双层结构PID控制器性能评估研究

针对工业过程常规PID控制回路普遍存在性能退化的现状,提出了1种基于双层结构的PID控制器性能评估方法,该方法可综合地评估PID控制器的性能.其中第1层次的评估只需最少量的过程信息,即采用最小方差基准和脉冲响应曲线方法对PID控制器的随机性性能和确定性性能两方面给出初步的评估.第2层次是基于PID结构的控制器性能评估,这个层次的评估更符合生产实际,但需要用到更多的过程知识和信息.最后将所提出的方法分别应用于仿真过程和工业实际过程.     

A dead-time compensating PID controller structure and robust tuning

In the present paper a dead-time compensating proportional-integral-derivative (DTC–PID) controller with anti-windup action is derived. The proposed controller also can be configured as a PID controller or as a dead-time compensating PI (DTC–PI) controller. For stable, integrating and unstable processes, approximated with the first-order plus dead-time (FOPDT) model, robust tuning procedure is derived for the DTC–PI controller. Optimization of the regulatory performance of the DTC–PID controller is based on the frequency response of higher-order models, under constraints on the robustness and sensitivity to measurement noise. Excellent performance/robustness trade-off is obtained for stable, integrating and unstable processes, including dead-time, as confirmed by simulations and by experimental results obtained on a laboratory thermal process.     

On some idea of a neuro-fuzzy controller

The paper presents a neuro-fuzzy technique for the design of controllers. This technique can effectively deal with two main types of knowledge which usually describe the control strategy for complex systems, that is, a qualitative, linguistic, fuzzy knowledge usually expressed in the form of linguistic rules, and a quantitative, nonfuzzy information in the form of measurements and other numerical data. The proposed technique combines artificial neural networks with fuzzy logic yielding a structure that can be called a neuro-fuzzy controller or, broadly speaking, a fuzzy neural network. The paper presents a general structure of a neuro-fuzzy controller and two essential phases of its design, that is, a learning phase and a functioning phase. In turn, a numerical example which illustrates how the proposed controller works is presented. Finally, the paper describes an application of a neuro-fuzzy control to inverter drive systems for electric vehicles. The results of simulation and experimental investigations carried out on the laboratory model of an inverter drive system are also provided.     

Adaptive neuro-fuzzy wheel slip control

Due to complex and nonlinear dynamics of a braking process and complexity in the tire–road interaction, the control of automotive braking systems performance simultaneously with the wheel slip represents a challenging problem. The non-optimal wheel slip level during braking, causing inability to achieve the desired tire–road friction force strongly influences the braking distance. In addition, steerability and maneuverability of the vehicle could be disturbed. In this paper, an active neuro-fuzzy approach has been developed for improving the wheel slip control in the longitudinal direction of the commercial vehicle. The dynamic neural network has been used for prediction and an adaptive control of the brake actuation pressure, during each braking cycle, according to the identified maximum adhesion coefficient between the wheel and road surface. The brake actuation pressure was dynamically adjusted on the level that provides the optimal level of the longitudinal wheel slip vs. the brake pressure selected by driver, the current vehicle speed, the brake interface temperature, vehicle load conditions, and the current value of longitudinal wheel slip. Thus the dynamic neural network model operates (learn, generalize and predict) on-line during each braking cycle, fuzzy logic has been integrated with the neural model as a support to the neural controller control actions in the case when prediction error of the dynamic neural model reached the predefined value. The hybrid control approach presented here provided intelligent dynamic model – based control of the brake actuation pressure in order to keep the longitudinal wheel slip on the optimum level during a braking cycle.     

Analytical structure and stability analysis of a fuzzy PID controller

Analytical structure for a fuzzy PID controller is introduced by employing two fuzzy sets for each of the three input variables and four fuzzy sets for the output variable. This structure is derived via left and right trapezoidal membership functions for inputs, trapezoidal membership functions for output, algebraic product triangular norm, bounded sum triangular co-norm, Mamdani minimum inference method, and center of sums (COS) defuzzification method. Conditions for bounded-input bounded-output (BIBO) stability are derived using the Small Gain Theorem. Finally, two numerical examples along with their simulation results are included to demonstrate the effectiveness of the simplest fuzzy PID controller.     

Adaptive control scheme for a robot manipulator: direct decoupler and PID controller

A two level adaptive controller for the control of a high speed robot is proposed. Dynamic interaction effects of the robot system are eliminated by applying a centralized adaptive multivariable decoupler. Decentralized adaptive PID controllers stabilize and eliminate the static positioning errors of the robot system. No prior knowledge of robot dynamics is required for this scheme. Output transients caused by sensitivity to the system parameter variations is reducible by adjusting the gain constants of the decoupler. Computation load for the implementation of the decoupler is minimal due to its direct nature, The effectiveness of the scheme is demonstrated by simulation results in high speed repetitive motion trackings and load change conditions.     

Gain-scheduled PID controller design

Gain scheduling (GS) is one of the most popular approaches to nonlinear control design and it is known that GS controllers have a better performance than robust ones. Following the terminology of control engineering, linear parameter-varying (LPV) systems are time-varying plants whose state space matrices are fixed functions of some vector of varying parameters. Our approach is based on considering that the LPV system, scheduling parameters and their derivatives with respect to time lie in a priori given hyper rectangles. To guarantee the performance we use the notion of guaranteed costs. The class of control structure includes centralized, decentralized fixed order output feedbacks like PID controller. Numerical examples illustrate the effectiveness of the proposed approach.     

Tuning of a neuro-fuzzy controller by genetic algorithm

Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.     

Induction motor servo drive using robust PID-like neuro-fuzzy controller

A robust PID-like neuro-fuzzy controller, which has an ability to compensate for parameter variation, is proposed and applied to the speed control of the indirect vector-controlled induction motor. The controller gains are adjusted on-line using the tuning algorithm based on an artificial neural network (ANN). And a variable learning rate algorithm is proposed to improve the tracking performance while keeping the robustness. Simulation and experimental results confirm that good dynamic performance and high robustness to parameter variation and disturbance can be achieved by means of the proposed controller.     

Cintia: a neuro-fuzzy real-time controller for low-power embeddedsystems

Cintia, a neuro-fuzzy real-time controller based on pulse stream computation techniques, is designed for applications in low-power embedded systems. The system mixes two approaches: neuro-fuzzy controllers and finite-state automata. We implement the former by means of a custom neural chip, the latter as sequential code on a traditional microcontroller. Cintia demonstrates the advantages of mixing the two approaches and the feasibility of embedded neuro-fuzzy control systems. A low-power, single-chip version is also under design     

Adaptive controller with fuzzy rules emulated structure and its applications

In this paper, the adaptive controller inspired by the neuro-fuzzy controller is proposed. Its structure, called fuzzy rules emulated network (FREN), is derived based on the fuzzy if–then rules. This structure not only emulates the fuzzy control rules but also allows the initial value of controller's parameters to be intuitively chosen. These parameters are further adjusted during system operation using a method similar to the steepest descent technique. The learning rate selection criteria based on Lyapunov's stability condition is also presented. FREN controller is applied to control various nonlinear systems, for examples, the single invert pendulum plant, the water bath temperature control, the high voltage direct current transmission system and the robotic system. Computer simulations results indicate that the proposed controller is able to control the target systems satisfactory.     

PID自适应控制

本文提出将ＰＩＤ继电自整定与神经网络相结合，共同完成ＰＩＤ自适应控制。以一个两层线性网络构造ＰＩＤ控制器，将由ＰＩＤ继电自整定法获取的ＰＩＤ参数值做适当的修正后作为网络权的初值，实现对系统的在线控制。     

Self-tuning PID/I controller

A self-tuning controller based on the method of frequency identification and intended for controlling plants with time-varying parameters under arbitrary bounded exogenous disturbances was proposed. The impact of the harmonic frequencies of the test signal on the results of identification over a prescribed time was studied. Methods to choose frequencies and an algorithm to adjust the amplitudes of the harmonics of the test signal were proposed. The duration of the identification parameters of the plant model is determined automatically depending on the intensity of the exogenous disturbance and the desired relative accuracy of identification. The results of experimental studies of the proposed controller demonstrating its efficiency were presented.     

A neuro-fuzzy controller for mobile robot navigation and multirobotconvoying

A Neural integrated Fuzzy conTroller (NiF-T) which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks is developed for nonlinear dynamic control problems. NiF-T architecture comprises of three distinct parts: (1) Fuzzy logic Membership Functions (FMF), (2) a Rule Neural Network (RNN), and (3) an Output-Refinement Neural Network (ORNN). FMF are utilized to fuzzify sensory inputs. RNN interpolates the fuzzy rule set; after defuzzification, the output is used to train ORNN. The weights of the ORNN can be adjusted on-line to fine-tune the controller. In this paper, real-time implementations of autonomous mobile robot navigation and multirobot convoying behavior utilizing the NiF-T are presented. Only five rules were used to train the wall following behavior, while nine were used for the hall centering. Also, a robot convoying behavior was realized with only nine rules. For all of the described behaviors-wall following, hall centering, and convoying, their RNN's are trained only for a few hundred iterations and so are their ORNN's trained for only less than one hundred iterations to learn their parent rule sets.     

Variable-structure PID controller for level process

A variable-structure (VS) PID controller for the level process is proposed. A methodology of analysis of its stability and performance is given. It is proposed that stability of the VS system can be approximately analyzed via the describing function method. The describing function of the VS PID controller is derived. It is shown that the system with the VS PID controller is quasi-linear. Tuning rules for the VS PI controller for the level process are given. It is shown via the theory and simulations presented that, if properly tuned, the VS PI controller has higher performance than the conventional PI controller for the process considered.     

一种参数自适应模糊RID控制器的设计与记真

提出了一种参数自适应模糊PID控制器,利用模糊推理的方法实现对PID参数的在线自动鉴定。仿真结果表明,该控制器明显地改善了控制系统的动态性能,并且此方法计算量少,易于实现,便于工程应用。     

基于SOPC的多通道智能PID控制器

针对传统的多通道数字PID控制器实时性较差的特点,本文提出一种利用FPGA技术实现多通道PID控制器的硬件设计方案。并且采用模糊自整定方法对PID控制器参数进行实时调节,实现PID控制器的自适应功能。     

PID controller design for robust performance

This note is devoted to the problem of synthesizing proportional-integral-derivative (PID) controllers for robust performance for a given single-input-single-output plant in the presence of uncertainty. First, the problem of robust performance design is converted into simultaneous stabilization of a complex polynomial family. An extension of the results on PID stabilization is then used to devise a linear programming design procedure for determining all admissible PID gain settings. The most important feature of the proposed approach is that it computationally characterizes the entire set of the admissible PID gain values for an arbitrary plant.     

新型的PID控制器参数整定方法

粒子群优化算法在进化中随种群多样性降低易出现早熟收敛等问题。针对这一问题,结合全局-局部最优模型,提出了一种改进的粒子群优化算法,称为全局-局部参数最优的粒子群优化算法。算法利用全局-局部最优惯性权重及全局-局部最优加速度常数,算法的速度更新方程被简化,性能得到改善。利用一组bench mark问题对该算法进行测试,仿真结果表明了算法的有效性和高效性。将该算法应用到对传统PID控制器的参数优化当中,仿真结果表明方法可以获得满意的控制效果,各项控制性能指标优于传统方法整定得到的PID控制器。     

Adaptive neuro-fuzzy computing technique for suspended sediment estimation

This paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in suspended sediment estimation. The monthly streamflow and suspended sediment data from two stations, Kuylus and Salur Koprusu, in Kizilirmak Basin in Turkey are used as case studies. The estimation results obtained by using the neuro-fuzzy technique are tested and compared with those of the artificial neural networks and sediment rating curves. Root mean squared errors, mean absolute errors and correlation coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neuro-fuzzy models can be employed successfully in monthly suspended sediment estimation.