A method for improving the robustness of PID control

In this paper, an effective method is proposed for robust proportional-integral-derivative (PID) control that is easily implementable on commonly used equipment such as programmable logic controller (PLC) and programmable automation controller (PAC). The method is based on a two-loop model following control (MFC) system containing a nominal model of the controlled plant and two PID controllers. Basic features exhibited by the MFC structure are presented, and a technique to tune both component controllers is given. The proposed structures have been implemented in a programmable logic controller and tested on control plants with perturbed parameters. Also, the proposed control system has been checked for its performance in cases when the operation of PID controllers is based on fuzzy logic. Tuning rules for the fuzzy controllers in the presented MFC system have been proposed. Results of tests lend support to the view that the proposed control structures may find wide application to robust control of plants with time-varying parameters.     

Indirect sliding mode control based on gray-box identification method for pneumatic artificial muscle

We present an indirect robust nonlinear controller for position-tracking control of a pneumatic artificial muscle (PAMs) testing system. The system modeling is reviewed, for which the existence of uncertain, unknown, and nonlinear terms in the internal dynamics is presented. From the obtained results, an online identification method is proposed for estimation of the internal functions with learning rules designed via a Lyapunov derivative function. A robust nonlinear controller is then designed based on the approximated functions to satisfy the control objective under the sliding mode technique. Appropriate selection of the smooth robust gain and the sliding surface ensures convergence of the tracking error to a desired level of performance. Stability of the closed-loop system is proven through another Lyapunov function. The proposed approach is verified and compared with a conventional proportional–integral–differential (PID) controller, adaptive recurrent neural network (ARNN) controller, and robust nonlinear controller in a real-time system with three different kinds of trajectories and loading. From the comparative experimental results, the effectiveness of the proposed method is confirmed with respect to transient response, steady-state behavior, and loading effect.     

基于量子粒子群算法的PID参数自整定方法

PID控制在工业生产中应用非常广泛.以直流电机模型为被控对象,提出了基于量子粒子群算法的PID参数自动整定方法.应用经典的Ziegler-Nichols方法整定PID参数,被控对象性超调大往往难以满足要求.粒子群算法是通过模拟鸟群觅食过程中的迁徙和群聚行为而提出的一种基于群体智能的全局随机搜索算法.将量子粒子群算法用于优化PID参数,并与Z-N法整定的PID控制器性能进行对比.仿真结果发现,与Z-N法相比,基于粒子群算法优化的PID控制器,系统超调明显减小.除QPSO-PID(ITSE)对应的系统具有较长调节时间外,虽然应用不同优化目标优化后的PID参数不同,控制对象的响应性能却非常相似.     

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.     

模糊工程PID在气动位置伺服中的应用

针对现有高精度气动位置伺服控制算法复杂,难于在工程上实现的问题。提出一种基于调整因子的模糊工程PID控制器,采用归一化加速度参量反映系统响应的快慢,引入变论域的思想构建模糊工程PID控制器的自调整机构。该机构根据系统误差和归一化加速度参量输出调整因子,动态调整基于工程整定法的模糊PID控制器的比例因子,以改变模糊控制器输入输出与模糊子集的映射关系,从而改善模糊控制器的动、静态性能。仿真与试验结果表明,该方法简单且具有良好的控制效果。     

Position control of a pneumatic surgical robot using PSO based 2-DOF H∞ loop shaping structured controller

This research proposed novel development of a 2-DOF H_∞ loop shaping structured controller based on Particle Swarm Optimization (PSO) that considers the closed-loop dynamic response, robustness, stability, and minimal control input in design criteria to control position of 3-DOF pneumatic surgical robot. Unlike other conventional H_∞ controllers, the proposed controller offers robustness, high performance, but cost-effective simple structure, which has recently received attention from several researchers and preferred in industrial applications. The proposed technique is simulated and experimented on a nonlinear system of a pneumatic 3-DOF surgical robot for a Minimally Invasive Surgery (MIS). Mechanical design, dynamics modeling, and system identification of the surgical robot are conducted. The simulation results verify that the proposed controller can gain a better H_∞ sub-optimal solution than the conventional 2-DOF H_∞ loop shaping controller. Also, the experiments confirm that the proposed controller is capable to tolerate the perturbed conditions and can be alternative to the conventional controllers in pneumatic controlled system     

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.     

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.     

圆柱-球体三自由度超声电机模糊自适应控制

针对圆柱-球体三自由度超声电机难以建立精确数学模型的特点,设计了模糊自适应定位控制系统。该控制系统由模糊控制器和在线参数自调整环节构成,在线参数自调整环节使该系统的动态特性、稳态性能更好地兼顾,克服了固定量化因子控制性能不理想的缺点。应用该控制器,实现了电机精确定位控制。结果表明,参数自调整模糊自适应控制器的性能优于传统PID控制器及固定量化因子的模糊控制器,得到了满意的控制效果。     

基于模糊PID控制的直流伺服系统设计与仿真

传统PID控制系统适应于受控对象能够用精确的数学模型表示,对于具有时变、非线性的直流电机伺服系统时其控制性能不理想,针对这个缺点,提出一种模糊PID控制方案,首先分析模糊控制原理及特点,其次通过制定的模糊规则等运算实现PID参数自整定,最后对某型装备直流伺服系统仿真实验,实验结果验证了模糊PID控制方法较传统PID控制方法超调量小、调节时间短、响应速度快,其动态性能得到明显改善.     

Adaptive fuzzy neural network control for motor-toggle servomechanism

In this study, the dynamic responses of an adaptive fuzzy neural network (FNN) controlled toggle mechanism is described. The toggle mechanism is driven by a permanent magnet (PM) synchronous servo motor. First, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the motor-toggle servomechanism. Since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the motor-toggle servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Moreover, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed adaptive and adaptive FNN controllers are robust with regard to uncertainties.     

T-S fuzzy controllers for nonlinear interconnected systems with multiple time delays

This paper investigates the effectiveness of a passive tuned mass damper (TMD) and fuzzy controller in reducing the structural responses subject to the external force. In general, TMD is good for linear systems. We proposed here an approach of Takagi-Sugeno (T-S) fuzzy controller to deal with the nonlinear system. To overcome the effect of modeling error between nonlinear multiple time-delay systems and T-S fuzzy models, a robustness design of fuzzy control via model-based approach is proposed in this paper. A stability criterion in terms of Lyapunov's direct method is derived to guarantee the stability of nonlinear multiple time-delay interconnected systems. Based on the decentralized control scheme and this criterion, a set of model-based fuzzy controllers is then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear multiple time-delay interconnected system and the H/sup /spl infin// control performance is achieved at the same time. Finally, the proposed methodology is illustrated by an example of a nonlinear TMD system.     

Kinematics control of a pneumatic system by hybrid fuzzy PID

In a pneumatic system, normally, the piston can stop at only two terminal endpoints. In order to extend the capabilities of the system, this research is conducted to develop a kinematics control-based pneumatic system. Both position and velocity of the pneumatic piston are controlled in such a way that the controlled piston is able to move with the specified velocity to the target position. A hybrid of fuzzy and proportional-plus-integral-plus-derivative (PID) control algorithm is proposed in this paper as the solution. The control algorithm is separated into two parts: fuzzy control and PID control. The fuzzy controller is used to control the piston when the piston locates far away from the target position whereas the PID controller is applied when the piston is near the desired position. The development starts with designing of a position sensor to detect position information of the piston. The sensor-manipulating circuit consisting of potentiometer, inverting amplifier, summing amplifier, low-pass filter and analog-to-digital converter is then designed and realized. Next, the proposed hybrid of fuzzy and PID control is implemented and programmed on the microprocessor. In order to test performance of the system, settling time and steady-state error of five control algorithms – proportional (P) control, proportional-plus-integral (PI) control, proportional-plus-derivative (PD) control, PID control, and hybrid of fuzzy and PID control – are investigated. The results from the experiments show that the proposed hybrid of fuzzy and PID control gives the most satisfied settling time and steady-state error.     

Fuzzy optimization techniques applied to the design of a digital PMSM servo drive

This paper presents a novel design approach by applying gradient optimization with fuzzy step-sizing techniques to the design of a digital permanent magnet synchronous motor (PMSM) servo drive. The servo specifications and design variables are specified and analyzed to formulate a controller optimization problem. The servo responses are then fed back to evaluate the overall system performances, which can be expressed as objective functions with respect to the servo control parameters. According to the objective functions and design specifications, the servo control parameters can be properly tuned toward their optimal values by using the proposed optimization techniques. In order to improve the convergent rate of the optimization process, a fuzzy-logic based step-size tuning strategy is presented. Because of the nonlinear property of the digital servo drives, the tuned servo control parameters may be only optimal for a particular operating point, therefore, once the optimum design is achieved, the proposed fuzzy optimizing controller can perform as an intelligent tuner for on-line gain adaptation under different loading conditions. The proposed fuzzy optimization servo tuner has been realized under a PC-MATLAB-based environment with an on-line controlled digital PMSM servo drive. Simulation and experimental results indicate that the control parameters of a digital PMSM servo drive can be optimized for its dynamic responses under various load conditions.     

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。     

A Globally Stable, Load-Independent Pressure Observer for the Servo Control of Pneumatic Actuators

Pneumatic actuators are governed by nonlinear dynamics. Thus, robust precision motion control of pneumatic systems requires model-based control techniques such as sliding-mode and/or adaptive control. These controllers typically require full-state knowledge of the system, i.e., pressure, position, velocity, and acceleration. For measuring pressure states, pneumatic servo systems require two expensive pressure sensors per axis, and hence, it makes the system economically noncompetitive with most electromagnetic types of actuation. This paper presents the development of a Lyapunov-based pressure observer for pneumatically actuated systems. The pressure observer is energy-based and has the useful feature of not requiring a model for the load of the system, i.e., it is load-independent. This pressure observer is proven to be globally stable with the added feature of having a response bandwidth equal to that of the modeled pressure dynamics. A robust observer-based controller is developed to obtain a low-cost precision pneumatic servo system. Experimental results are presented that demonstrate and validate the effectiveness of the proposed observer.     

基于模糊自适应PID控制的速度调节器设计与仿真

针对异步电动机非线性的特点,考虑到传统PID控制难以对其进行有效的控制的问题。文中设计了一种模糊自适应PID控制器,并将这种控制器应用到系统的转速调节的环节中。利用Matlab的Simulink工具搭建了模糊自适应PID控制的异步电机的仿真模型。仿真实验结果证明了设计的优越性,模糊自适应PID控制下的系统超调变小,反应速度变快,与原先的PID控制方式相比提高了系统的稳定性、动态响应性能以及鲁棒性能。     

基于RBF神经网络的永磁同步伺服电机控制系统

针对永磁同步电机控制系统,建立其磁场定向控制数学模型。运用增量式数字PID的方法实现对PMSM的传统PID控制策略。在此基础上,借助RBF神经网络的学习能力,进行PID控制器参数的自适应整定,进一步改善PID控制器的性能。同时,为提高RBF网络性能,采用粒子群算法对网络进行优化。仿真表明,与传统PID控制比较,基于RBF的PID控制系统能提高PID控制器的性能,改善了PMSM控制系统的收敛速度和跟踪精度。     

A two-layered fuzzy logic controller for systems with deadzones

Existing fuzzy control methods do not perform well when applied to systems containing nonlinearities arising from unknown deadzones. In particular, we show that a usual "fuzzy PD" controller applied to a system with a deadzone suffers from poor transient performance and a large steady-state error. In this paper, we propose a novel two-layered fuzzy logic controller for controlling systems with deadzones. The two-layered control structure consists of a fuzzy logic-based precompensator followed by a usual fuzzy PD controller. Our proposed controller exhibits superior transient and steady-state performance compared to usual fuzzy PD controllers. In addition, the controller is robust to variations in deadzone nonlinearities. We illustrate the effectiveness of our scheme using computer simulation examples.<>     

Temperature control by chip-implemented adaptive recurrent fuzzy controller designed by evolutionary algorithm

Online adaptive temperature control by field-programmable gate array (FPGA) - implemented adaptive recurrent fuzzy controller (ARFC) chip is proposed in this paper. The RFC is realized according to the structure of Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network. Direct inverse control configuration is used. To design RFC offline, evolutionary fuzzy controller using the hybrid of the Simplex method and particle swarm optimization (SPSO) is proposed. In SPSO, each RFC corresponds to a particle, and all the free parameters in RFC are optimally searched. We use the PSO to find a good solution globally, and the incorporation of the Simplex method helps find a better solution around the local region of the best solution found by PSO so far. Then, online adaptive temperature control with ARFC chip implemented by FPGA is proposed. In the ARFC chip, the consequent parameters of all rules are all tuned online using gradient descent. To verify the performance of the ARFC chip, experiments on a water bath temperature system are performed.