Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

In this paper, a new technique called robust loop shaping-fuzzy gain scheduled control (RLS-FGS) is proposed to design an effective nonlinear controller for a long stroke pneumatic servo system. In our technique, a nonlinear dynamic model of a long stroke pneumatic servo plant is identified by the fuzzy identification method and is used as the plant for our design. The structure of local controllers is selected as PID control which is proven by many research works that this type of control has many advantages such as simple structure, well understanding, and high performance. The proposed technique uses particle swarm optimization (PSO) to find the optimal local controllers which maximize the average stability margin. In addition, performance weighting function which is normally difficult to obtain is automatically determined by PSO. By the proposed technique, the RLS-FGS controller can be designed, and the structure of local controllers is still not complicated. As seen in the simulation and experimental results, our proposed technique is better than the classical gain scheduled PID controller tuned by pole placement and the conventional fuzzy PID controller tuned by ISE method in terms of robust performance.     

压电陶瓷精密控制系统的自适应模糊控制器研究

针对压电陶瓷等非线性系统,建立压电陶瓷微精密位移系统模型,并将模糊控制器替换传统的比例、积分和微分(PID)控制器,实现对PID参数的在线自整定。对模糊控制器的控制变量及论域等级等进行了研究及制定,并对其进行MATLAB仿真。实验结果表明,模糊逻辑控制器比传统PID控制器的响应速度快,超调量小,且控制策略简单易行。     

用多元逻辑电路实现模糊自组织PID控制器

针对一类生产过程。本文提出了一种ＰＩＤ控制器参数的模糊推理自整定方案，并基于多抑逻辑电路（ＤＹＬ）得以硬件实现，仿真表明电路设计正确，控制效果明显优于Ｚｉｅｇｌｅｒ－ＮｉｃｈｏｌｓＰＩＤ控制器，且对系统参数变化有较好的自适应能力。     

A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional–integral–derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.     

基于Matlab的模糊PID控制系统设计及仿真

模糊PID控制是利用PID参数整定经验来使模糊控制器自动整定其参数,从而使PID控制器以变应变。文中采用Matlab软件设计模糊PID控制器,并应用于控制锅炉液位。通过实验仿真比较研究PID控制、模糊控制及模糊PID控制的控制效果。实验结果显示,模糊PID控制效果理想,具有较好的应用前景。     

Fuzzy Sliding-Mode Control of Active Suspensions

In this paper, a robust fuzzy sliding-mode controller for active suspensions of a nonlinear half-car model is introduced. First, a nonchattering sliding-mode control is presented. Then, this control method is combined with a single-input–single-output fuzzy logic controller to improve its performance. The negative value of the ratio between the derivative of error and error is the input and the slope constant of the sliding surface of the nonchattering sliding-mode controller is the output of the fuzzy logic controller. Afterwards, a four-degree-of-freedom nonlinear half-car model, which allows wheel hops and includes a suspension system with nonlinear spring and piecewise linear damper with dry friction, is presented. The designed controllers are applied to this model in order to evaluate their performances. It has been shown that the designed controller does not cause any problem in suspension working limits. The robustness of the proposed controller is also investigated for different vehicle parameters. The results indicate the success of the proposed fuzzy sliding-mode controller.      

Hybridized ABC-GA optimized fractional order fuzzy pre-compensated FOPID control design for 2-DOF robot manipulator

The robotic manipulator is an extremely nonlinear, multi-input multi-output (MIMO), highly coupled, and complex system wherein the parameter uncertainties and external disturbances adversely affect the performance of this system. From this, it necessitates that the controllers designed for such system must overcome these complexities. In this paper, we develop a novel fractional order fuzzy pre-compensated fractional order PID (FOFP-FOPID) controller for 2-degree of freedom (2-DOF) manipulator dealing with trajectory tracking problem. In order to optimize the controller’s parameters while minimizing integral of time absolute error (ITAE), a metaheuristic optimization technique, viz., artificial bee colony-genetic algorithm (ABC-GA) is presented. The efficacy of our proposed controller is demonstrated by comparing it with some existing controllers, such as integer order fuzzy pre-compensated PID (IOFP-PID), fuzzy PID (FPID), and conventional PID controllers. Furthermore, the robustness analysis for proposed controllers is also investigated for parameter variations and external disturbances. The simulation results indicate that FOFP-FOPID controller can not only guarantee the best trajectory tracking but also ameliorate the system robustness for parameter variations as well as external disturbances.     

基于改进Fuzzy-PID控制理论的无人机纵向飞控律设计

针对无人机飞行中存在大量的随机干扰和不确定误差等问题,常规的PID控制器不能满足高精度、高灵活性和强适应性的要求,提出了基于改进Fuzzy-PID控制理论的飞控律设计。该设计使用模糊规则下的切换开关来完成PID控制和Fuzzy控制输出量的加权,使控制具有两者的优点。通过调整模糊隶属度函数参数,可以得到系统的最佳控制方案。仿真结果表明:基于改进Fuzzy-PID控制理论的飞控律明显优于常规控制理论,具有较强的适应性和灵活性,在提高无人机的飞行控制系统性能上具有实际意义。     

模糊自适应PID控制算法分析

基于模糊控制和自适应PID控制的模糊自适应PID控制算法可在线实时调整PID参数,使系统具有模糊控制的灵活性和适应性强的优点,又具有PID控制精度高的优势。结果表明,系统动态特性好,鲁棒性强,实现简单。     

Chaos synchronization using higher-order adaptive PID controller

This paper is devoted to designing higher-order adaptive PID controllers as a new generation of PID controllers for chaos synchronization, in which second order integration and second-order derivative terms to the PID controller (PII²DD²) are employed. The five PII²DD² control gains are updated online with a stable adaptation law driven by Lyapunov’s stability theory. This is the unique advantage of the proposed approach. Furthermore, it is equipped with a novel robust control term to improve controller robustness against system uncertainties and unknown disturbances. An important feature of the proposed approach is that it is a model-free controller. In addition, to determine the control design parameters and avoid trial and error, the Teaching–learning-based optimization algorithm (TLBO) is employed to regulate these parameters and enhance the performance of the proposed controller. Based on the Lyapunov stability theory, it is proven that the proposed control scheme can guarantee the synchronization and the stability of closed-loop control system. The case study is the Duffing–Holmes oscillator. Comparative simulation results are presented which confirm the superiority of the proposed approach.     

模糊PID控制系统的设计与研究

为了解决工程中二阶系统的控制问题,在此对PID控制与模糊控制的原理进行了研究,并将二者的优势相互结合,设计了一种具有参数自整定功能的模糊PID控制系统。对PID参数初值的确定,隶属度函数的选取,模糊控制规则表的设计做了较为深入的研究。并利用Matlab/Simulink软件对控制系统进行了仿真研究。对阶跃输入下PID控制系统与该文设计的模糊PID控制系统的响应情况做出了定量的比较。结果表明对于二阶延迟系统,模糊PID控制器的超调量与调节时间均小于传统的PID控制,能显著提高控制效果。     

一种基于PLC的模糊自适应PID控制器设计

通过对模糊自适应PID控制器设计过程的详细分析,提出了一种基于PLC查表方式实现模糊自适应PID控制器的方法,实现了基于PLC的自适应模糊PID控制器的设计,并应用于实际的控制系统中。结果表明,用PLC实现模糊自适应PID控制简单实用,适于工业控制系统应用。     

自适应模糊PID在温度控制中的应用

温度控制具有非线性、大惯性、时变性等特点,常规PID控制和常规模糊控制还不能使温度的控制达到理想效果。设计一种自适应模糊PID控制器,该控制器在大偏差范围内采用模糊控制,根据偏差和偏差变化的需要实时调整PID参数,小偏差范围内采用PID精确控制。介绍自适应模糊PID控制器的构成和原理,并利用Matlab／Simulink和模糊逻辑工具箱对其进行仿真,仿真结果表明,这种自适应PID控制器既具有模糊控制灵活、响应快、适应性强的特点,又具有PID控制精度高的特点,改善了温度控制的效果。     

New hybrid fuzzy controller for direct torque control induction motor drives

A new hybrid fuzzy controller for direct torque control (DTC) induction motor drives is presented in this paper. The newly developed hybrid fuzzy control law consists of proportional-integral (PI) control at steady state, PI-type fuzzy logic control at transient state, and a simple switching mechanism between steady and transient states, to achieve satisfied performance under steady and transient conditions. The features of the presented new hybrid fuzzy controller are highlighted by comparing the performance of various control approaches, including PI control, PI-type fuzzy logic control (FLC), proportional-derivative (PD) type FLC, and combination of PD-type FLC and I control, for DTC-based induction motor drives. The pros and cons of these controllers are demonstrated by intensive experimental results. It is shown that the presented induction motor drive is with fast tracking capability, less steady state error, and robust to load disturbance while not resorting to complicated control method or adaptive tuning mechanism. Experimental results derived from a test system are presented confirming the above-mentioned claims.     

Soft pneumatic actuators adapted in multiple environments: A novel fuzzy cascade strategy for the dynamics control with hysteresis compensation

Soft pneumatic actuators (SPAs) have been widely used in the design of various soft robots due to their compliance, adaptability, and high force density characteristics. However, it is a challenge to accurately model and control such soft pneumatic robotic systems due to inherent hysteresis nonlinearity, uncertainties, and disturbances from external environments. In this paper, we propose a novel fuzzy cascade strategy to control the dynamics of bellow-type soft pneumatic actuators when working in multiple environments (air, water, and their transition process). First, the components of the soft pneumatic system including the actuator and solenoid valve are mathematically modeled using second-order transfer functions, which are derived with a system identification method. Then, the Prandtl-Ishlinskii (P-I) model is proposed to accommodate and characterize the complex hysteresis effect. In the P-I model, the parameters are identified and derived using a particle swarm optimization (PSO) method. Subsequently, an inverse P-I model is constructed and placed in the feed-forward path to compensate for the hysteresis effect. In addition to the hysteresis nonlinearity, the uncertainties and disturbances from multiple environments will also degrade the tracking performance of soft pneumatic actuators. To enhance the adaptability, especially during the trans-environment process (e.g., from air into water or the reverse), a single-input FUZZY P+ID controller is proposed and integrated into the cascade strategy aiming to improve the robustness and precisely control the system dynamics. Extensive simulations and real-world tracking experiments of soft pneumatic actuators fabricated with the fused deposition modeling (FDM) method are performed to evaluate the performance of the proposed strategy and three designed controllers (PID, fuzzy PID, and FUZZY P+ID). It is noted that the comparison of tracking results has proved that the proposed FUZZY P+ID controller with only single input has better overall performance than traditional PID and fuzzy PID controllers in terms of adaptability and robustness.     

Variable-structured robust controller by fuzzy logic forservomotors

A variable-structure robust controller whose structure is continuously changed by fuzzy logic so that the system responds quickly if the error or its rate is large and vice versa is proposed. Such a controller is insensitive to both the plant noise and the observation noise. It is applied to speed control for an induction servomotor. Experiments show that the controller is superior to both a sliding-mode controller and a proportional integral-derivative (PID) controller. The paper includes the stability analysis of the overall system and the design procedure by using Lyapunov's method     

基于模糊神经网络的拥塞控制算法研究

本文介绍一种基于模糊神经网络的主动队列管理(AQM)算法,实现网络拥塞控制。利用神经网络来实现模糊推理,可自适应修正隶属函数的参数和加权系数,优化模糊逻辑控制器,从而达到某种性能指标的最优化。仿真结果表明,采用模糊神经网络进行流量速率预测的拥塞控制策略能够使缓冲器队列长度快速收敛到目标值,并且维持小的队列震荡。结果也表明该方法与传统的PD控制器相比具有更好的性能和鲁棒性。     

机载光电跟踪系统的模糊PID控制

为了提高机载光电跟踪系统的控制性能,提出了一种模糊自适应PID控制算法。首先,针对机载光电跟踪控制系统的特点,建立了被控对象的模型。接着,对机载光电跟踪系统模糊PID控制器的设计进行了详细介绍。最后,利用经典PID控制、模糊控制、模糊PID控制3种算法对机载光电稳定跟踪系统进行仿真比较。仿真结果表明模糊PID控制算法较之前两种算法具有响应快、超调量小、抗干扰能力强、稳态性能好等优点,对机载光电跟踪系统具有较好的控制能力。     

Learning of Hybrid Fuzzy Controller for the Optical Data Storage Device

A hybrid track-seeking fuzzy controller for an optical disk drive (ODD) is proposed in this paper. The proposed hybrid fuzzy controller (HFC) smoothes the voltage applied to the sled motor and improves the track-seeking efficiency. The HFC consists of two subsystems including an intelligent time switch and a driving force controller. Both subsystems are designed based on fuzzy logic inferences. The main functions of the proposed HFC are to drive the optical head unit (OHU) to the target track neighborhood as fast as possible and smoothly park the OHU in the least time in the target track neighborhood. An automatic learning approach based on genetic algorithms (GAs) is proposed for learning the fuzzy rules for both the intelligent time switch and driving force controller. Modulated orthogonal membership functions are utilized in both fuzzy controllers to improve the GA learning efficiency. The number of parameters needed to parameterize the fuzzy rule base is greatly reduced with the modulated orthogonal membership functions. Compared to the conventional track-seeking controller currently utilized in most ODDs that employ a speed profile as the reference signal for the track-seeking feedback control system, the proposed HFC outperforms the conventional track-seeking control schemes. Experiments are performed to justify the performance comparison.     

Fuzzy error governor: A practical approach to counter actuator saturation on flexible joint robots

In this paper, a practical method to counter actuator saturation based on a fuzzy error governor is developed and a complete case study is considered. In addition to good performance, the method has two attracting properties: It does not change the structure of the main controller, and therefore, the theoretically proven characteristics of the system are untouched, and it is simply implementable in practice. The proposed controller structure is applied on a flexible joint robot (FJR). The robust stability of the closed loop system for an n-DOF FJR is thoroughly analyzed and the proposed controller is implemented on a laboratory setup to show the ease of implementation and the resulting closed-loop performance. The main controller used for the n-DOF FJR consists of a composite structure, with a PD controller on the fast dynamics and a PID controller on the slow dynamics. The bandwidth of the fast controller is decreased during critical occasions with the fuzzy logic supervisor, which adjusts the loop gain to a proper level. Using Lyapunov direct method, the robust stability of the overall system is analyzed in presence of modeling uncertainties, and it is shown that if the PD and the PID gains are tuned to satisfy certain conditions, the closed loop system becomes UUB stable.