A complex control system based on the fuzzy PID control and state predictor feedback control

A multi-mode adaptive controller was proposed. The controller features in the combination of Bang-bang and Fuzzy PID controls with state predictor. When large error exists, the controller operates in Bang-bang mode, otherwise it works as a fuzzy PID controller. For only few parameters to be adjusted, the real time controlled system achieveed good stability and fast response. Furthermore, the introduction of state observer was also discussed to extend the capability of the proposed controller to the plant with time-delay factors. The classical PID controller and the multi-mode controller were applied to the same second-order system successively. By comparison of the simulation results, the effectiveness of the controller were shown. At last, on electric-wire production line, this approach was practiced to control electric-wire diameter with an additive random disturbance signal. The test result further proved the effectiveness of the multi-mode controller.     

High Performance Position Control System Based on SR-PM Motor

A position control system was developed using a synchronous reluctance permanent magnetic (SR-PM) motor with the field oriented control (FOC). To get satisfactory control performance, a fuzzy adaptive proportion integration (PI) controller was used rather than a conventional PI controller for the position control. The controller tunes the PI parameters online with fuzzy logic, based on the error and the change of the error between the feedback and reference position. The superiority of the fuzzy PI controller compared to conventional controller is illustrated by simulations and experiments which confirm that the fuzzy PI controller effectively restrains overshoot of the position response. As a result, the servo system gives satisfac- tory steady state and dynamic performance.     

Robust direct adaptive fuzzy control for nonlinear MIMO systems

For a class of nonlinear multi-input multi-output systems with uncertainty, a robust direct adaptive fuzzy control scheme was proposed. The feedback control law and adaptive law for parameters were derived based on Lyapunov design approach. The overall control scheme can guarantee that the tracking error converges in the small neighborhood of origin, and all signals of the closed-loop system are uniformly bounded. The main advantage of the proposed control scheme is that in each subsystem only one parameter vector needs to be adjusted on-line in the adaptive mechanism, and so the on-line computing burden is reduced. In addition, the proposed control scheme is a smooth control with no chattering phenomena. A simulation example was proposed to demonstrate the effectiveness of the proposed control algorithm.     

Data acquisition, analysis and applications of multi-sensor integration

This paper presents some key techniques for multisensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection, digital map making. The techniques are synchronization control of multisensor, spacetime benchmark for sensor data, and multisensor data fusion and mining. Firstly, synchronization control of multisensor is achieved through a synchronization control system which is composed of a time synchronization controller and some synchronization subcontrollers. The time synchronization controller can receive GPS time information from GPS satellites, relative distance information from distance measuring instrument and send spacetime information to the synchronization subcontroller. The latter can work at three types of synchronization mode, i.e. active synchronization, passive synchronization and time service synchronization. Secondly, spacetime benchmark can be established based on GPS time and global reference coordinate system, and can be obtained through position and azimuth determining system and synchronization control system. Thirdly, there are many types of data fusion and mining, e.g. GPS/Gyro/DMI data fusion, data fusion between stereophotogrammetry and PADS, data fusion between laser scanner and PADS, and data fusion between CCD camera and laser scanner. Finally, all these solutions presented in paper have been applied to two areas, i.e. landborne intelligent road detection and measurement system and 3D measurement system based on unmanned helicopter. The former has equipped some highway engineering Co., Ltd. and has been successfully put into use. The latter is an ongoing research.     

An Intelligent Adaptive Fuzzy PID Controller Based on Multimodel Control Approach

A novel intelligent adaptive fuzzy PHD controller based on multimodel control approach is presented in this paper.It can improve the system performance of the dynamic time- varying system at various operating conditions.The fuzzy PHD controller is implemented by combining a fuzzy PI with a fuzzy PD controller in a parallel structure. The parameters of the fuzzy PHD controller are linked, via analytical derivation, to the gains of the linear PID controller. The sum of error square is used as performance criterion to locate the model that best reresents the process among the multiple models, The desired control output to drive the process along the desired path is generated only by modifying the output scale factots GU_I and GU_D of the fuzzy PID controller, Among the prescribed models, the control signal of the nearestmmodel to the system is applied. The system can be driven to its original trajectory because of the robustness of the fuzzy PID controller, Computer simulation results show that the adaptiv     

An Adaptive Model and Algorithm for an Electrohydraulic Position Servo System in Auto Gauge Control for Aluminum Hot Tandem Mill

The electrohydraulic servo position control system (HPC) is the core of an auto gap control for a modern fourhigh continuous hot strip mill. The actuators are loaded with considerable external disturbances, rolling forces, and the system stiffness and position accuracy will become hypersensitive to the leakage and the compressibility of the oil. Many efforts have being made to increase the position accuracies. The conventional resources are proportion integral differential (PID) controller. But the PID controller is faint for a considerable external disturbance. In this paper, by introducing the adaptive control strategy of model reference the reciprocities of the system structure with the loads were analyzed. With the Lyapunov energy function, an adaptive control algorithm applied to improve the system accuracy was formulated. The system simulations and the selection of parameters of the model were also discussed. The simulations show the static state errors at 0.02%. Finally, the industrial experimental result was given.     

Temperature modeling and control of Direct Methanol Fuel Cell based on adaptive neural fuzzy technology

Aiming at on-line controlling of Direct Methanol Fuel Cell （DMFC） stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System （ANFIS） identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative （PID） and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.     

Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network

An adaptive fuzzy logic controller （AFC） is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection with a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller （WCC） is also developed to describe the interactions of traffic flow among the adjacent intersections. So the AFC combined with the WCC can be applied in a road network for signal timings. Simulations of the AFC on a real traffic scenario have been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.     

LINGUISTIC SELF-ORGANIZING PROCESS CONTROLLER USING GENETIC ALGORITHM

A linguistic self-organizing controller using genetic algorithm is presented, whose control policy is able to generate, develop and improve. The scaling factors can be chosen automatically.Optimizing the scaling factors by genetic algorithm instead of trial or experimental method which is often used in conventional linguistic self-organizing controller eliminates the drawback of an exhausive search of the GE*GC*GU space by human operator, and also produces the better system response and a set of better control rules. A number of simulations on linear dynamic systems as well as non-linear systems such as second order process with a random disturbance, third order process with time lags and the cart-pole balancing problem etc. are described in this paper, which shows that the controller has strong adaptive properties and gives better performance than that of the conventional linguistic self-organizing controller.     

Attitude estimation method based on extended Kalman filter algorithm with 22 dimensional state vector for low-cost agricultural UAV

To overcome the shortcomings of traditional artificial spraying pesticides and make more efficient prevention of diseases and pests, a coaxial sixteen-rotor unmanned aerial vehicle(UAV) with pesticide spraying system is designed. The coaxial sixteen-rotor UAV's basic structure and attitude estimation method are explained. The whole system weights 25 kg, cruising speed can reach 15 m/s, and the flight time is more than 20 min. When the UAV takes large load, the traditional extended Kalman filter(EKF) attitude estimation method can not meet the work requirements under the condition of strong vibration, the attitude measure accuracy is poor and the attitude angle divergence is easily caused. Hence an attitude estimation method based on EKF algorithm with 22 dimensional state vector is proposed which can solve these problems. The UAV system consists of STM32 F429 as controller, integrating following measure sensors: accelerometer and gyroscope MPU6000, magnetometer LSM303 D, GPS NEO-M8 N and barometer. The attitude unit quaternion, velocity, position, earth magnetic field, biases error of gyroscope, accelerometer and magnetometer are introduced as the inertial navigation systems(INS) state vector, while magnetometer, global positioning system(GPS) and barometer are introduced as observation vector, thus making the estimate of the navigation information more accurate. The control strategy of coaxial sixteen-rotor UAV is based on the control method of combining active disturbance rejection control(ADRC) and proportion integral derivative(PID) control. Actual flight data are used to verify the algorithm, and the static experiment shows that the precision of roll angle and pitch angle of the algorithm are ±0.1 °, the precision of yaw angle is ±0.2 °. The attitude angle output of MTi sensor is used as reference. The dynamic experiment shows that the accuracy of attitude estimated by EKF algorithm is quite similar to that of MTi's output, moreover, the algorithm has good real-time performance which meets the need of high maneuverability of agricultural UAV.     

Optimal Fuzzy PID Controller with Adjustable Factors and Its Application to Intelligent Artificial Legs

A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on-line fuzzy inference mechanism and another is a conventional PID controller. In the fuzzy inference mecha-nism, three adjustable factors χp, χi, and χd are introduced. Their function is to further modify and op-timize the result of the fuzzy inference to make the controller have the optimal control effect on a given ob-ject. The optimal values of these factors are determined based on the ITAE criterion and the flexible poly-hedron search algorithm of Nelder and Mead. This PID controller has been used to control a D.C. motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that the design of this controller is very effective and can be widely used to control different kinds of objects and processes.     

Measurement and AFPS control of molten steel level in strip casting

The measurement and control of the molten steel level are studied, which affect the quality of strip surface in strip casting.A system of molten steel measurement with a CCD (Charge Coupled Devices) sensor is designed, real-time measured data are given and its precision is analyzed. The level fluctuation model is derived, and an adaptive fuzzy-PID controller with supervisory control (AFPS) is proposed. The stability of the system is proved using Lyapunov theorem, and the simulation results are given when the model, the casting speed and the roll gap change. It is suggested that this kind of coupled nonlinear and time varying system is stable and robust using the designed AFPS controller.     

Discrete Time Optimal Adaptive Control for Linear Stochastic Systems

The least-squares (LS) algorithm has been used for system modeling for a long time. Without any excitation conditions, only the convergence rate of the common LS algorithm can be obtained. This paper analyzed the weighted least-squares (WLS) algorithm and described the good properties of the WLS algorithm. The WLS algorithm was then used for adaptive control of linear stochastic systems to show that the linear closed-loop system was globally stable and that the system identification was consistent. Compared to the past optimal adaptive controller, this controller does not impose restricted conditions on the coefficients of the system, such as knowing the first coefficient before the controller. Without any persistent excitation conditions, the analysis shows that, with the regulation of the adaptive control, the closed-loop system was globally stable and the adaptive controller converged to the one-step-ahead optimal controller in some sense.     

Research on the Winding/Unwinding Control System Based on Fuzzy Control Theory

A fuzzy controller of the winding/unwinding control system used in jig-dyeing machine is introduced, which is superior to the one with conventional optimal PID controller in convergence speed and stability. Its mathematical model and transfer function are presented based on mechanism of the winding/unwinding control system. Simulation of the fuzzy controller carried out in the MATLAB （Simulink） environment proves that the control system based on fuzzy controller is superior in quality, precision and operation to a conventional optimal PID controller. The outlined experimental results also show the effectiveness and the robustness of the fuzzy PID controller in good dynamic performance, high robustness to parameter variation and disturbance.     

Intelligent Integrated Control of Underactuated Mechanical Systems with Second-order Nonholonomic Constraints

This paper describes an intelligent integrated control of an acrobot, which is an underactuated mechanical system with second-order nonholonomic constraints. The control combines a model-free fuzzy control, a fuzzy sliding-mode control and a modelbased fuzzy control. The model-free fuzzy controller designed for the upswing ensures that the energy of the acrobot increases with each swing. Then the fuzzy sliding-mode controller is employed to control the movement that the acrobot enters the balance area from the swing-up area. The model-based fuzzy controller, which is based on a Takagi-Sugeno fuzzy model, is used to balance the acrobot. The stability of the fuzzy control system for balance control is guaranteed by a common symmetric positive matrix, which satisfies linear matrix inequalities.     

MEMS-Based Low-Cost Flight Control System for Small UAVs

Small unmanned air vehicles （UAVs） can be used for various kinds of surveillance and data collection missions. The UAV flight control system is the key to a successful mission. This paper describes a low-cost micro-electro mechanical system-based flight control system for small UAVs. The integrated hardware flight control system weighs only 24 g. The system includes a highly-integrated wireless transmission link, which is lighter than traditional links. The flight control provides altitude hold control and global positioning system navigation based on gain scheduling proportional-integral-derivative control. Flight tests to survey the grass quality of a large lawn show that the small UAV can fly autonomously according to a series of pre-arranged waypoints with a controlled altitude while the wireless video system transmits images of the surveillance target to a ground control station.     

NN adaptive pole placement method for sintering finish point control

A neural network model with a special structure, which is divided into linear and nonlinear parts, was proposed for identification of a nonlinear system. In this model, the nonlinear part of the object is treated as a measured disturbance, and is compensated by a feed forward method; an adaptive pole placement algorithm is used to control the linear part of the object. The simulation results show that the identification efficiency and accuracy are improved when the new controller is applied to sintering finish point control.     

Modified Projective Synchronization of a New Hyperchaotic System with Known or Unknown Parameters

This work presents two different methods-- nonlinear control method and adaptive control approach to achieve the modified projective synchronization of a new hyperchaotic system with known or unknown parameters. Based on Lyapunov stability theory, nonlinear control method is adopted when the parameters of driving and response systems are known beforehand; when the parameters are fully unknown, adaptive controllers and parameters update laws are proposed to synchronize two different hyperchaotic system and identify the unknown parameters. Moreover, the rate of synchronization can be regulated by adjusting the control gains designed in the controllers. The corresponding simulations are exploited to demonstrate the effectiveness of the proposed two methods.     

Fuzzy Variable Structure Control of Photovoltaic MPPT System

In order to reduce chattering phenomenon of variable structure control, a fuzzy variable structure control method is adopted and applied in the photovoitaic maximum power point tracking （MPPT） control system. Firstly, the electric features of PV cells and a dynamic model of photovoitaic system with a DGDC buck converter are analysed. Then a hybrid fuzzy variable structure controller is designed. The controller is composed of a fuzzy variable structure control term and a supervisory control term. The former is the main part of the controller and the latter is used to ensure the stability of the system. Finally, the conventional variable structure control method and the fuzzy variable structure control method are applied respectively. The comparing of simulation results shows the superiority of the latter.     

Longitudinal Acceleration Tracking Control of Low Speed Heavy-Duty Vehicles

This paper presents a model matching control （MMC） method based on the sliding mode control （SMC） method for longitudinal acceleration tracking control in a vehicular stop-and-go cruise control system. The nonlinearity of the vehicle acceleration response at low speeds was analyzed to develop a transfer function model of the vehicle longitudinal dynamics using the least-mean-square system identification technique. This transfer function was then used to design the MMC controller, including an SMC feedback compensator. The system combines the advantages of the two control methods with robust control and rapid response. Simulations show that the controller enhances the rapid trackability to the vehicle acceleration and improves the system＇s robustness at low speeds compared with conventional PID MMC controllers.