Force Control of Electrical Load System Based on Single Neuron PID Adaptive and Repetitive Control

In this paper, an adaptive single neuron-based PID controller for the Triaxial and Torsional Shear Apparatus is proposed. Considering variations of the control precision, the single neuron adaptive PID controller is used to construct the control system to achieve the adaptive control of triaxial and torsion testing. The single neuron adaptive PID controller using hybrid Supervised-Hebb rule is proposed to tune control parameters. The testing results of actual application show that the single neuron adaptive PID controller makes better improvement in the control precision and robustness of control system.

Hysteresis nonlinearity in the piezoelectric material could decrease control performance of piezoelectric cantilever beam. A control strategy that combines feedforward inverse compensator with single neuron adaptive PID controller is proposed in this paper. A Hammerstein dynamic hysteresis model for flexible piezoelectric cantilever beam is given. In order to improve the control accuracy, a single neuron adaptive PID controller is incorporated with feedforward compensator. Real-time trajectory tracking control experimental results show that compared with the controller without feedforward compensator, tracking accuracy is greatly improved by combining the feedback loop with hysteresis compensation in the feedforward loop.

Radial basis function (RBF) neural network (NN) is powerful computational tools that have been used extensively in the areas of pattern recognition, systems modeling and identification. This paper proposes an adaptive PID control method based on RBF neural network identification. This approach can on-line identify the controlled plant with the RBF neural network identifier and the weights of the adaptive PID controller are adjusted timely based-on the identification of the plant and self-learning capability of RBFNN. Simulation result shows that the proposed controller has the adaptability, strong robustness and satisfactory control performance in the nonlinear and time varying system.

<div class="section abstract"><div class="htmlview paragraph">Amphibious vehicles with both land and water navigation functions have extremely high application value in the military and civilian fields. In order to fully utilize the wheel driving force and ensure the smooth landing of the amphibious vehicle driven by four wheel hub motor, an acceleration slip regulation (ASR) is designed under the condition of landing from water. First, the road friction coefficient is identified based on the back propagation neural network (BPNN). Then, utilizing the improved Burckhardt model, the current optimal slip ratio is calculated from the identified road friction coefficient. Finally, the ASR under the condition of landing from water is designed based on radial basis function (RBF) single neuron adaptive PID control algorithm. By analyzing the process of amphibious vehicles transitioning from water to land, a typical working condition for amphibious vehicles landing is established, and a joint simulation is conducted using CarSim/Simulink. The simulation results indicate that, using the BPNN, the road friction coefficient of the typical working condition can be accurately identified. Compared to without ASR, after applying the ASR designed in this paper, the landing time of the amphibious vehicle is reduced by 7.5 s, and the final climbing velocity is increased by 7 km/h. After applying the ASR, the power performance of the amphibious vehicle during landing is improved, demonstrating that this ASR has significant practical application value.</div></div>

In this paper, the problem of adaptive control is investigated for a class of non-strict feedback stochastic nonlinear systems with input delay. First, the effect of the input delay is eliminated by constructing an appropriate auxiliary system with the same order as the considered system. Then, with the help of the backstepping technique and the structural characteristics of the radial basis function (RBF) neural network (NN), an adaptive neural control scheme is extended to non-strict feedback stochastic nonlinear systems with input delay, in which uncertain nonlinear functions are approximated by RBF NN. Furthermore, the proposed adaptive controller ensures that all the closed-loop signals remain bounded in probability. Finally, two examples are provided to confirm the effectiveness of the designed strategy.

In this paper, a new grid current control strategy based on internal model control and repetitive control is designed. The performance characteristic and operation principle are analyzed according to the basic principle of internal model control and the dynamic mathematical model of three-phase photovoltaic grid-connected inverters. Because the harmonic current caused by the dead-time effect and the periodic disturbance of the grid can not be eliminated by internal model control, a composite control strategy with internal model control and repetitive control is proposed in the paper. Internal model control can simplify the controller’s parameters and ensure the dynamic quality of the system. Repetitive control is able to restrain the dead-time effect and the periodic disturbance of the grid. It can also reduce the total harmonic distortion of the grid-connected current. The simulation results show that the proposed control strategy has good dynamic and steady-state performance and can restrain the periodic disturbance as well.KeywordsThree-phase grid-connected invertersInternal model controlRepetitive controlDead-time effect

Fault diagnosis for wireless sensor is very important to ensure signal acquisition precision. Radial basis function (RBF) neural network has strong classification ability. However, the selection of the connection weights, the hidden centers and the widths has an important influence on the classification performance of the RBF neural network in the learning process of RBF neural network. Thus, ant colony optimization is employ to gain the parameters of radial basis function neural network. Therefore, a novel method for fault diagnosis of wireless sensor based on RBF neural network and ant colony optimization is presented. The results of computational experiments show that ACO- RBF neural network has a great higher than RBF neural network.

Model reference adaptive sliding mode control (MRASMC) using radical basis function (RBF) neural network (NN) is proposed to control the single-phase active power filter (APF). The RBF NN is utilized to approximate nonlinear function and eliminate the modeling error. AC side model reference adaptive current controller not only guarantees the globally stability of the APF system but also generate the compensating current to track the harmonic current accurately. Moreover, a sliding mode controller based on exponential approach is designed to improve the tracking performance of DC side voltage. Simulation results demonstrate that MRASMC using RBF NN can improve the adaptability and robustness of the APF system and track the given instructional signal quickly.

An adaptive feedback control scheme and a switching strategy for the output tracking stabilization of switched nonlinear systems with time-delay are presented by using RBF (radial basis function) neural network The RBF neural networks is used to compensate adaptively for the unknown nonlinear parts of switched nonlinear systems, and the approximation errors of RBF neural networks are introduced to the adaptive law in order to improve the tracking quality of the systems. The controller and switching strategy can guarantee asymptotic stability of tracking error for the overall system

A force/position hybrid adaptive control method based on radial basis function(RBF) neural network is proposed to solve the problem of difficulties to precisely describe the compliance and friction for robot terminal during trajectory tracking process.RBF neural network is an efficient feed-forward neural network with non-linear approximation and global optimization characteristics,which is not provided by other feed-forward networks,which is simple in network structure,and rapid in training speed.An adaptive controller is designed that relies on the nonlinear approximation ability of the RBF neural network to estimate the uncertainty factors in the models,the update rules for the weights of the controller neural network is provided and its finally uniform boundedness of the errors of the controller output force and position is proved.The controller is applied to a duct cleaning robot for simulation experiments.Simulation results shows that the adaptive controller demonstrates superior tracking precision and robustness compared with traditional adaptive controller.

Magnetic levitation rotating machinery has been widely used in industrial applications. Odd harmonic currents in the active magnetic bearing coils, which are caused by rotor mass unbalance and sensor runout, increase the power consumption and the base vibration. Repetitive control has recently been used to suppress harmonic currents in magnetic bearing systems. However, repetitive control needs the rotor rotational speed information and has some limitations without a speed sensor. In this paper, an adaptive odd repetitive control which does not need the speed sensors is proposed to suppress the active magnetic bearing odd harmonic currents. Compared to the conventional repetitive control, the second-order generalized integral frequency-locked loop is applied in adaptive odd repetitive control to estimate the rotor speed in real time without additional speed sensors. The magnetically suspended rotor with harmonic disturbances is modeled. The work principle, structure, and design of the adaptive odd repetitive control are addressed. The simulation and experiments are carried out on a rotor active magnetic bearing test rig to validate the effectiveness of the proposed adaptive odd repetitive control, and the results show that the active magnetic bearing odd harmonic currents are well suppressed.

PID control was commonly used for control system which often depend on accurate mathematical models, but in some complicated situations, the model are hard to obtain and the plant parameters are subject to perturbations, the application of PID control are limited and adaptive schemes should be taken. BP neural networks with good model identification and can be used to satisfied PID parameters through self learning, with its adaptive learning strategy the need for a computationally intensive process was eliminated and robust control can realized. The structure of single neuron adaptive PID control system was designed and adaptive algorithm was proposed, the system was simulated by the algorithm and improved algorithm, the simulation result show the improved adaptive algorithm is feasible and the control characteristic realized.

In the process of moisture content's measurement with microwave resonant cavity based on RBF (radial basis function) neural network, the relationship among resonant frequency, quality factor and environmental temperature (the inputs of network) and moisture content (the output of network) is multiple non-linear. The performance of multiple non-linear regression algorithm is the major factor that determines the accuracy of measurement. A regression algorithm based on a RBF neural network is put forward to improve the measurement result in this paper. The RBF neural network regression algorithm can effectively avoid the BP algorithm disadvantages such as getting into local minimum and the low efficiency, and has the advantages such as high generalization precision and rapid convergence. Consequently, the measurement accuracy is enhanced. In this paper, experimental results show that the high-precision measurement value of moisture content can be obtained with the multiple non-linear regression algorithm based on RBF neural network.

This paper is concerned with the problem of adaptive neural control for uncertain nonlinear strict-feedback time-delay systems with unknown virtual control coefficients. Radial basis function (RBF) neural networks are employed to directly approximate unknown virtual control signals, and then the adaptive neural control law is constructed by Lyapunov-Krasovskii functionals and backstepping. In order to avoid encountering a large number of adaptive parameters when using RBF neural networks as function approximators, an unknown constant, instead of unknown neural weights themselves, is employed as the estimated parameter. This technique makes only one adaptive parameter tuned online, thus significantly alleviating the burdensome computation. Meanwhile, some continuous functions are introduced to overcome the design difficulty originating from the use of one adaptive parameter. The proposed adaptive control guarantees the boundedness of all the signals in the closed-loop system. Simulation studies are presented to illustrate the effectiveness of the scheme.

In this paper, two novel adaptive finite-time control schemes are proposed for position tracking of nonlinear teleoperation system, which dynamic uncertainties, actuator saturation, and time-varying communication delays are considered. First, a novel auxiliary variable is designed to provide more stable performance. The radial basis function (RBF) neural network is introduced to estimate dynamic uncertainties. Second, two adaptive finite-time control schemes are investigated. In control scheme I, the RBF neural network and the gain switching strategy are applied to compensate the actuator saturation. In control scheme II, an auxiliary compensation filter and the compensation adaptive update laws, which contain the finite-time structure, are developed for dealing with saturation. Third, the finite-time adaptive controller is designed in each of these two control schemes. Based on the multiple Lyapunov function method, the closed-loop teleoperation system with these two control methods is proved to be bounded and finite-time stability. Finally, the simulation experiments are performed and the comparisons with other control methods are shown. The effectiveness of the proposed control schemes is demonstrated.