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Control load system is an important equipment of flight simulator. The basic requirement of the controller is that it can control the model force needed for output and good dynamic characteristics. In this paper, the mathematical model of the control load system is studied. In order to solve the problem of poor universality and difficult parameter tuning of the existing control load system, a control algorithm of flight simulator general control load system based on the combination of PID control algorithm and BP neural network is proposed. The algorithm is called PIDNN algorithm. This paper introduces the arithmetic principle and weight updating method of PIDNN, and designs the controller and chooses the parameters according to the PID neural network. The system model is built with MATLAB/SIMULINK and the simulation experiment is carried out. The simulation results show that the controller based on the PIDNN algorithm has excellent performance. Through the control algorithm, the feedback feel of the output is more accurate and more versatile, which frees the developer from the trouble of poor consistency of parameter setting and control quality. It provides a solution for the design of general control load system controller of small aircraft simulator.

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 temperature of agricultural film unit affects the plastic film directly. Since unit heating process has the characters of time delay, nonlinear, time-varying and strong coupling. It is difficult to create a mathematical model structure of plastic melting process. Thus, temperature control is very difficult. This paper presents decoupling control strategy and corresponding control algorithm based on PID (proportional-Integral-differential) neural network. Proportional, integral, differential neurons form a three-layer neural network. This design gives full play to respective advantages of PID control and neural network, and takes advantage of BP neural network to establish the dynamic model of system.

A decentralized proportional-integral-derivative neural network (PIDNN) control scheme is proposed to regulate and stabilize a fully suspended five degree-of-freedom (DOF) active magnetic bearing (AMB) system which is composed of two radial AMBs (RAMBs) and one thrust AMB (TAMB). First, the structure and operating principles of the five-DOF AMB system are introduced. Then, the adopted differential driving mode (DDM) for the drive system of the AMB is analyzed. Moreover, due to the exact dynamic model of the five-DOF AMB system is vague, a decentralized PIDNN controller is proposed to control the five-axes of the rotor simultaneously in order to confront the uncertainties including inherent nonlinearities and external disturbances effectively. Furthermore, the connective weights of the PIDNN are trained on-line and the convergence analysis of the regulating error is provided using a discrete-type Lyapunov function. Based on the decentralized concepts, the computational burden is reduced and the controller design is simplified. Finally, the experimental results show that the proposed control scheme provides good control performances and robustness for controlling the fully suspended five-DOF AMB system in different operating conditions.

According to the time delay in industrial control objects, the PID neural network control method and Smith predictor compensation principle are combined to form the PIDNN-Smith control algorithm. Namely, in Smith predictor compensation control system, the PIDNN as the controller, using the PIDNN neural network on-line self-learning function to tune weight value, make the implicit layers of proportion, integral and differential neurons to achieve the best combination, thus overcome disadvantages of the conventional PID algorithm that does not adapt to the control of large delay system and conventional Smith algorithm depended too much on model precision of the defect. Simulation results show strong robustness and good control quality of this algorithm.

In this paper, traditional PID neural network (PIDNN) control theory was introduced and the characteristics and disadvantages of the algorithm was been analysis. An improvement PIDNN method based on multi-innovation theory was proposed. Then the steward tracking control system was simulated, some planning trace was given out and the math model of the control system was established. The result showed that the improvement algorithm is effect.

A mathematical model of engine throttle as the controlled object is established and then the neural network algorithms and PID control are combined. With the self -learning function of the neural network, self -tunings of PID parameters are realized. The method overcomes disadvantages of PID as parameters which are difficult to determine and embodies better intelligence and robustness of the neural network, the simulation is researched by Matlab and the results show that the PID neural network controller is more accurate and adaptive than conventional PID.

In view of the serious misalignment, the mathematical model's uncertainty, the characteristic of the system operating point changing fiercely of the heating furnace temperature control system, this paper designed a new type of intelligent control system - PID neural network system (PIDNN) which melt the PID control rule in the neural network. This system has the quick reasoning speed and the strong anti-interference ability.

Because it is difficult for the traditional PID algorithm for nonlinear time-variant control objects to obtain satisfactory control results, this paper studies a neuron PID controller. The neuron PID controller makes use of neuron self-learning ability, complies with certain optimum indicators, and automatically adjusts the parameters of the PID controller and makes them adapt to changes in the controlled object and the input reference signals. The PID controller is used to control a nonlinear time-variant membrane structure inflation system. Results show that the neural network PID controller can adapt to the changes in system structure parameters and fast track the changes in the input signal with high control precision.

In order to overcome the system nonlinear instability and uncertainty inherent in magnetic bearing systems, two PID neural network controllers (BP-based and GA-based) are designed and trained to emulate the operation of a complete system. Through the theoretical deduction and simulation results, the principles for the parameters choice of two neural network controllers are given. The feasibility of using the neural network to control nonlinear magnetic bearing systems with un-known dynamics is demonstrated. The robust performance and reinforcement learning capability in controlling magnetic bearing systems are compared between two PID neural network controllers.

In this paper, a novel adaptive tuning method of PID neural network (PIDNN) controller for nonlinear process is proposed. The presented method utilizes an improved gradient descent method to adjust PIDNN parameters where the margin stability will be employed to get high tracking performance and robustness with regard to external load disturbance and parameter variation. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.

The deaerator pressure and deaerator water level are intercoupling in marine steam power plant. Traditional PID control strategy is difficult to get satisfactory control effect. We must take corresponding decoupling measures. This paper proposes a deaerator pressure and deaerator water level decoupling control strategy based on PID neural network, with which we can make comprehensive utilization of the advantage of both PID and neural network. Results of the simulation show that compared with traditional PID control strategy, the PID neural network decoupling control strategy can provide more stability and faster response speed in deaerator pressure and deaerator water level control.

As a new kind of neural network model, Neural network PID (PIDNN) combines the advantages of PID and neural network. However, the error back propagation algorithm (BP) limits the performance of PIDNN. In order to realize effective control of nonlinear, large delay and strong coupling system, this paper proposes a neural network PID control method based on chaos particle swarm optimization. Using chaos particle swarm algorithm to replace the reverse pass algorithm of original PID neural network, adjusting the weights of PIDNN between each neuron, the algorithm achieved rapid decoupling control effect. The simulation results show that the proposed method in this paper, compared with the original BP algorithm, has more excellent dynamic and steady-state performance.

PID neural network (PIDNN) is a new type of feed-forward neural network which has been found by Huai-lin Shu, in which neural network is integrated with PID control law. The priori knowledge of PID control can be used to choose the initial weights to make sure that PIDNN control systems are initial stable. Without priori knowledge and using the random initial weights, initial stable of the PIDNN system may be uncertain. The paper proposes an improved algorithm which has momentum factor to overcome the local minimum of the PIDNN. The distinct improvement of the PIDNN control system is proved by simulation results.

The deaerator pressure and deaerator water level are intercoupling in marine steam power plant. Traditional PID control strategy is difficult to get satisfactory control effect. We must take corresponding decoupling measures. This paper proposes a deaerator pressure and deaerator water level decoupling control strategy based on PID neural network, with which we can make comprehensive utilization of the advantage of both PID and neural network. Results of the simulation show that compared with traditional PID control strategy, the PID neural network decoupling control strategy can provide more stability and faster response speed in deaerator pressure and deaerator water level control.

PID neural network (PIDNN) is a new kind of networks. It consists of three layers and its hidden layer's units are proportional (P), integral (I) and derivative (D) neurons. PIDNN's weights are adjusted by the back-propagation algorithms and it perform a perfect function in process control. In this paper, we introduce PIDNN structure and algorithm and give examples in which PIDNN is used to control time-delay systems.

Since the CPT system is a complex higher-order nonlinear system, it is very difficult to meet the control requirements if the traditional control method is used to control the stability of the system. In this paper, a performance simulation study is performed by introducing the PID neural network controller into the Phase-Shifted Full-Bridge CPT system. Compared with the control effects of traditional PID controllers, the PID neural network controllers have better dynamic responses and more robustness under load rapid changes and input step disturbances.

By using Lagrange equations, the mathematical model and simplified model of the ball and plate system are derived effectively. Base on this simplified model, a PID control strategy based RBF neural network tuning scheme is proposed for position control of the ball and plate system, and simulation study is made. The simulation results show that the control scheme given in this paper is feasible, the output of the system can track the set point value approximately.

In order to solve the strong coupling problem existing between the frequency converter-fan-pressure difference loop and the return air damper-CO2 content loop in the pneumatic membrane structure inflation system, this paper studies PID neural network decoupling control algorithm based on neural network theory and PID theory, establishes double-variable dual-output PID neural network decoupling control system model. The application results show that the PID neural network decoupling control algorithm is effective on decoupling of two loops of pneumatic membrane structure inflation system, gets better control effect, and improves the system real-time control.

According to the combustion control system of industry boiler with large time delay and nonlinear, a kind of combustion system of industry boiler based on GA PID neural network controller design method is proposed in this paper. The boiler model is given and the comparison is made between GA PID neural network controller and the traditional PID controller. The simulation results indicate that the new controller has good performance of strong robustness and disturbance resisting ability than traditional PID controller.

A PID neural network is proposed to solve the decoupling problem of multi-cylinder forging machine. Based on analyzing forging machine's model the PID neural network is determined as 10-15-5 structure. 10 input- neurons connect the given value and the measure value of left front cylinder, left posterior cylinder, right front cylinder, right posterior cylinder and main cylinder.15 neurons of hidden layer respond 5 groups of P-neuron, I-neuron and D-neuron. And 5 output-neurons connect 5 circuit controllers. The learning process uses BP algorithm. The simulation shows this approach is effective.

An improved PID neural network-based controller is designed and analyzed for the inverted pendulum system. In order to deal with the local minimum problem in training neural network with backpropagation algorithm and to enhance controlling precision, neural network's weights are adjusted by optimization algorithm. The controller employs a PID neural network instead of estimating the unknown plant nonlinearities on-line. The simulation results show that the proposed controller with improved PID neural network is flexible and efficient in the control of inverted pendulum system.

PIDNN (proportional, integral and derivative neural network) was first created by the author in 1997. In this paper, the author analyzes the characteristics of the temperature system of the plastic injecting-moulding machine and the performances of the PIDNN control system. The simulation results for the three-stage heater in a plastic injection machine are shown. It is proved that the PID neural network has perfect decoupling and self-learning control performances.

In this paper, the proportional (P) neuron, integral (I) neuron and derivative (D) neuron are introduced and a new neural network: PID neural network (PIDNN) is built. Based on the PID neural network, a multivariable system which has 3 inputs and 2 outputs is discussed and the paper displays the perfect performances of the PIDNN for the unsymmetry multivariable system.

The PID (proportional-integral-derivative) neural network (PIDNN) controller for nonlinear system is proposed in this paper, which is different from the hybrid controllers of the PID and the neural networks. The PID neural network is a new kind of multilayer neural network in which the neurons have proportional, integral and derivative functions. And the controller is optimized by the learning processing of PIDNN. The principle of PIDNN is discussed and the learning method based on back-propagation-algorithm is given. The simulation results show that the proposed controller has the adaptability, strong robustness and satisfactory control performance in the nonlinear and time varying system

In this paper, the authors advance some new opinions about neural networks (NNs). A new kind of NN, the PID neural network (PIDNN), is introduced. A PIDNN consists of three layers and its hidden layer consists of P neurons, I neurons and D neurons. The PIDNN's weights are adjusted by back-propagation algorithms and a PIDNN can be used to control multivariable systems. The authors give the functions of proportional (P) neurons, integral (I) neurons and derivative (D) neurons and present simulation results of a multivariable control system based on the PIDNN.