Design and analysis of a novel cascade control algorithm for braking-by-wire system based on electromagnetic direct-drive valves

In order to improve the response speed and control precision of the braking system with parameters uncertainty and nonlinear friction, a braking-by-wire system based on the electromagnetic direct-drive valve and a novel cascade control algorithm was proposed in this paper. An electromagnetic linear actuator directly drives the valve spool and rapidly adjusts the pressure of braking wheel cylinders. A dynamic model of electromagnetic direct-drive valve considering improved LuGre dynamic friction is established. A novel cascade control algorithm with an outside loop pressure fuzzy controller and an inside loop electromagnetic direct-drive valve position controller was proposed. An adaptive integral robust inside loop controller is designed by combining friction compensation adaptive control law, linear feedback, and integral robust control. The uncertainty parameters and the friction state are estimated online. The stability of the cascade controller is proved by the Lyapunov method. Then a multi-objective opitimizemization design method of control parameters is proposed, which combines a multi-objective game theory and a technique for order preference by similarity to ideal solution (TOPSIS) based on entropy weight. The results show that the pressurization time of cascade control is less than 0.09 s under the 15 MPa step target signal. The control precision is improved effectively by the cascade controller under the ARTEMIS condition.     

Decentralized sampled-data fuzzy controller design for a VTOL UAV

This study focuses on a sampled-data fuzzy decentralized tracking control problem for a quadrotor unmanned aerial vehicle (UAV) under the variable sampling rate condition. To this end, the overall dynamics of the quadrotor is expressed as a decentralized Takagi–Sugeno (T–S) fuzzy model interconnected with each other. Although the proposed decentralized control technique divides the overall UAV control system into attitude and position subsystems, the stability of the entire control system is guaranteed. Besides, in this paper, the model uncertainty, interconnection, and reference trajectory are considered as disturbances acting on the tracking error. To attenuate these disturbances, a novel sampled-data tracking control design technique is derived based on a linear reference model to be tracked and the time-dependent Lyapunov–Krasovskii functional (LKF). By doing so, both the stability of the tracking error dynamics and the minimization of tracking performance are guaranteed. Also, the proposed tracking control design method is derived as a linear matrix inequality (LMI)-based optimal problem. Finally, a simulation example is provided to demonstrate the effectiveness and feasibility of the proposed design methodology.     

漂浮基空间机器人的基于模糊神经网络的自适应补偿控制

针对自由漂浮状态的空间机器人模型不确定性及其动力传动机构的摩擦死区非线性,将一种自适应模糊小脑模型关联控制( FCMAC)补偿策略用于轨迹跟踪及补偿问题.利用模糊神经网络并引入GL矩阵及其乘法算子“.”分别对执行机构中的摩擦死区及系统模型不确定部分进行自适应补偿,其补偿误差及外界扰动通过滑模控制器来消除.基于Lyapunov理论证明了闭环系统跟踪误差的有界性.仿真表明控制器可以达到较高精度,且能满足实时性要求.     

DSP-based synchronous control of dual linear motors via Sugeno type fuzzy neural network compensator

A digital signal processor (DSP)-based complementary sliding mode control (CSMC) with Sugeno type fuzzy neural network (SFNN) compensator is proposed in this study for the synchronous control of a dual linear motors servo system installed in a gantry position stage. The dual linear motors servo system comprises two parallel permanent magnet linear synchronous motors (PMLSMs). The dynamics of the single-axis motion system with a lumped uncertainty which contains parameter variations, external disturbances and nonlinear friction force is briefly introduced first. Then, a CSMC is designed to guarantee the precision position tracking requirement in single-axis control for the dual linear motors. Moreover, to enhance the robustness to uncertainties and to eliminate the synchronous error of dual linear motors, the CSMC with a SFNN compensator is proposed where the SFNN compensator is designed mainly to compensate the synchronous error. Furthermore, to increase the control performance of the proposed intelligent control approach, a 32-bit floating-point DSP, TMS320VC33, is adopted for the implementation of the proposed CSMC and SFNN. In addition, some experimental results are illustrated to show the validity of the proposed control approach.     

Composite fuzzy voltage-based command-filtered learning control of electrically-driven robots with input delay using disturbance observer

This paper presents an improved composite fuzzy learning control for uncertain electrically-driven robot manipulators with input delay and the external disturbances. In the framework of the backstepping algorithm, fuzzy systems are employed to approximate the unknown terms where the accuracy of fuzzy learning is also considered by defining prediction errors. With the aid of integral technique and the dynamic surface control, a variable is engendered for the system in such a way that the input-delayed robotic system is converted to the non-delayed robotic system. Besides, the command-filtered control is used to cope with the complexity explosion of the backstepping-based design. In order to improve the robust behavior of the control system, the proposed control scheme is equipped with disturbance observers (DOBs). Different from the previous works, the information of the input-delayed, the compensated error surfaces (obtained from the command-filtered approach), the prediction errors and the disturbance estimations (derived from DOBs) are unified to construct the proposed control framework. The stability of the overall system is verified by the Lyapunov theorem. The efficiency of the proposed concept is illustrated using various simulations for an electrically-driven robot manipulator in the presence of uncertainties.     

An improved event-triggered control method for fuzzy systems under membership functions online learning

This paper is concerned with the problem of adaptive event-triggered (AET) based optimal fuzzy controller design for nonlinear networked control systems (NCSs) characterized by Takagi–Sugeno (T–S) fuzzy models. An improved AET communication scheme with a memory adaptive rule is proposed to enhance the utilization of the state response vertex data. Different from the existing ET based results, the improved AET scheme can save more communication resources and acquire better system performance. The sufficient criteria of performance analysis and controller design are presented for the closed-loop control system subject to mismatched membership functions (MFs) and AET scheme. And then, a new MFs online learning algorithm on the basis of the gradient descent approach is employed to optimize the MFs of fuzzy controller and obtain optimal fuzzy controller for further improving system performance. Finally, two simulation examples are presented to verify the advantage and effectiveness of the provided controller design technique.     

T-S fuzzy tracking control of nonlinear constrained time-delay systems using a reference-management approach

A new control design approach is proposed for a class of nonlinear systems expressed by Takagi–Sugeno (T-S) fuzzy model, considering several objectives including robustness against input time-varying delay, input constraint satisfaction, and reference tracking. The proposed controller is designed on the basis of an augmented model, Lyapunov–Krasovskii functional, linear matrix inequality (LMI) tools, and parallel distributed compensation (PDC) approach. Proof of the input-to-state stability (ISS) criterion is provided for the error dynamics. Input constraint satisfaction is performed using a reference-management algorithm based on the linearized closed-loop system from the reference input to the constrained variables. In order to illustrate the effectiveness of the proposed control approach, simulations are performed on three practical examples, including a flexible-joint robot and a continuous stirred tank reactor (CSTR).     

Finite-time synchronization for periodic T–S fuzzy master-slave neural networks with distributed delays

Finite-time (FT) synchronization for periodic T–S fuzzy master-slave neural networks (NNs) with distributed delays is addressed in this work. A fuzzy controller is designed for the salve NNs to synchronize the master NNs in FT and a synchronization error system (SES) is derived. Sufficient conditions are established to guarantee that the SES is FT bounded by using the mode and fuzzy basis dependent Lyapunov function. A new algorithm is proposed to obtain the suboptimal boundary of the SES to analyze how the periodic characteristics affect the system boundary. Finally, a numerical example is provided to demonstrate the validity of the fuzzy controller and the iterative algorithm for the boundary.     

Adaptive fuzzy output feedback controller design for a HAGC system with input saturation and output error constraints

The comprehensive effect of external disturbance, measurement delay, unmeasurable states and input saturation makes the difficulties and challenges for a HAGC system. In this paper, an adaptive fuzzy output feedback control scheme is designed for a HAGC system under the simultaneous consideration of those factors. At the first place, by state transformation technique, the dynamic model of a HAGC system is simply expressed as a strict feedback form, where measurement delay is converted into input delay. Then, an auxiliary system is employed to compensate for the effect of input delay. Furthermore, an asymmetric barrier Lyapunov function (BLF) is constructed to ensure the output error constraint requirement of thickness error and the fuzzy observer is established to solve unmeasurable states, unknown nonlinear functions at the same time. With the aid of backstepping method, adaptive fuzzy controller is developed to assure that the closed-loop system is semi-globally boundedness and the output error of thickness error doesn’t violate its constraint. At the end, compared simulations are carried out to verify the efficiency of the proposed control scheme.     

Modeling and control analysis of electric driving system considering gear friction based on dual-inertia system

In the electric driving system, the measurement of the motor speed error becomes more and more important, which has an impact on the system vibration suppression. In this paper, based on the single-neuron adaptive PID control method, the dual-inertia system considering gear friction torque is modeled and studied. Firstly, the dual-inertia system with gear friction is established, and dynamic differential equations of it are derived; Then, the comprehensive meshing stiffness and the time-varying friction torque of the gear system are deduced; Next, the Ziegler-Nichlos frequency domain response method is adopted to obtain the parameters of the PID controller. The control methods including the PID, Fuzzy-PID with DOB and single-neuron adaptive PID are utilized to adjust the motor speed of the system; Finally, the effects of gear friction, the moment of inertia of load and control methods on motor speed and system error are analyzed.     

Adaptive fuzzy fixed-time dynamic surface control for stochastic nonstrict nonlinear systems with unknown dead-zones

This paper focuses on an adaptive fuzzy fixed-time control problem for stochastic nonstrict nonlinear systems with unknown dead-zones by using dynamic surface control (DSC) technology. Fuzzy logic systems (FLSs) and DSC technology are used to approximate nonlinear functions and reduce the computational complexity, respectively. At the same time, the influence of the dead-zone disturbance is offset by transforming the dead-zone model into the nonlinear model that can be approximated by the FLSs. Then, based on the fixed-time stability theory, an adaptive fuzzy fixed-time tracking control strategy is proposed, which can ensure semi-global practical fixed-time stability of the system and the tracking error converging to a small neighborhood near the origin. Finally, two simulation examples are given to prove the effectiveness of the proposed control strategy.     

单相交流逆变器的设计研究

本文针对单相逆变器的设计提出了一种新的CVCF逆变器方案——内部模型法。这种方法能够非常清晰地反映逆变器的内部工作原理，能按照控制精度对逆变器的输出波形进行控制。在控制策略上寻求抑制逆变器的输出因负载的改变而导致系统输出变化的方法。这种CVCF逆变器的输出不仅可以直接接入负载，还可以并入交流电网。     

Quantized asynchronous extended dissipative observer-based sliding mode control for Markovian jump TS fuzzy systems

This article is dedicated to the issue of asynchronous adaptive observer-based sliding mode control for a class of nonlinear stochastic switching systems with Markovian switching. The system under examination is subject to matched uncertainties, external disturbances, and quantized outputs and is described by a TS fuzzy stochastic switching model with a Markovian process. A quantized sliding mode observer is designed, as are two modes-dependent fuzzy switching surfaces for the error and estimated systems, based on a mode dependent logarithmic quantizer. The Lyapunov approach is employed to establish sufficient conditions for sliding mode dynamics to be robust mean square stable with extended dissipativity. Moreover, with the decoupling matrix procedure, a new linear matrix inequality-based criterion is investigated to synthesize the controller and observer gains. The adaptive control technique is used to synthesize asynchronous sliding mode controllers for error and SMO systems, respectively, so as to ensure that the pre-designed sliding surfaces can be reached, and the closed-loop system can perform robustly despite uncertainties and signal quantization error.Finally, simulation results on a one-link arm robot system are provided to show potential applications as well as validate the effectiveness of the proposed scheme.     

Data-driven-based adaptive fuzzy neural network control for the antimony flotation plant

Due to the unknown system structure of the froth flotation process and frequent fluctuations in production conditions, design of control strategy is a challenging problem. As a result, manual operation is still widely applied in practice by observing froth image features. However, since the manual observation is subjective and the production conditions are time-varying, the manual operation cannot make decisions quickly and accurately. In this paper, a data-driven-based adaptive fuzzy neural network control strategy is developed to implement the automatic control of the antimony flotation process. The strategy is composed of fuzzy neural network (FNN) controllers, a data-driven model, and an on-line adaptive algorithm. The FNN is constructed to derive the control laws of the reagent dosages. The parameters of the FNN controllers are tuned by gradient descent algorithm. To obtain the real-time error feedback information, the data-driven model is established, which integrates the long short term memory (LSTM) network and radial basis function neural network (RBFNN). The LSTM network is utilized as a primary model, and the RBFNN is used as an error compensation model. To handle the challenges of the frequent fluctuations in the production conditions, the on-line adaptive algorithm is proposed to tune the parameters of the FNN controllers. Simulations and experiments are carried out in a real-world antimony flotation plant in China. The results demonstrate that the proposed adaptive fuzzy neural network control strategy produces better control performance than the other two existing methods.     

Structural design and active disturbance rejection control of rigid-flexible coupling motion stage for nanopositioning

Mechanical-bearing-guided motion stage is widely used in electronic manufacturing equipment for its excellent high-acceleration performance and low cost, but its positioning precision is limited by the friction of mechanical bearing. To this end, a rigid-flexible coupling motion stage (RFCMS) with compound flexure hinges (CFHs) and a single drive is designed to simultaneously achieve large stroke and nanoscale precision in this work. The friction dead zone is isolated by utilizing the bending deflection of CFHs. To suppress the resonance of CFHs and deal with nonlinear disturbances and uncertainties, a model-based active disturbance rejection control (model-based ADRC) is adopted based on the bending stiffness modeling of CFHs, which can improve the tracking accuracy for the position profile and reduce the estimating error of the extended state observer for the total disturbance. Despite the uncertain control input gain and the nonlinear coupling of the working stage and the rigid frame, the tracking and estimating errors of closed-loop system are theoretically investigated. Experimental results show that RFCMS with model-based ADRC strategy can achieve positioning accuracy within 100 nm in point-to-point motion and has strong robustness to load mass changes.     

Exponential synchronization in inertial Cohen–Grossberg neural networks with time delays

In this paper, the problem of exponential synchronization for inertial Cohen–Grossberg neural networks with time delays is studied. According to the concept of synchronization, a controlled response system is constructed to obtain the error systems. First, by introducing a directive Lyapunov functional, a sufficient condition is derived to ascertain the exponential synchronization of the drive and response systems based on feedback control. Moreover, by introducing a variable substitution, a sufficient condition is obtained to ensure the global exponential synchronization for the systems. Two sufficient conditions are feasible for the global exponential synchronization of the drive and response systems, and complement each other. Finally, the parameters were set for numerical simulation, two illustrative examples are provided to show the effectiveness of the obtained theoretical results, and the validity of the model was proved.     

Nonlinear modeling and robust LMI fuzzy control of overhead crane systems

Overhead cranes are widely used structures for lifting and conveying heavy loads. The development of feedback control systems for such equipment is important due to the large number of potential applications and advantages over manual operation concerning stability and robustness. This paper aims to represent the key nonlinear dynamics of crane systems by means of a state-space fuzzy model with compact rule-base structure. The fuzzy model is useful to assist the design of a fuzzy controller based on the concept of parallel compensation. A well-posed conservative linear-matrix-inequality (LMI) feasibility problem is formulated so that a solution guarantees closed-loop Lyapunov stability, bounded control inputs, quick positioning of the supporting cart, and suppression of load oscillations and collisions. The fuzzy controller is composed by rules with linear control laws derived from local state-space models. The controller warrants asymptotic convergence of the states. Due to the nonlinear nature of the fuzzy model and controller, Jacobian linearization is avoided. The proposed fuzzy control approach for cranes has shown to be more effective and robust than an optimal quadratic controller, and able to move cargo smoothly and safely to a destination. Particularly, constrained and smoother control inputs avoid actuator saturation, and tend to increase its lifetime. Laboratory experiments using the LMI fuzzy controller and actual data validates the approach for cranes in actual scenario.     

Adaptive T–S fuzzy controller using reinforcement learning based on Lyapunov stability

In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy controller based on reinforcement learning for controlling the nonlinear dynamical systems is proposed. The parameters of the T–S fuzzy system are learned using the reinforcement learning based on the actor-critic method. This on-line learning algorithm improves the controller performance over the time, which it learns from its own faults through the reinforcement signal from the external environment and tries to reinforce the T–S fuzzy system parameters to converge. The updating parameters are developed using the Lyapunov stability criterion. The proposed controller is faster in learning than the T–S fuzzy that parameters learned using the gradient descent method under the same conditions. Moreover, it is able to handle the load changes and the system uncertainties. The test is carried out based on two mathematical models. In addition, the proposed controller is applied practically for controlling a direct current (DC) shunt machine. The results indicate that the response of the proposed controller has a good performance compared with other controllers.     

Actuator saturation control of continuous-time positive switched T–S fuzzy systems

The saturated control problem is investigated for positive switched Takagi–Sugeno (T–S) fuzzy systems with partially controllable subsystems in this paper. Based on the parallel distribution compensation (PDC) algorithm and the convex hull technology, new fuzzy control schemes are proposed for continuous-time positive switched T–S fuzzy systems (PSTSFSs) with actuator saturation. By the multiple linear co-positive Lyapunov function and the slow-fast combined mode-dependent average dwell time (MDADT) approach, sufficient conditions for the stability of continuous-time closed-loop PSTSFSs are developed, which is an extension of the results in previous literature. Furthermore, the least conservative estimation of the attraction domain of PSTSFSs is transformed into an optimization problem. Finally, three simulation examples are given to illustrate the effectiveness of the proposed saturated control schemes.     

Fuzzy logic controller aided expert relaying mechanism system

Protection and reliability enactment of electrical systems are important and emerging in power system research. Nowadays, it is very evident that the implementation of an intelligent algorithm is found in the field of substation equipment protection and relaying purposes. Majority of the researches are based on single load connected to a single feeder line and validated using simulation. A hardware based implementation and validation system will be an additional aspects. In this paper, we have discussed an expert system based intelligent relaying scheme by incorporating fuzzy algorithm in microcontroller. Purpose is to control the moving contacts of the breaker part for controlling multiple loads connected to a single feeder line. This paper reports the entire performance of intelligent relaying mechanism only considering stage - I with respect to non-fuzzy based relaying scheme and successfully achieved fastest coordination time after validating it under IEEE 13 Bus system. We have also validated the cascaded fuzzy based system and a non-fuzzy based system using ATMEL microcontroller.