Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Hybrid electric vehicles (HEVs) have been introduced to mitigate problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% of the desired SOC regardless of starting SOC.

Non-fragile guaranteed cost control of discrete-time fuzzy bilinear system

This paper focuses on the problem of non-fragile guaranteed cost control for a class of T-S discrete-time fuzzy bilinear systems（DFBS）.Based on the parallel distributed compensation（PDC） approach,the sufficient conditions are derived such that the closed-loop system is asymptotically stable and the cost function value is no more than a certain upper bound in the presence of the additive controller gain perturbations.The non-fragile guaranteed cost controller can be obtained by solving a set of bilinear matrix inequalities（BMIs）.The Van de Vusse model is utilized to demonstrate the validity and effectiveness of the proposed approach.

Static output feedback control for discrete-time fuzzy bilinear system

The problem of designing fuzzy static output feedback controller for T-S discrete-time fuzzy bilinear system （DFBS） is presented. Based on parallel distribution compensation method, some sufficient conditions are derived to guarantee the stability of the overall fuzzy system. The stabilization conditions are further formulated into linear matrix inequality （LMI） so that the desired controller can be easily obtained by using the Matlab LMI toolbox. In comparison with the existing results, the drawbacks, such as coordinate transformation, same output matrices, have been elim- inated. Finally, a simulation example shows that the approach is effective.

Indirect adaptive fuzzy control for a class of nonlinear discrete-time systems

An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membership functions in fuzzy logic systems are adjusted according to adaptive laws for the purpose of controlling the plant to track a reference trajectory. It is proved that the scheme can not only guarantee the boundedness of the input and output of the closed-loop system, but also make the tracking error converge to a small neighborhood of the origin. Simulation results indicate the effectiveness of this scheme.

Robust passive control for discrete-time T-S fuzzy systems with delays

This article deals with the robust stability analysis and passivity of uncertain discrete-time Takagi- Sugeno （T-S） fuzzy systems with time delays. The T-S fuzzy model with parametric uncertainties can approximate nonlinear uncertain systems at any precision. A sufficient condition on the existence of robust passive controller is established based on the Lyapunov stability theory. With the help of linear matrix inequality （LMI） method, robust passive controllers are designed so that the closed-loop system is robust stable and strictly passive. Furthermore, a convex optimization problem with LMI constraints is formulated to design robust passive controllers with the maximum dissipation rate. A numerical example illustrates the validity of the proposed method.

Fuzzy Hybrid Control of Vibration Attitude of Full Car via Magneto-rheological Suspensions

A magneto-rheological（MR） semi-active suspension system with the controllable damping forces has received more attention in reducing the vibration of a vehicle. However, many control strategies only discussed one or two vibration states of the vehicle based on a quarter-car model or a half vehicle model via MR suspensions. They cannot provide a satisfying whole-vehicle performance on a road test. Hence, a full car vibration model via an MR suspension system is proposed. To reduce the heave, pitch and roll motion of the vehicle body and the vertical vibration of four wheels, a fuzzy hybrid controller for vibration attitude of full car via MR suspensions is proposed. First, a skyhook-fuzzy control scheme is designed to reduce the heave, roll and pitch motion of the vehicle body. Second, a revised ground hook control strategy is adopted to decrease the vertical vibration of the wheels. Finally, a hybrid control scheme based on a fuzzy reasoning method is proposed to tune the hybrid damping parameter, which is suitable for coordination the attitude of the vehicle body and the wheels. A test and control system for the vibration attitude of full car is set up. It is implemented on a car equipped with four MR suspensions. The results on random highway and rough road indicate that the fuzzy hybrid controller can decrease the vibration accelerations of the vehicle body and the wheels to 65%-80% and 80%-90%, respectively. It reduces the automotive vibrations of heave, roll and pitch more effectively than a passive suspension and an MR suspension with a traditional hybrid control scheme so that it achieves better ride comfort and road holding concurrently. This paper proposes a new fuzzy hybrid control（FHC） method for reducing vibration attitude of full car via MR suspensions and develops a road test to evaluate the FHC.

Controlling Torque Distribution for Parallel Hybrid Vehicle Based on Hierarchical Structure Fuzzy Logic

The Hierarchical Structure Fuzzy Logic Control (HSFLC) strategies of torque distribute for Parallel Hybrid Electric Vehicle (PHEV) in the mode of operation of the vehicle i. e. , acceleration, cruise, deceleration etc. have been studied. Using secondly developed the hybrid vehicle simulation tool ADVISOR, the dynamic model of PHEV has been set up by MATLAB/SIMULINK. The engine, motor as well as the battery characteristics have been studied. Simulation results show that the proposed hierarchical structured fuzzy logic control strategy is effective over the entire operating range of the vehicle in terms of fuel economy. Based on the analyses of the simulation results and driver’s experiences, a fuzzy controller is designed and developed to control the torque distribution. The controller is evaluated via hardware-in-the-loop simulator (HILS). The results show that controller verify its value.

HYBRID FUZZY CONTROL FOR ELECTRO-HYDRAULIC ACTIVE DAMPING SUSPENSION

A new control scheme, the hybrid fuzzy control method, for active dampingsuspension system is presented. The scheme is the result of effective combination of the statisticaloptimal control method based on the statistical property of suspension system, with the bang-bangcontrol method based on the real-time characteristics of suspension system. Computer simulations areperformed to compare the effectiveness of hybrid fuzzy control scheme with that of optimal dampingcontrol, bang-bang control, and passive suspension. It takes the effects of time-variant factorsinto full account. The superiority of the proposed hybrid fuzzy control scheme for active dampingsuspension to the passive suspension is verified in the experiment study.

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.

Control strategy for hybrid tracked vehicles using fuzzy logic

To solve the problem of power distribution for hybrid tracked vehicles （HTV）, a supervi- sory control strategy is proposed. Firstly, power system integration is analyzed and modeled. Then the control algorithm is given. Two fuzzy logics are used to realize the coordination control over each power unit. One controls power distribution based on the load power and battery state of charge （SOC）. The other manage the power during regenerating braking. To validate the presented control strategy, a ＂driver and controller＂ in the loop simulation platform is built based on dSPACE system and real-time simulation is made. The simulation results show that the strategy presented can solve the power distribution problem of hybrid tracked vehicles correctly and effectively.

Dehydration of Agro Products in a Hybrid Solar Dryer Controlled through a Fuzzy Logic System

Drying is one of the most energy-intensive processes in agro-products industry. For this reason, using solar energy appears as an attractive not polluting alternative to be used in drying processes. However, the daily and seasonal fluctuations in the radiation level require using energy accumulators with phase change materials (paraffin wax), to have a continuous drying processes. In hybrid solar dryers with energy accumulation system, a control system is essential to coordinate the control valves that allow the income of air that comes from the solar panel or from the energy accumulator. In this work, we implemented an advances multivariable control system that uses fuzzy logic in the hybrid solar dryer. The dryer includes an energy accumulator panel with paraffin wax as phase change material. The input variables were ambient temperature and solar radiation, both not controllable. The controlled variables were the opening level of the solar panel and accumulator energy valves. The control program consisted in an algorithm implemented with the “Fuzzy” toolbox in Matlab. Data were acquired with OPTO 22. The control system performed adequately when used to dehydrate mushroom slices and plums. Closing or opening the respective valves as a response to the variations of solar radiation and ambient air temperature allowed optimizing the use of solar energy.

An Investigation into Regenerative Braking Control Strategy for Hybrid Electric Vehicle

Energy regeneration during braking is an important technique for hybrid electric vehicle (HEV) to improve their fuel economy and extend their driving range. Due to the effect of regenerative braking torque which is added by electric motor, the braking torque distribution between front and rear axles should be changed and the control logic of anti-lock braking system (ABS) ought to be adjusted according to the regenerative braking torque. This paper put forward a braking control strategy for hybrid electric vehicle; the control strategy is implemented with eight DOFs (Degree-of-Freedom) nonlinear vehicle forward simulation model which is built under the environment of Matlab/Simulink. Based on target wheel slip ratio, a fuzzy logic approach was applied to maintain the optimal target slip ratio so that best compromise between hydraulic torque and regenerative torque can be obtained for the vehicle.

OPTIMAL TORQUE CONTROL STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLE WITH AUTOMATIC MECHANICAL TRANSMISSION

In parallel hybrid electrical vehicle （PHEV） equipped with automatic mechanical transmission （AMT）, the driving smoothness and the clutch abrasion are the primary considerations for powertrain control during gearshift and clutch operation. To improve these performance indexes of PHEV, a coordinated control system is proposed through the analyzing of HEV powertrain dynamic characteristics. Using the method of minimum principle, the input torque of transmission is optimized to improve the driving smoothness of vehicle. Using the methods of fuzzy logic and fuzzy-PID, the engaging speed of clutch and the throttle opening of engine are manipulated to ensure the smoothness of clutch engagement and reduce the abrasion of clutch friction plates. The motor provides the difference between the required input torque of transmission and the torque transmitted through clutch plates. Results of simulation and experiments show that the proposed control strategy performs better than the contrastive control system, the smoothness of driving and the abrasion of clutch can be improved simultaneously.

Transient Air-Fuel Ratio Control in a CNG Engine Using Fuzzy Neural Networks

The fuzzy neural networks has been used as means of precisely controlling the air-fuel ratio of a lean-burn compressed natural gas (CNG) engine. A control algorithm, without based on engine model, has been (utilized) to construct a feedforward/feedback control scheme to regulate the air-fuel ratio. Using fuzzy neural networks, a fuzzy neural hybrid controller is obtained based on PI controller. The new controller, which can adjust parameters online, has been tested in transient air-fuel ratio control of a CNG engine.

Intelligent Control Scheme of Engineering Machinery of Cluster Hybrid System

In a hybrid system, the subsystems with discrete dynamics play a central role in a hybrid system. In the course of engineering machinery of cluster construction, the discrete control law is hard to obtain because the construction environment is complex and there exist many affecting factors. In this paper, hierarchically intelligent control, expert control and fuzzy control are introduced into the discrete subsystems of engineering machinery of cluster hybrid system, so as to rebuild the hybrid system and make the discrete control law easily and effectively obtained. The structures, reasoning mechanism and arithmetic of intelligent control are replanted to discrete dynamic, conti- nuous process and the interface of the hybrid system. The structures of three types of intelligent hybrid system are presented and the human experiences summarized from engineering machinery of cluster are taken into account.

Torsional vibration active control of hybrid construction machinery complex shafting

Due to the coaxial connection of engine, motor and pump, the dynamic characteristics of hybrid construction machinery are changed, which generates a new torsional vibration problem of multi-power sources. To reduce the torsional vibration of the hybrid construction machinery complex shafting, torsional vibration active control was proposed. The three-mass model of coaxial shafting of hybrid construction machinery was established. The PID control and the fuzzy sliding mode control were chosen to weaken torsional vibration by controlling the motor speed and torque. The simulation results show that the fuzzy sliding mode control has 12% overshoot of the PID control when the engine torque changes. The active control is effective and can realize smooth power switch.

THEORETICAL AND EXPERIMENTAL STUDY ON THE PRESSURE AND VACUUM CONTINUOUS CONTROL SYSTEM BASED ON HYBRID PUMP

A novel pressure and vacuum continuous control system, which adopts a hybrid pump as pressure and vacuum source, is presented. The mathematical model of the system is developed. The theoretical simulation and analysis on the system are implemented in order to study the relationships among the characteristics, parameters and working points of the system. The experimental investigations on the system characteristics are presented with the adoption ofa fuzzy-PID controller. The simulation and experimental results indicate that the pressure and vacuum continuous control system based on hybrid pump has good dynamic and static performance, strong robustness and satisfactory adaptability to various system parameters. According to the results, system can successfully gain high accuracy and fast response signal. Also, the mathematical model of system is also testified by the experimental results.

Kinematic Modelling and Control Simulation for 1PS+3TPS Type Hybrid Machine Tool

A structure scheme for a novel hybrid machine tool (HMT) is proposed in this paper. In the scheme,a 4-DOFs 1PS+3TPS type spatial hybrid mechanism is utilized as main feed mechanism,with assistance of a two direction movable worktable,multi-coordinates NC machining can be realized. In the main feed mechanism,fixed platform is connected with moving platform by three TPS driving links and one PS driving link,one translation DOF and three rotation DOFs can be achieved by it. This type HMT enjoys some advantages over its conventional counterparts:large workspace,good dexterity,etc. Closed form inverse displacement analysis model and inverse kinematic model for main feed mechanism are established. A fuzzy PID control scheme for machining control of HMTs with high tracking precision is proposed aiming at highly nonlinear,tightly coupled and uncertain characteristic of HMTs. Simulation researches for fuzzy PID control of HMTs are carried out. Simulation Results demonstrate the effectiveness and the Robostness of the fuzzy PID controller.

Arbitrary full-state hybrid projective synchronization for chaotic discrete-time systems via a scalar signal

In this paper we present a new projective synchronization scheme, where two chaotic （hyperchaotic） discrete-time systems synchronize for any arbitrary scaling matrix. Specifically, each drive system state synchronizes with a linear combination of response system states. The proposed observer-based approach presents some useful features： i） it enables exact synchronization to be achieved in finite time （i.e., dead-beat synchronization）; ii） it exploits a scalar synchronizing signal; iii） it can be applied to a wide class of discrete-time chaotic （hyperchaotic） systems; iv） it includes, as a particular case, most of the synchronization types defined so far. An example is reported, which shows in detail that exact synchronization is effectively achieved in finite time, using a scalar synchronizing signal only, for any arbitrary scaling matrix.