Neural PID Controller Research Articles

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

This paper proposes an adaptive switch controller (ASC) design for the nonlinear multi-input multi-output system (MIMO). In fact, the proposed method is an online switch between the neural network adaptive PID (APID) controller and the neural network indirect adaptive controller (IAC). According to the design of the neural network IAC scheme, the adaptation law has been developed by the gradient descent (GD) method. However, the adaptive PID controller is built based on the neural network combining the PID control and explicit neural structure. The strategy of training consists of online tuning of the neural controller weights using the backpropagation algorithm to select the suitable combination of PID gains such that the error between the reference signal and the actual system output converges to zero. The stability and tracking performance of the neural network ASC, the neural network APID, and the neural network IAC are analyzed and evaluated by the Lyapunov function. Then, the controller results are compared between APID, IAC, and ASC, in this paper, applying to a nonlinear system. From simulations, the proposed adaptive switch controller has better effects both on response time and on tracking performance with smallest MSE.

Decoupling Fuzzy-Neural Temperature and Humidity Control in HVAC Systems

This paper presents a neuro-fuzzy structure of a decoupling fuzzy neural PID controller with self-tuning parameters. This structure is appropriate for heating, ventilation and air conditioning HVAC nonlinear plants. The main advantage here is that the equation of classical PID control with decoupling coefficients are used as a Sugeno function into the consequent part of the fuzzy rules. Hence, the designed decoupling fuzzy PID controller could be viewed as a natural similarity to the conventional PID controller with decoupling elements. A benchmark HVAC system with temperature and humidity control is considered to illustrate the benefits of the design paradigm. The performance of this set up was studied for reference tracking and disturbance rejection cases. Simulation results confirm the effectiveness of the proposed control system.

RBF neural network tuning PID control based on UMAC

RBF neural network tuning PID control based on UMAC

Motion control of linear induction motor based on optimal recurrent wavelet neural network-PID controller

This paper contains a proposed controller based on optimal recurrent wavelet neural network (RWNN) with PID controller to control the velocity and hence the stator current as well as the developed thrust of three phase linear induction motor (LIM) which consider the end effects. A vector control represented by indirect field oriented control (IFOC) technique is appointed to attain velocity and flux control for different loading conditions. Moreover, a voltage source inverter based on space vector pulse width modulation (SVPWM) is utilized to give the required stator voltage of LIM. A well-known particle swarm optimization (PSO) algorithm is employed for online tuning of the proposed controller. The computer simulation results show that this controller is effective and gives preferable and rigorous performance compared with a results obtained from conventional wavelet neural network (WNN) and PID controllers.

Modified Elman Neural-PID Controller Design for DC-DC Buck Converter System Based on Dolphin Echolocation Optimization

This paper describes a new proposed structure of the Proportional Integral Derivative (PID) controller based on modified Elman neural network for the DC-DC buck converter system which is used in battery operation of the portable devices. The Dolphin Echolocation Optimization (DEO) algorithm is considered as a perfect on-line tuning technique therefore, it was used for tuning and obtaining the parameters of the modified Elman neural-PID controller to avoid the local minimum problem during learning the proposed controller. Simulation results show that the best weight parameters of the proposed controller, which are taken from the DEO, lead to find the best action and unsaturated state that will stabilize the Buck converter system performance and achieve the desired output. In addition, there is a minimization for the tracking voltage error to zero value of the Buck converter output, especially when changing a load resistance by 10%.

Modified Elman Neural-PID Controller Design for DC-DC Buck Converter System Based on Dolphin Echolocation Optimization

This paper describes a new proposed structure of the Proportional Integral Derivative (PID) controller based on modified Elman neural network for the DC-DC buck converter system which is used in battery operation of the portable devices. The Dolphin Echolocation Optimization (DEO) algorithm is considered as a perfect on-line tuning technique therefore, it was used for tuning and obtaining the parameters of the modified Elman neural-PID controller to avoid the local minimum problem during learning the proposed controller. Simulation results show that the best weight parameters of the proposed controller, which are taken from the DEO, lead to find the best action and unsaturated state that will stabilize the Buck converter system performance and achieve the desired output. In addition, there is a minimization for the tracking voltage error to zero value of the Buck converter output, especially when changing a load resistance by 10%.

Modified Elman Neural-PID Controller Design for DC-DC Buck Converter System Based on Dolphin Echolocation Optimization

This paper describes a new proposed structure of the Proportional Integral Derivative (PID) controller based on modified Elman neural network for the DC-DC buck converter system which is used in battery operation of the portable devices. The Dolphin Echolocation Optimization (DEO) algorithm is considered as a perfect on-line tuning technique therefore, it was used for tuning and obtaining the parameters of the modified Elman neural-PID controller to avoid the local minimum problem during learning the proposed controller. Simulation results show that the best weight parameters of the proposed controller, which are taken from the DEO, lead to find the best action and unsaturated state that will stabilize the Buck converter system performance and achieve the desired output. In addition, there is a minimization for the tracking voltage error to zero value of the Buck converter output, especially when changing a load resistance by 10%.

Controller design for upper limb motion using measurements of shoulder, elbow and wrist joints

Passive exercises are implemented by physiotherapists to people who have movement functionality loss due to any reason at their limbs for recovering all or a part of movement functionality. Physiotherapists are constantly repeating the passive therapy movements with patients. Nowadays, rehabilitation devices which are capable of repeating therapy exercises and control techniques for that device are developing. For this reason, controller design for the angular trajectory of human shoulder, elbow and wrist joints is presented in this paper. This paper consists of two parts. At first part, patients who had loss of function at their upper limbs were identified and shoulder, elbow and wrist angular displacement parameters were obtained by means of sensors. At second part, three control structures were selected for tracking shoulder, elbow and wrist angle trajectories. According to the experimental and simulation results, neural network-based PID control structure is the best controller on tracking given trajectories.

Self-tuning of a Neuro-Adaptive PID Controller for a SCARA Robot Based on Neural Network

In this paper a MIMO (Multiple-Input-Multiple-Output) adaptive neural PID (AN-PID) controller that can be applied to a nonlinear dynamics is proposed, and its use is shown in the control of a SCARA robot for two degrees of freedom. The AN-PID controller, including a neural network of the dynamic perceptron type, is designed. The proposed controller uses a RBF network to identify the model and back propagates the control error to the AN-PID controller, unlike other controllers, that use direct methods to back propagate such error. With these properties, an AN-PID controller corrects the tracking errors due to the uncertainties and variations in the robot arm dynamics. It is robust and with adaptive capacity in order to achieve a suitable control performance. Experimental results on the SCARA robot were obtained to illustrate the effectiveness of the proposed control strategy, including comparison with a classical PID. By using Lyapunov's discrete-time theory, it was demonstrated that the control error is semi-global uniformly ultimate bounded (SGUUB).

A novel adaptive feed-forward-PID controller of a SCARA parallel robot using pneumatic artificial muscle actuator based on neural network and modified differential evolution algorithm

This paper proposes a novel control system combining adaptively feed-forward neural controller and PID controller to control the joint-angle position of the SCARA parallel robot using the pneumatic artificial muscle (PAM) actuator. Firstly, the proposed inverse neural NARX (INN) model dynamically identifies all nonlinear features of the SCARA parallel PAM robot. Parameters of the inverse neural NARX model are optimized with the modified differential evolution (MDE) algorithm. Secondly, combining the inverse neural NARX model that provides a feed-forward control value from the desired joint position and the conventional PID controller applied to improve the precision and reject the steady state error in the joint position control. Finally, the new adaptive back-propagation (aBP) algorithm, based on Sugeno fuzzy system, proposed for online updating the weight values of the inverse neural NARX model as to adapt well to the disturbances and dynamic variations in its operation. Experimental tests confirmed the performance and merits of the proposed control scheme in comparison with the traditional control methods.

Application of PID Neural Network Control System Based on Virtual Instrument

The PID neural network controller can make both neural networks and PID control into an organic whole, which has the merit of any PID controller for its simple construction and definite physical meaning of parameters, and also has the self-study and adaptive functions of neural network. The approach to realize neural network control in virtual instrument is researched. Combining virtual instrument technology and PID neural network together organically and adopting LabVIEW to design the module of PID neural network, which will improve performance of the control system and development efficiency.

The application of neural network PID controller to control the light gasoline etherification

Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.

Research on Variable Current Regenerative Braking Control Strategy Based on Radial Basis Function Neural Network Tuning PID Control

Nowadays, regenerative braking is one of the most prevalent and important technology applied in EV (electric vehicle). It can extend the driving range of EV by transforming part of kinetic energy into electric energy during the braking process of EV. The research object of this paper is one certain EV with the hybrid electric power which includes lithium ion battery and ultra-capacitor. This paper establishes the circuit mathematical model of RBS (regenerative braking system) and analyzes the system comprehensively. Based on that, this paper designs a braking force distribution method between the front-axle and rear-axle. Under the premise of braking safety, this paper puts forward a variable current regenerative braking control strategy based on RBF (radial basis function) neural network tuning PID control with the aim of maximum energy recovery. In order to prove the validity of the model and the control strategy, this paper contrasts the variable current regenerative braking control strategy with the traditional constant current braking control strategy by bench test and simulation under NEDC driving cycle. The result shows that the variable current regenerative braking control strategy is effective, improving the energy recovery efficiency, extending the driving range of EV.

Efficient VLSI Implementation of the C-MANTEC Conn Algorithm by Using PID Controllers

Through the research on the existing C-MANTEC neural network and PID control technology, this paper presents an improved C-MANTEC algorithm based on PID control system. The combining of the artificial neural networks with conventional PID control helps in exploring their respective advantages to forming the intelligent PID control. From UCI Repository cancer dataset, the developed system is tested. The results show that the scheme can not only improve the speed of the algorithm in the training process but also improve the generalization capability of the network, which further enhances the performance of PID controllers. The overall power consumed is also reduced to a greater extent.

Adaptive Neural Control Algorithm Design for Attitude Stabilization of Quadrotor UAV

This paper presents a new adaptive neural network control scheme to stabilize the attitude of the quadrotor helicopter. The dynamic model was developed via Newton−Euler formalism. The robust adaptive control is then realized using neural network (NN) algorithm based on a PID controller in the aim to adjust its gain parameters. The proposed algorithm is developed to adapt the structure of the conventional PID controller to a dynamic PID controller. Finally, the proposed controller is compared with that classical PID controller using MATLAB/Simulink. The simulation results show that the neural PID controller produces better performance than the conventional one, particularly in case of perturbation.

Design and Implementation of Adaptive Neural PID for Non Linear Dynamics in Mobile Robots

In this work, it will be reported original results concerning the application of PID Adaptive Neural controller in mobile robot in trajectory tracking control. In this control strategy the exact dynamical model of the robot will not need to be known and identified. To implement this strategy, two controllers are implemented separately: a kinematic controller and an adaptive neural PID controller. The uncertainty and dynamics variations in the robot dynamic are compensated by an adaptive neural PID controller. The resulting adaptive neural PID controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance. The stability of the proposed technique (based on Lyapunov's theory) was demonstrated. Finally, experiments on a mobile robot have been developed to show the performance of the proposed technique, including the comparison with other controllers.

Design of RBF neural network adaptive PID controller of DVD pick-up head

Design of RBF neural network adaptive PID controller of DVD pick-up head

Identification and control of nonlinear dynamics of a mobile robot in discrete time using an adaptive technique based on neural PID

In this work, original results, concerning the application of a discrete-time adaptive PID neural controller in mobile robots for trajectory tracking control, are reported. In this control strategy, the exact dynamical model of the robot does not need to be known, but a neural network is used to identify the dynamic model. To implement this strategy, two controllers are implemented separately: a kinematic controller and an adaptive neural PID controller. The uncertainty and variations in the robot dynamics are compensated by an adaptive neural PID controller. It is efficient and robust in order to achieve a good tracking performance. The stability of the proposed technique, based on the discrete-time Lyapunov's theory, is proven. Finally, experiments on the mobile robot have been developed to show the performance of the proposed technique, including the comparison with a classical PID.

Design of PID Temperature Control System Based on BP Neural Network

Based on BP neural network PID controller has the ability to approximate any nonlinear function, can achieve real-time online tuning PID controller parameter . Through the system simulation analysis, simulation results show that the BP neural network tuning PID control than traditional PID algorithm and BP network algorithm has a greater degree of improvement, the system has better robustness and adaptability, its output can also achieve the desired control accuracy through online adjustments. Suitable for temperature control system.

Real-time implementation of a neural model-based self-tuning PID strategy for oxygen stoichiometry control in PEM fuel cell

This paper proposes a real-time implementable self-tuning PID control strategy to tackle oxygen excess ratio regulation challenge of a proton exchange membrane fuel cell. Controller parameters are updated on-line, at each sampling time, using a not iterative procedure based on an artificial neural network model. The proposed controller takes account of nonlinear behaviors of the process, while avoiding heavy computations.To assess the efficiency and relevance of the proposed strategy, the controller is implemented on-line, experimentally validated on a real fuel cell and compared to the built-in controller. In this aim, several control scenarios are considered to evaluate the controller performance.Experimental results show the excellent tracking capability and disturbances rejection ability of the controller, regardless of the operating conditions. Moreover, compared to the built-in controller the proposed strategy demonstrates better disturbances rejection capability.Overall, the proposed neural model-based self-tuning PID controller appears as an excellent candidate to address the oxygen excess ratio regulation issue.