基于模糊干扰观测器的电动Stewart平台自适应模糊控制

建立了一个电动Stewart平台的统一动力学模型,并基于它设计了一种新型的自适应模糊控制算法。这个统一的动力学模型在任务空间中使用了Newton-Euler方法建立,同时结合了平台动力学和执行器动力学模型。自适应模糊控制算法使用计算力矩方法设计运动平台标称模型的逆动力学控制器,然后使用基于模糊干扰观测器的自适应模糊控制器对模型的不确定性和外部扰动进行补偿。通过数值仿真分析表明,在不引入高增益控制器的情况下,成功地消除了平台参数的不确定性和外部干扰的影响,保证了平台的跟踪性能。     

Interval type-2 fuzzy PID controller for uncertain nonlinear inverted pendulum system

In this paper, the interval type-2 fuzzy proportional–integral–derivative controller (IT2F-PID) is proposed for controlling an inverted pendulum on a cart system with an uncertain model. The proposed controller is designed using a new method of type-reduction that we have proposed, which is called the simplified type-reduction method. The proposed IT2F-PID controller is able to handle the effect of structure uncertainties due to the structure of the interval type-2 fuzzy logic system (IT2-FLS). The results of the proposed IT2F-PID controller using a new method of type-reduction are compared with the other proposed IT2F-PID controller using the uncertainty bound method and the type-1 fuzzy PID controller (T1F-PID). The simulation and practical results show that the performance of the proposed controller is significantly improved compared with the T1F-PID controller.     

RBFNDOB-based neural network inverse control for non-minimum phase MIMO system with disturbances

An adaptive control strategy combining neural network inverse controller (NNIC) with RBFN disturbance observer (RBFNDOB) is developed for a multi-input-multi-output (MIMO) system with non-minimum phase, internal and external disturbances in this paper. Since the inverse model of system is unstable due to the non-minimum phase, a pseudo-plant is constructed, then the RBFN is used to identify the inverse model of pseudo-plant, which can track the parameter variations of system. By copying the structure and parameters of the identifier, the NNIC is obtained. Cascading the NNIC with the original plant, the MIMO system can be decoupled and linearized into independent SISO systems. For the independent decoupled system, the RBFNDOB employs a RBFN to observe the external disturbances and this estimate value is used as a feed-forward compensation term in controller. The case study on ball mill grinding circuit is presented. The effectiveness of the proposed method is demonstrated by simulation results and comparisons.     

Fault tolerant control for modified quadrotor via adaptive type-2 fuzzy backstepping subject to actuator faults

In this paper, a robust attitude and position control of a novel modified quadrotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against actuator faults than conventional quad-rotor UAV has been developed. A robust backstepping controller with adaptive interval type-2 fuzzy logic is proposed to control the attitude and position of the modified quadrotor under actuator faults. Besides globally stabilizing the system amid other disturbances, the insensitivity to the model errors and parametric uncertainties are the asset of the backstepping approach. The adaptive interval type-2 fuzzy logic as fault observer can effectively estimate the lumped faults without the knowledge of their bounds for the modified quadrotor UAV. Additionally, the type-2 fuzzy systems are utilized to approximate the local nonlinearities of each subsystem under actuator faults, next and in order to achieve the expected tracking performance, we used Lyapunov theory stability and convergence analysis to online adjust adaptive laws. As a result, the uniformly ultimate stability of the modified quadrotor system is proved. Finally, the performances of the proposed control method are evaluated by simulation and the results demonstrate the effectiveness of the proposed control strategy for the modified quadrotor in vertical flights in presence of actuator faults.     

Energy efficient walking control for biped robots using interval type-2 fuzzy logic systems and optimized iteration algorithm

An energy efficient approach is proposed for the walking control of bipedal robots. To compensate the ZMP error caused by model uncertainties and external disturbances, we design a new walking controller in this paper. Different from currently available control schemes for cancelling ZMP error, our newly proposed one additionally incorporates a fuzzy logic systems(FLSs) mechanism and an iterative mechanism. By employing FLSs to deduce Center of Mass(CoM) correction according to ZMP error and designing iterative mechanism to compute the optimal joint position, the newly proposed controller exhibits an excellent performance. To tackle the control difficulties arising from physical constraints of actuators and hard-to-stabilization of biped robot, an optimized control algorithm is included in the iterative mechanism to guarantee the convergence to the optimal solution. Moreover, the interval type-2 FLSs are adopted to handle the uncertainties. Finally, the experiment results are provided to validate the proposed control scheme.     

A novel interval type-2 fractional order fuzzy PID controller: Design,performance evaluation,and its optimal time domain tuning

In this paper, a novel concept of an interval type-2 fractional order fuzzy PID (IT2FO-FPID) controller, which requires fractional order integrator and fractional order differentiator, is proposed. The incorporation of Takagi-Sugeno-Kang (TSK) type interval type-2 fuzzy logic controller (IT2FLC) with fractional controller of PID-type is investigated for time response measure due to both unit step response and unit load disturbance. The resulting IT2FO-FPID controller is examined on different delayed linear and nonlinear benchmark plants followed by robustness analysis. In order to design this controller, fractional order integrator-differentiator operators are considered as design variables including input-output scaling factors. A new hybridized algorithm named as artificial bee colony-genetic algorithm (ABC-GA) is used to optimize the parameters of the controller while minimizing weighted sum of integral of time absolute error (ITAE) and integral of square of control output (ISCO). To assess the comparative performance of the IT2FO-FPID, authors compared it against existing controllers, i.e., interval type-2 fuzzy PID (IT2-FPID), type-1 fractional order fuzzy PID (T1FO-FPID), type-1 fuzzy PID (T1-FPID), and conventional PID controllers. Furthermore, to show the effectiveness of the proposed controller, the perturbed processes along with the larger dead time are tested. Moreover, the proposed controllers are also implemented on multi input multi output (MIMO), coupled, and highly complex nonlinear two-link robot manipulator system in presence of un-modeled dynamics. Finally, the simulation results explicitly indicate that the performance of the proposed IT2FO-FPID controller is superior to its conventional counterparts in most of the cases.     

Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network – A novel structure

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs.     

自调整模糊PID及其在pH值控制中的仿真研究

酸碱中和反应中pH值变化的严重非线性及时滞特性给其控制带来了极大的困难。针对这一难题,采用自调整模糊PID控制算法对其进行控制,在传统PID控制器的基础上加入了模糊推理机制,由于模糊控制无需对象的精确模型,因此其具有很好的自适应特性,并且能够克服非线性因素带来的影响,具有较强的鲁棒性。对洗衣粉生产的磺化、分酸工段中的pH值控制的仿真实验结果验证了自调整模糊PID控制器在pH值控制中具有较强的鲁棒性和抗干扰能力。     

具有迟滞和蠕变特性压电作动器的模糊自适应逆控制方法

针对具有迟滞和蠕变特性的压电作动器非线性模型,提出了一种前馈控制和反馈控制相结合的自适应模糊逆控制方案。在前馈控制器中压电作动器的迟滞和蠕变非线性特性的逆模型由自适应模糊逻辑系统近似;在反馈控制器中比例控制器用来调节压电作动器的输出误差。该方法可以实时补偿压电作动器的迟滞和蠕变特性,减少作动器跟踪误差。仿真计算结果表明了该方法的有效性。     

Fault tolerant control based on interval type-2 fuzzy sliding mode controller for coaxial trirotor aircraft

In this paper, a robust controller for a Six Degrees of Freedom (6 DOF) coaxial trirotor helicopter control is proposed in presence of defects in the system. A control strategy based on the coupling of the interval type-2 fuzzy logic control and sliding mode control technique are used to design a controller. The main purpose of this work is to eliminate the chattering phenomenon and guaranteeing the stability and the robustness of the system. In order to achieve this goal, interval type-2 fuzzy logic control has been used to generate the discontinuous control signal. The simulation results have shown that the proposed control strategy can greatly alleviate the chattering effect, and perform good reference tracking in presence of defects in the system.     

Trajectory and vibration control of a flexible joint manipulator using interval type-2 fuzzy logic

This study presents the development of a novel interval type-2 fuzzy logic controller for real time trajectory and vibration control of a flexible joint manipulator. The controller is designed on the Mamdani based interval type-2 fuzzy logic toolbox, which is developed by the authors, using interval triangular membership functions and Karnik–Mendel type reduction algorithm. The closed-loop stability of the system is proved based on Lyapunov stability theorem. In order to observe the effectiveness and robustness of the proposed controller to variations of system parameters (change in link length and payload), the experimental results of interval type-2 and conventional type-1 fuzzy logic controllers are compared. The results show that proposed controller clearly improves the link vibration and trajectory tracking behavior of the system.     

A two-stage servo feed controller of micro-EDM based on interval type-2 fuzzy logic

In order to improve the performance of micro-electrical discharge machining (micro-EDM), the most important issue is to develop a highly stable servo control system. In order to account for the characteristics of high frequency, serious signal distortion, and high noise in micro-EDM process, type-2 fuzzy logic sets are introduced which is able to handle these uncertainties effectively. Based on interval type-2 fuzzy sets theory, a two-stage servo feed controller is designed. The first stage of the controller is for detecting the discharge state for a single sample point, and the second stage is used to obtain the servo feed speed. In addition, the discrimination and statistical methods for discharge states of sample points are proposed to obtain the proportion of each discharge state in an analytical period. Experiments demonstrate that the new controller can obviously improve the processing efficiency of micro-EDM compared with traditional controllers. Therefore, the proposed interval type-2 fuzzy logic-based two-stage servo feed controller is an effective way to enhance the efficiency and stability of micro-EDM and meanwhile to achieve good processing quality.     

Implementation of an adaptive fuzzy compensator for coupled tank liquid level control system

In this paper, an adaptive fuzzy control (AFC) system is proposed to realize level position control of two coupled water tanks, often encountered in practical process control. The fuzzy control system includes an adaptive model identifier and controller. The gains of AFC are obtained by using the fuzzy identifier model which is defined by real system outputs and control inputs. The parameters of fuzzy identifier model are adjusted online by using recursive least square algorithm. Because the controller has a recursive form it treats model uncertainties and external disturbances in an implicit way. Thus there is no need to specify uncertainty and disturbances for this controller design in advance. A well-tuned conventional proportional integral (PI) controller is also applied to the two coupled tank system for comparison with the AFC system. Experimentation of the coupled tank system is realized in two different configurations, namely configuration #1 and configuration #2 respectively. In configuration #1, the water level in the top tank is controlled by a pump. In configuration #2, the water level in the bottom tank is controlled by the water flow coming out of the top tank. Experimental results prove that the AFC shows better trajectory tracking performance than PI controller in that the plant transient responses to the desired output changes have shorter settling time and smaller magnitude overshot/undershoot. Robustness of the AFC with respect to water level variation and capability to eliminate external disturbances are also achieved. Experimental results show that AFC is a strong and a practical choice for liquid level control.     

Active fault tolerant control based on interval type-2 fuzzy sliding mode controller and non linear adaptive observer for 3-DOF laboratory helicopter

In this paper, a robust controller for a three degree of freedom (3 DOF) helicopter control is proposed in presence of actuator and sensor faults. For this purpose, Interval type-2 fuzzy logic control approach (IT2FLC) and sliding mode control (SMC) technique are used to design a controller, named active fault tolerant interval type-2 Fuzzy Sliding mode controller (AFTIT2FSMC) based on non-linear adaptive observer to estimate and detect the system faults for each subsystem of the 3-DOF helicopter.The proposed control scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller.Exponential stability of the closed loop is guaranteed by using the Lyapunov method. The simulation results show that the AFTIT2FSMC can greatly alleviate the chattering effect, providing good tracking performance, even in presence of actuator and sensor faults.     

Design of interval type-2 fuzzy precompensated PID controller applied to two-DOF robotic manipulator with variable payload

The interval type-2 fuzzy logic controller (IT2-FLC), with footprint of uncertainty (FOU) in membership functions (MF), has increasingly recognized for controlling uncertainties and nonlinearities. Within the ambit of this, the efficient interval type-2 fuzzy precompensated PID (IT2FP-PID) controller is designed for trajectory tracking of 2-DOF robotic manipulator with variable payload. A systematic strategy for optimizing the controller parameters along with scaling factors and the antecedent MF parameters for minimization of performance metric integral time absolute error (ITAE) is presented. Prominently, recently proposed optimization technique hybridizing grey wolf optimizer and artificial bee colony algorithm (GWO–ABC) is utilized for solving this high-dimensional constrained optimization problem. In order to witness effectiveness, the performance is compared with type-1 fuzzy precompensated PID (T1FP-PID), fuzzy PID (FPID), and conventional PID controllers. More significantly, the robustness of IT2FP-PID is examined for payload variation, model uncertainties, external disturbance, and noise cancellation. After experimental outcome, it is inferred that IT2FP-PID controller outperforms others and can be referred as a viable alternative for controlling nonlinear complex systems with higher uncertainties.     

Fuzzy Lyapunov Reinforcement Learning for Non Linear Systems

We propose a fuzzy reinforcement learning (RL) based controller that generates a stable control action by lyapunov constraining fuzzy linguistic rules. In particular, we attempt at lyapunov constraining the consequent part of fuzzy rules in a fuzzy RL setup. Ours is a first attempt at designing a linguistic RL controller with lyapunov constrained fuzzy consequents to progressively learn a stable optimal policy. The proposed controller does not need system model or desired response and can effectively handle disturbances in continuous state-action space problems. Proposed controller has been employed on the benchmark Inverted Pendulum (IP) and Rotational/Translational Proof-Mass Actuator (RTAC) control problems (with and without disturbances). Simulation results and comparison against a) baseline fuzzy Q learning, b) Lyapunov theory based Actor-Critic, and c) Lyapunov theory based Markov game controller, elucidate stability and viability of the proposed control scheme.     

基于阶跃响应的反向响应过程的预测函数控制

通过分析被控过程产生反向响应的内在机理,基于过程阶跃响应系数利用预测函数控制算法实现反向响应过程的控制。将系统控制结构归结为内模控制结构,并在此基础上给出控制系统的鲁棒稳定性证明。该方法的优点是所得控制器结构简单、参数易于调节,且所给方法也适用于其他非最小相位系统的控制。实验结果证实该方法有很强的抗干扰能力和较好鲁棒性。     

A type-2 fuzzy expert system based on a hybrid inference method for steel industry

In this paper, a novel type-2 fuzzy expert system for prediction the amount of reagents in desulfurization process of a steel industry in Canada is developed. In this model, the new interval type-2 fuzzy c-regression clustering algorithm for structure identification phase of Takagi–Sugeno (T–S) systems is presented. Gaussian Mixture Model is used to generate partition matrix in clustering algorithm. Then, an interval type-2 hybrid fuzzy system, which is the combination of Mamdani and Sugeno method, is proposed. The new hybrid inference system uses fuzzy disjunctive normal forms and fuzzy conjunctive normal forms for aggregation of antecedents. A statistical test, which uses least square method, is implemented in order to select variables. In order to validate our method, we develop three system modeling techniques and compare the results with our proposed interval type-2 fuzzy hybrid expert system. These techniques are multiple regression, type-1 fuzzy expert system, and interval type-2 fuzzy TSK expert system. For tuning parameters of the system, adaptive-network-based fuzzy inference system is used. Finally, neural network is utilized in order to reduce error of the system. The results show that our proposed method has less error and high accuracy.     

电液伺服扭振试验机模糊PID控制仿真研究

为提高电液伺服扭振试验机控制系统抗参数变化和干扰的能力,保证控制精度,提出了应用于本试验机控制系统的模糊PID控制。首先根据控制系统结构原理建立了控制对象的数学模型。然后设计了模糊PID控制器,使用正交试验法对PID控制参数进行整定,以获得较优的PID控制参数,同时使用模糊控制器对PID的控制效果加以修正。最后利用MATLAB／Simulink对控制系统进行了仿真。仿真结果表明：模糊PID控制对于参数的变化和干扰,能实现控制参数的自调整．具有较好的控制效果。     

一种用于pH值控制的非线性系统的实现

分析了pH值曲线的非线性特点,对PID控制和前馈控制中的非线性控制因素分别采用非线性增益和模糊控制的方法来实现,并在实际运用中取得了较好的效果。