This paper presents a novel five degrees of freedom (DOF) two-wheeled robotic machine (TWRM) that delivers solutions for both industrial and service robotic applications by enlarging the vehicle′s workspace and incre...This paper presents a novel five degrees of freedom (DOF) two-wheeled robotic machine (TWRM) that delivers solutions for both industrial and service robotic applications by enlarging the vehicle′s workspace and increasing its flexibility. Designing a two-wheeled robot with five degrees of freedom creates a high challenge for the control, therefore the modelling and design of such robot should be precise with a uniform distribution of mass over the robot and the actuators. By employing the Lagrangian modelling approach, the TWRM′s mathematical model is derived and simulated in Matlab/Simulink?. For stabilizing the system′s highly nonlinear model, two control approaches were developed and implemented: proportional-integral-derivative (PID) and fuzzy logic control (FLC) strategies. Considering multiple scenarios with different initial conditions, the proposed control strategies′ performance has been assessed.展开更多
In the last decade,artificial intelligence(AI)techniques have been extensively used for maximum power point tracking(MPPT)in the solar power system.This is because conventional MPPT techniques are incapable of trackin...In the last decade,artificial intelligence(AI)techniques have been extensively used for maximum power point tracking(MPPT)in the solar power system.This is because conventional MPPT techniques are incapable of tracking the global maximum power point(GMPP)under partial shading condition(PSC).The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points(MPPs).The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT.The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits.In general,all of the AI-based MPPT techniques exhibit fast convergence speed,less steady-state oscillation and high efficiency,compared with the conventional MPPT techniques.However,the AI-based MPPT techniques are computationally intensive and costly to realize.Overall,the hybrid MPPT is favorable in terms of the balance between performance and complexity,and it combines the advantages of conventional and AI-based MPPT techniques.In this paper,a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results.The merits,open issues and technical implementations of AI-based MPPT techniques are evaluated.We intend to provide new insights into the choice of optimal AI-based MPPT techniques.展开更多
In a conventional direct torque control(CDTC) of the induction motor drive, the electromagnetic torque and the stator flux are characterized by high ripples. In order to reduce the undesired ripples, several methods a...In a conventional direct torque control(CDTC) of the induction motor drive, the electromagnetic torque and the stator flux are characterized by high ripples. In order to reduce the undesired ripples, several methods are used in the literature. Nevertheless,these methods increase the algorithm complexity and dependency on the machine parameters such as the space vector modulation(SVM). The fuzzy logic control method is utilized in this work to decrease these ripples. Moreover, to eliminate the mechanical sensor the extended kalman filter(EKF) is used, in order to reduce the cost of the system and the rate of maintenance. Furthermore, in the domain of controlling the real-time induction motor drives, two principal digital devices are used such as the hardware(FPGA) and the digital signal processing(DSP). The latter is a software solution featured by a sequential processing that increases the execution time. However, the FPGA is featured by a high processing speed because of its parallel processing. Therefore, using the FPGA it is possible to implement complex algorithms with low execution time and to enhance the control bandwidth. The large bandwidth is the key issue to increase the system performances. This paper presents the interest of utilizing the FPGAs to implement complex control algorithms of electrical systems in real time. The suggested sensorless direct torque control using the fuzzy logic(DTFC) of an induction motor is successfully designed and implemented on an FPGA Virtex 5 using xilinx system generator. The simulation and implementation results show proposed approach s performances in terms of ripples, stator current harmonic waves, execution time, and short design time.展开更多
In this article, an adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems. In the AFSMC design, the sliding mode control (SMC) concept is combined with fuzzy control strategy to obtain a mo...In this article, an adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems. In the AFSMC design, the sliding mode control (SMC) concept is combined with fuzzy control strategy to obtain a model-free fuzzy sliding mode control. The equivalent controller has been substituted for by a fuzzy system and the uncertainties are estimated on-line. The approach of the AFSMC has the learning ability to generate the fuzzy control actions and adaptively compensates for the uncertainties. Despite the high nonlinearity and coupling effects, the control input of the proposed control algorithm has been decoupled leading to a simplified control mechanism for robotic systems. Simulations have been carried out on a two link planar robot. Results show the effectiveness of the proposed control system.展开更多
In this study an indirect adaptive sliding mode control (SMC) based on a fuzzy logic scheme is proposed to strengthen the tracking control performance of a general class of multi-input multi-output (MIMO) nonlinear un...In this study an indirect adaptive sliding mode control (SMC) based on a fuzzy logic scheme is proposed to strengthen the tracking control performance of a general class of multi-input multi-output (MIMO) nonlinear uncertain systems. Combining reaching law approach and fuzzy universal approximation theorem, the proposed design procedure combines the advantages of fuzzy logic control, adaptive control and sliding mode control. The stability of the control systems is proved in the sense of the Lyapunov second stability theorem. Two simulation studies are presented to demonstrate the effectiveness of our new hybrid control algorithm.展开更多
The performance of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. In most cases, the closed-loop control performance and stability are enhanced if more rules are added to the rule base o...The performance of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. In most cases, the closed-loop control performance and stability are enhanced if more rules are added to the rule base of the FLC. However, a large set of rules requires more on-line computational time and more parameters need to be adjusted. In this paper, a robust PD-type FLC is driven for a class of MIMO second order nonlin- ear systems with application to robotic manipulators. The rule base consists of only four rules per each de- gree of freedom (DOF). The approach implements fuzzy partition to the state variables based on Lyapunov synthesis. The resulting control law is stable and able to exploit the dynamic variables of the system in a lin- guistic manner. The presented methodology enables the designer to systematically derive the rule base of the control. Furthermore, the controller is decoupled and the procedure is simplified leading to a computationally efficient FLC. The methodology is model free approach and does not require any information about the sys- tem nonlinearities, uncertainties, time varying parameters, etc. Here, we present experimental results for the following controllers: the conventional PD controller, computed torque controller (CTC), sliding mode con- troller (SMC) and the proposed FLC. The four controllers are tested and compared with respect to ease of design, implementation, and performance of the closed-loop system. Results show that the proposed FLC has outperformed the other controllers.展开更多
This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generat...This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generator (SEIG). The aim of the developed control method is to automatically tune and optimize the scaling factors and the membership functions of the Fuzzy Logic Controllers (FLC) using Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO). Two Pulse Width Modulated voltage source PWM converters with a carrier-based Sinusoidal PWM modulation for both Generator- and Grid-side converters have been connected back to back between the generator terminals and utility grid via common DC link. The indirect vector control scheme is implemented to maintain balance between generated power and power supplied to the grid and maintain the terminal voltage of the generator and the DC bus voltage constant for variable rotor speed and load. Simulation study has been carried out using the MATLAB/Simulink environment to verify the robustness of the power electronics converters and the effectiveness of proposed control method under steady state and transient conditions and also machine parameters mismatches. The proposed control scheme has improved the voltage regulation and the transient performance of the wave energy scheme over a wide range of operating conditions.展开更多
A wind energy conversion system(WECS)based on a permanent magnet synchronous generator(PMSG)is an effective solution for renewable energy generation in modern power systems.The main advantages of PMSG include high per...A wind energy conversion system(WECS)based on a permanent magnet synchronous generator(PMSG)is an effective solution for renewable energy generation in modern power systems.The main advantages of PMSG include high performance at high and low speeds,minimal control effort owing to lower rotor inertia,self-excitation,high reliability,and simplicity of structure compared with induction generators.However,the intermittent nature of wind energy implies that maximum efficiency is not obtained from this system.Accordingly,maximum power point tracking(MPPT)in wind turbine systems has been proposed to address this problem.Traditional MPPT strategies suffer from severe output power fluctuations,low efficiency,and significant ripples in turbine rotation speed.This paper presents a novel MPPT control strategy based on fuzzy logic control(FLC)and model predictive control(MPC)to extract the maximum power from a PMSG-WECS and control the machine-side and grid-side converters.The simulation results obtained from Matlab/Simulink confirm the superiority of the control model in eliminating the output power fluctuations of the wind generators and accurately tracking the maximum power point.A comparative study between conventional MPPT and control methods is also conducted.展开更多
文摘This paper presents a novel five degrees of freedom (DOF) two-wheeled robotic machine (TWRM) that delivers solutions for both industrial and service robotic applications by enlarging the vehicle′s workspace and increasing its flexibility. Designing a two-wheeled robot with five degrees of freedom creates a high challenge for the control, therefore the modelling and design of such robot should be precise with a uniform distribution of mass over the robot and the actuators. By employing the Lagrangian modelling approach, the TWRM′s mathematical model is derived and simulated in Matlab/Simulink?. For stabilizing the system′s highly nonlinear model, two control approaches were developed and implemented: proportional-integral-derivative (PID) and fuzzy logic control (FLC) strategies. Considering multiple scenarios with different initial conditions, the proposed control strategies′ performance has been assessed.
基金supported by the School of EngineeringMonash University Malaysia
文摘In the last decade,artificial intelligence(AI)techniques have been extensively used for maximum power point tracking(MPPT)in the solar power system.This is because conventional MPPT techniques are incapable of tracking the global maximum power point(GMPP)under partial shading condition(PSC).The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points(MPPs).The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT.The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits.In general,all of the AI-based MPPT techniques exhibit fast convergence speed,less steady-state oscillation and high efficiency,compared with the conventional MPPT techniques.However,the AI-based MPPT techniques are computationally intensive and costly to realize.Overall,the hybrid MPPT is favorable in terms of the balance between performance and complexity,and it combines the advantages of conventional and AI-based MPPT techniques.In this paper,a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results.The merits,open issues and technical implementations of AI-based MPPT techniques are evaluated.We intend to provide new insights into the choice of optimal AI-based MPPT techniques.
文摘In a conventional direct torque control(CDTC) of the induction motor drive, the electromagnetic torque and the stator flux are characterized by high ripples. In order to reduce the undesired ripples, several methods are used in the literature. Nevertheless,these methods increase the algorithm complexity and dependency on the machine parameters such as the space vector modulation(SVM). The fuzzy logic control method is utilized in this work to decrease these ripples. Moreover, to eliminate the mechanical sensor the extended kalman filter(EKF) is used, in order to reduce the cost of the system and the rate of maintenance. Furthermore, in the domain of controlling the real-time induction motor drives, two principal digital devices are used such as the hardware(FPGA) and the digital signal processing(DSP). The latter is a software solution featured by a sequential processing that increases the execution time. However, the FPGA is featured by a high processing speed because of its parallel processing. Therefore, using the FPGA it is possible to implement complex algorithms with low execution time and to enhance the control bandwidth. The large bandwidth is the key issue to increase the system performances. This paper presents the interest of utilizing the FPGAs to implement complex control algorithms of electrical systems in real time. The suggested sensorless direct torque control using the fuzzy logic(DTFC) of an induction motor is successfully designed and implemented on an FPGA Virtex 5 using xilinx system generator. The simulation and implementation results show proposed approach s performances in terms of ripples, stator current harmonic waves, execution time, and short design time.
文摘In this article, an adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems. In the AFSMC design, the sliding mode control (SMC) concept is combined with fuzzy control strategy to obtain a model-free fuzzy sliding mode control. The equivalent controller has been substituted for by a fuzzy system and the uncertainties are estimated on-line. The approach of the AFSMC has the learning ability to generate the fuzzy control actions and adaptively compensates for the uncertainties. Despite the high nonlinearity and coupling effects, the control input of the proposed control algorithm has been decoupled leading to a simplified control mechanism for robotic systems. Simulations have been carried out on a two link planar robot. Results show the effectiveness of the proposed control system.
文摘In this study an indirect adaptive sliding mode control (SMC) based on a fuzzy logic scheme is proposed to strengthen the tracking control performance of a general class of multi-input multi-output (MIMO) nonlinear uncertain systems. Combining reaching law approach and fuzzy universal approximation theorem, the proposed design procedure combines the advantages of fuzzy logic control, adaptive control and sliding mode control. The stability of the control systems is proved in the sense of the Lyapunov second stability theorem. Two simulation studies are presented to demonstrate the effectiveness of our new hybrid control algorithm.
文摘The performance of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. In most cases, the closed-loop control performance and stability are enhanced if more rules are added to the rule base of the FLC. However, a large set of rules requires more on-line computational time and more parameters need to be adjusted. In this paper, a robust PD-type FLC is driven for a class of MIMO second order nonlin- ear systems with application to robotic manipulators. The rule base consists of only four rules per each de- gree of freedom (DOF). The approach implements fuzzy partition to the state variables based on Lyapunov synthesis. The resulting control law is stable and able to exploit the dynamic variables of the system in a lin- guistic manner. The presented methodology enables the designer to systematically derive the rule base of the control. Furthermore, the controller is decoupled and the procedure is simplified leading to a computationally efficient FLC. The methodology is model free approach and does not require any information about the sys- tem nonlinearities, uncertainties, time varying parameters, etc. Here, we present experimental results for the following controllers: the conventional PD controller, computed torque controller (CTC), sliding mode con- troller (SMC) and the proposed FLC. The four controllers are tested and compared with respect to ease of design, implementation, and performance of the closed-loop system. Results show that the proposed FLC has outperformed the other controllers.
文摘This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generator (SEIG). The aim of the developed control method is to automatically tune and optimize the scaling factors and the membership functions of the Fuzzy Logic Controllers (FLC) using Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO). Two Pulse Width Modulated voltage source PWM converters with a carrier-based Sinusoidal PWM modulation for both Generator- and Grid-side converters have been connected back to back between the generator terminals and utility grid via common DC link. The indirect vector control scheme is implemented to maintain balance between generated power and power supplied to the grid and maintain the terminal voltage of the generator and the DC bus voltage constant for variable rotor speed and load. Simulation study has been carried out using the MATLAB/Simulink environment to verify the robustness of the power electronics converters and the effectiveness of proposed control method under steady state and transient conditions and also machine parameters mismatches. The proposed control scheme has improved the voltage regulation and the transient performance of the wave energy scheme over a wide range of operating conditions.
文摘A wind energy conversion system(WECS)based on a permanent magnet synchronous generator(PMSG)is an effective solution for renewable energy generation in modern power systems.The main advantages of PMSG include high performance at high and low speeds,minimal control effort owing to lower rotor inertia,self-excitation,high reliability,and simplicity of structure compared with induction generators.However,the intermittent nature of wind energy implies that maximum efficiency is not obtained from this system.Accordingly,maximum power point tracking(MPPT)in wind turbine systems has been proposed to address this problem.Traditional MPPT strategies suffer from severe output power fluctuations,low efficiency,and significant ripples in turbine rotation speed.This paper presents a novel MPPT control strategy based on fuzzy logic control(FLC)and model predictive control(MPC)to extract the maximum power from a PMSG-WECS and control the machine-side and grid-side converters.The simulation results obtained from Matlab/Simulink confirm the superiority of the control model in eliminating the output power fluctuations of the wind generators and accurately tracking the maximum power point.A comparative study between conventional MPPT and control methods is also conducted.
