling, the approximation using ordinary differential equation, the measurement and the conduction of the nerve signals are discussed respectively as advanced topics. The novelties can be summarised as follows: 1) the Hodgkin-Huxley model has been extended considering the mutual interaction between the neural axon membranes, 2) the iterative learning approach has been developed for factor identification using entropy criterion, and 3) the theoretical framework has been established for this class of system identification problems with convergence analysis.

This paper presents an approach of the force/position hybrid control for a hexa parallel robot to guarantee a safe and accurate interaction when touching the object surface. A double-loop PID controller is proposed to replace the common PID controller in the position control to eliminate position errors due to the dynamics coupling effect between the arms and the vibration of the mechanical system. An impedance control model is used to guarantee a safe and accurate interaction when touching the object surface. In addition, a gradient descent iterative learning control algorithm is used and modified to determine the optimal impedance parameters in unknown environments. A model of the robot is built in SimMechanics to simulate and estimate system parameters. After that, the experimental work was conducted on a real robot to verify the effectiveness and feasibility of the proposed method.

This work explores the problem of observing nonrepetitive disturbances under an almost data-driven framework. First, a linear data model among the inputs, states, and outputs is built for a repetitive system (linear or nonlinear) between two consecutive iterations where the nonrepetitive disturbances are accumulated along time axis as a total one. The accumulative disturbance contains all the influences on the system states or outputs caused by the disturbances from the initial time instant to the current time instant between two consecutive iterations. Furthermore, an iterative updating algorithm is designed to estimate the gradient matrix in the derived linear data model. Subsequently, iterative learning-based accumulative disturbance observer (ILADOB) is proposed employing the state information in the iteration domain when the system states are measurable; otherwise, when the states are immeasurable, an output-based ILADOB is presented as an alternative. The proposed two ILADOB methods are executed along the iteration direction all over the finite time interval pointwisely using the system data from preceding trials. The convergence and stability are proved mathematically. The simulation study confirms the validity of the state-based and output-based ILADOB methods.

In order to meet the rigorous motion accuracy requirement and efficiently utilize the repetitive-task characteristics in modern precision industry, this paper concentrates on the comprehensive research of model-based data-driven learning adaptive robust control (LARC) strategy for precision mechatronic motion systems. The proposed LARC can achieve not only excellent transient/steady-state tracking performance but also adaptation ability and disturbance robustness. Specifically, the LARC strategy contains robust feedback term, adaptive model compensation term, and iterative learning term. Herein, the former two terms are designed based on the system dynamic model under parametric uncertainty and uncertain nonlinearity, and the data-driven iterative learning term is synthesized to generate optimal input to adjust the optimal reference. The whole controller design procedure and stability is presented, while the reason for the practically achievable performance of LARC is analyzed. Comparative experiments, among proportional-integral-differential, adaptive robust control, iterative learning control, and the proposed LARC, are conducted on a developed linear motor stage. The experimental results consistently validate that the proposed LARC scheme simultaneously achieves excellent transient/steady-state tracking performance, parametric adaptation ability, and disturbance robustness. The LARC strategy essentially provides an effective control technology with good potential in industrial applications.