A set of decentralized PID controllers for an n – link robot manipulator

A class of stabilizing decentralized proportional integral derivative (PID) controllers for an n-link robot manipulator system is proposed. The range of decentralized PID controller parameters for an n-link robot manipulator is obtained using Kharitonov theorem and stability boundary equations. Basically, the proposed design technique is based on the gain-phase margin tester and Kharitonov??s theorem that synthesizes a set of PID controllers for the linear model while nonlinear interaction terms involve in system dynamics are treated as zero. The stability analysis of the composite system with the designed set of decentralized PID controllers is investigated by incorporating bounding parameters of interconnection terms in LMI formulation. From the range of controller gains obtained by the proposed method, a genetic algorithm is adopted to get an optimal controller gains so that the tracking error is minimum. Simulation results are shown to demonstrate the applicability of the proposed control scheme for solution of fixed as well as time-varying trajectory tracking control problems.     

一种基于PSO算法的非线性模型预测控制方法

将微粒群优化(PSO)算法用于输入受限非线性系统,提出了一种基于PSO的非线性模型预测控制算法.该算法采用双模控制策略,将保证预测控制稳定性的终端等式约束转化为终端不等式约束,推导出使系统稳定的不变可行集.在不变集外,利用PSO算法优化求解预测控制律,使系统状态进入不变集;在不变集内,利用线性状态反馈使系统状态渐近稳定.同时对算法的稳定性进行了分析.仿真结果证明了该算法的可行性和有效性.     

Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral?derivative controller in automatic voltage regulator system

Optimal tuning of proportional?integral?derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a combined genetic algorithm (GA) and fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The problem of obtaining the optimal PID controller parameters is formulated as an optimisation problem and a real-coded genetic algorithm (RGA) is applied to solve the optimisation problem. In the proposed RGA, the optimisation variables are represented as floating point numbers in the genetic population. Further, for effective genetic operation, the crossover and mutation operators which can deal directly with the floating point numbers are used. The proposed approach has resulted in PID controller with good transient response. The optimal PID gains obtained by the proposed GA for various operating conditions are used to develop the rule base of the Sugeno fuzzy system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.     

A Bio-Inspired Global Finite Time Tracking Control of Four-Rotor Test Bench System

A bio-inspired global finite time control using global fast-terminal sliding mode controller and radial basis function network is presented in this article, to address the attitude tracking control problem of the three degree-of-freedom four-rotor hover system. The proposed controller provides convergence of system states in a pre-determined finite time and estimates the unmodeled dynamics of the four-rotor system. Dynamic model of the four-rotor system is derived with Newton’s force equations. The unknown dynamics of four-rotor systems are estimated using Radial basis function. The bio-inspired global fast terminal sliding mode controller is proposed to provide chattering free finite time error convergence and to provide optimal tracking of the attitude angles while being subjected to unknown dynamics. The global stability proof of the designed controller is provided on the basis of Lyapunov stability theorem. The proposed controller is validated by (i) conducting an experiment through implementing it on the laboratory-based hover system, and (ii) through simulations. Performance of the proposed control scheme is also compared with classical and intelligent controllers. The performance comparison exhibits that the designed controller has quick transient response and improved chattering free steady state performance. The proposed bio-inspired global fast terminal sliding mode controller offers improved estimation and better tracking performance than the traditional controllers. In addition, the proposed controller is computationally cost effective and can be implanted on multirotor unmanned air vehicles with limited computational processing capabilities.     

Designing optimal controllers for doubly fed induction generators using a genetic algorithm

This work presents a design procedure based on evolutionary computation, more specifically on a genetic algorithm combined with the formal pole placement project, to obtain optimal controllers to the rotor-side converter of doubly fed induction generators (DFIGs), in variable-speed wind generation systems connected to the electrical grid. With this procedure it is intended to improve the global system dynamic behaviour during and after the fault period, also increasing the transient stability margin of the power system and the fault ride-through capability. The control action of the DFIG converters is accomplished by proportional and integral controllers, whose gainspsila adjustment is not a trivial task, because of the high complexity of the system. The results obtained confirm the efficiency of the proposed control design procedure.     

Long run and transient analysis of a double EWMA feedback controller

The “predictor-corrector” feedback controller is a popular adjustment scheme proposed for the quality control of certain semiconductor manufacturing process steps. This controller is based on a double Exponentially-Weighted Moving Average (EWMA) scheme; thus the performance of the closed-loop system depends on the two weight parameters of the EWM A equations. In this paper, the conditions the weights must satisfy to ensure closed-loop stability are discussed. The optimal determination of the controller weights depends on a trade-off between long-run process variance and transient bias performance. It is shown that small weights, although they can guarantee stability, may result in severe, long transients, an important concern if fabrication is in small batches. An optimization model for finding the controller weights is given and numerically solved. An extension of this type of controllers to the multiple controllable factor case is described. The performance of the controller is illustrated with an application to Chemical Mechanical Polishing, a critical semiconductor manufacturing step.     

Optimisation-based control coordination of PSSs and FACTS devices for optimal oscillations damping in multi-machine power system

An optimal procedure for designing co-ordinated controllers of power system stabiliser and flexible ac transmission system devices is developed for achieving and enhancing small-disturbance stability in multi-machine power systems. A constrained optimisation approach is applied for minimising an objective function formed from selected eigenvalues of the power systems state matrix. The eigenvalue-eigenvector equations associated with the selected modes form a set of equality constraints in the optimisation. There is no need for any standard eigenvalue calculation routines, and the use of sparse Jacobian matrix in the case of large system for forming the eigenvalue-eigenvector equations leads to the sparsity formulation. Inequality constraints include those for imposing bounds on the controller parameters. Constraints which guarantee that the modes are distinct ones are derived and incorporated in the control coordination formulation using the property that eigenvectors associated with distinct modes are linearly independent. The robustness of the controllers is achieved very directly through extending the sets of equality constraints and inequality constraints in relation to selected eigenvalues and eigenvectors associated with the state matrices of power systems with loading conditions and/or network configurations different from that of the base case. Simulation results of a multi-machine power system confirm that the procedure is effective in designing controllers that guarantee and enhance the power system small-disturbance stability.     

PSO Based Multi-Objective Approach for Controlling PID Controller

CSTR (Continuous stirred tank reactor) is employed in process control and chemical industries to improve response characteristics and system efficiency. It has a highly nonlinear characteristic that includes complexities in its control and design. Dynamic performance is compassionate to change in system parameters which need more effort for planning a significant controller for CSTR. The reactor temperature changes in either direction from the defined reference value. It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature, and deviation from reference values may cause degradation of biomass quality. Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential. In this paper, a conventional Proportional Integral Derivative (PID) controller is designed. The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters. Hence, A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller's limitation. In the proposed technique, PID parameters are tuned by Particle Swarm Optimization (PSO). It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response. In this article, a multi-objective function is proposed for PSO based controller design of CSTR.     

Chattering‐ free hybrid adaptive neuro‐fuzzy inference system‐particle swarm optimisation data fusion‐based BG‐level control

In this study, a closed‐loop control scheme is proposed for the glucose–insulin regulatory system in type‐1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose–insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well‐studied. Higher‐order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non‐linear property of glucose–insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro‐fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG‐level tracking and chattering reduction in the presence of daily glucose‐level disturbances is verified.Inspec keywords: fuzzy control, variable structure systems, particle swarm optimisation, neurocontrollers, fuzzy neural nets, blood, genetic algorithms, closed loop systems, medical control systems, fuzzy reasoning, diseases, nonlinear control systems, sugarOther keywords: data fusion, adaptive neuro‐fuzzy inference systems, particle swarm optimisation, hybrid controller, desired BG‐level tracking, chattering reduction, daily glucose‐level disturbances, closed‐loop control scheme, glucose–insulin regulatory system, type‐1 diabetic mellitus patients, innovative hybrid glucose–insulin regulators, artificial intelligence, fuzzy logic, genetic algorithm, Palumbo model, blood glucose level, T1DM patients, glucose reference tracking, insulin injection, mode controllers, glucose–insulin mechanism, chattering‐free hybrid adaptive neuro‐fuzzy inference system, particle swarm optimisation data fusion‐based BG‐level control     

Optimal sub-band and strength selection for blind watermarking in wavelet domain

With increasing use of digital media, need for digital rights management has arisen. Watermarking is used to hide copyright protection information in the host medium. Hiding information to ensure digital right protection must ensure high imperceptibility and an acceptable level of robustness. In the watermark embedding, appropriate watermark strength and place selection in the host image is the most critical aspect of the whole process. Both watermark strength and place selection are considered as optimisation problems and are optimised using genetic algorithm (GA) and particle swarm optimisation (PSO). The watermark is embedded in the wavelet domain. With the proposed method optimal wavelet family, band, watermark strength and wavelet depth level are selected to ensure higher robustness and imperceptibility. The watermark is embedded in the selected bands of the wavelet packets. The band and wavelet depth is selected using GA and watermark strength is optimised using PSO method. The proposed method shows promising results against attacks on a variety of filters, i.e. low pass, high pass and median filters. Robustness results on JPEG compression and gaussian noise are also improved compared with the current approaches in practice.     

微粒群算法在自动控制系统设计中的应用

提出了将微粒群优化(Particle Swarm Optimization,PSO)算法与控制系统设计相结合的系统设计思路和方法。系统设计过程包括两个部分：首先基于历史输入输出数据,用微粒群算法建立系统的模型,然后基于得到的模型进行控制器的设计,并用微粒群算法进行控制器的参数优化整定。仿真试验结果表明,微粒群算法在控制系统设计的模型建立、控制器参数优化等方面发挥了重要的作用,简化了控制系统设计任务,提高了设计效率。     

Load-frequency control: a GA-based multi-agent reinforcement learning

The load-frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional-integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias (-) estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on - estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.     

基于GA-PSO的印版滚筒温度二自由度PID参数整定

目的为了得到最优PID控制器的整定参数,满足印版滚筒温度实际控制要求,使温度控制具有更高的精确性和准确性。方法提出一种把遗传算法、粒子群算法相结合的智能混合算法,用该算法对二自由度PID参数进行优化,并将该思想用于印版滚筒温度控制系统;基于Matlab进行仿真,并与文中其他整定方法相比较。结果通过仿真验证,文中提出方法具有良好的控制效果,与其他方法相比,具有系统响应快、调节时间短、超调量小、鲁棒性好等优点,并具有极强的抗干扰能力。其目标值跟踪特性中调节时间为143.7 s,超调量为15%;其外扰动抑制特性中调节时间为57 s,超调量为2.5%,明显优于其他方法。结论采用该融合算法可充分发挥2种算法的优点,具有优良的性能指标值,可更好地满足现场控制要求。     

模糊控制的EPS系统研究与仿真

建立了基于模糊控制理论的电动助力转向控制器多输入模型,并搭建了包括直流型助力电机在内的汽车转向系统的比较完整的仿真试验环境,然后针对标准试验要求进行仿真试验并以数据和图形显示结果.仿真分析表明,建立的控制模型具有很好的控制效果.在此模型基础上为EPS控制器设计提供控制数据表,台架试验显示具有良好的控制特性,符合设计要求.     

Automated setup planning in CAPP: a modified particle swarm optimisation-based approach

Comprehensive process planning is the key technology for linking design and the manufacturing process and is a rather complex and difficult task. Setup planning has a basic role in computer-aided process planning (CAPP) and significantly affects the overall cost and quality of machined parts. This paper presents a generative system and particle swarm optimisation algorithm (PSO) approach to the setup planning of a given part. The proposed approach and optimisation methodology analyses constraints such as the TAD (tool approach direction), the tolerance relation between features and feature precedence relations, to generate all possible process plans using the workshop resource database. Tolerance relation analysis has a significant impact on setup planning to obtain part accuracy. Based on technological constraints, the PSO algorithm approach, which adopts the feature-based representation, optimises the setup planning using cost indices. To avoid becoming trapped in local optima and to explore the search space extensively, several new operators have been developed to improve the particles’ movements, combined into a modified PSO algorithm. A practical case study is illustrated to demonstrate the effectiveness of the algorithm in optimising the setup planning.     

Fractional-Order Control of a Wind Turbine Using Manta Ray Foraging Optimization

In this research paper, an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator (DFIG) based wind energy system has been proposed. The proposed strategy used the robust Fractional-Order (FO) Proportional-Integral (PI) control technique. The FOPI control contains a non-integer order which is preferred over the integer-order control owing to its benefits. It offers extra flexibility in design and demonstrates superior outcomes such as high robustness and effectiveness. The optimal gains of the FOPI controller have been determined using a recent Manta Ray Foraging Optimization (MRFO) algorithm. During the optimization process, the FOPI controller’s parameters are assigned to be the decision variables whereas the objective function is the error racking that to be minimized. To prove the superiority of the MRFO algorithm, an empirical comparison study with the homologous particle swarm optimization and genetic algorithm is achieved. The obtained results proved the superiority of the introduced strategy in tracking and control performances against various conditions such as voltage dips and wind speed variation.     

Generating collision-free tool orientations for 5-axis NC machining with a short ball-end cutter

A novel tool orientation optimisation algorithm is proposed for 5-axis NC machining with a short ball-end cutter. It can generate collision-free and smooth tool orientations along with a safe and shortest tool length (SSTL). The use of shorter cutters without collision is a key advantage of 5-axis machining because the magnitude of tool deflection and the stability of cutting process are greatly affected by the slenderness ratio of the cutter. Existing methods can calculate the SSTL in the NC simulation process. However, the SSTL is essentially determined by the tool orientations and should be considered in the process of tool path generation. To overcome this limitation, a new tool orientation optimisation algorithm is proposed. The SSTL is determined by optimising the tool orientations under the constraints of global collision avoidance and tool orientation smoothness. The algorithm first computes the global accessibility cone and the SSTL along each accessible tool orientation. Then the tool orientations are optimised based on the discrete dynamic programming with the SSTL along the whole tool path being the optimisation objective. Finally, the tool path is generated by globally smoothing the tool orientations. Computational examples and cutting experiment are given to illustrate the validity and efficiency of the proposed algorithm.     

Direct control algorithm for doubly fed induction generators in weak grids

An algorithm utilising direct feedbacks of torque and reactive power for the control of a doubly fed induction generator (DFIG) is proposed. Although the algorithm is based on field-oriented control, the algorithm calculates the feedback and feed-forward variables in the stator reference frame to reduce the number of coordinate transformations. Since the algorithm uses a single-loop approach instead of a cascaded loop design, the tuning of the controller is easier and the response time is faster when compared to those of most existing methods. The internal model control (IMC) design method is used in the proposed scheme to formulate the controller parameters. Measures on the possible voltage variations are included to increase the robustness of the system. Computer simulation and experimental implementation are reported. The results show that the algorithm can control the torque and reactive power independently. Harmonic distortions generated by the system are not noticeable because of the use of constant switching frequency.     

Model predictive control for inventory management in biomass manufacturing supply chains

This paper presents a centralised model predictive control strategy applied to biomass inventory control in sugarcane industries. Sugarcane industries are important renewable energy producers and an adequate inventory control of their feed material (biomass) can improve energy production. Simple linear discrete-time models with dead-time are developed to predict the controlled variable behaviour. Two layers are used in the controller, in the upper one performance is optimised by an linear programming (LP) algorithm and a multivariable generalised predictive controller (GPC) or multivariable generalised predictive controller with dead-time compensation (DTC-GPC) is used in the lower level. Simulation results in general show that the proposed controllers globally optimise the system behaviour and find an optimal ordering amount for keeping stock levels. In cases of plant/model mismatch DTC-GPC can have a significant and positive impact on the control of stock levels adding one more parameter for achieving minimised oscillatory performances (bullwhip effect).     

基于PSO改进深度置信网络的滚动轴承故障诊断

针对深度置信网络(Deep Belief Network,DBN)用于轴承故障诊断时,网络层结构调试比较费时等问题,提出一种基于粒子群优化(Particle Swarm Optimization,PSO)的DBN算法,以及基于该算法的轴承故障诊断模型。该模型利用PSO算法优选DBN网络结构,并通过自适应时刻估计法微调模型参数,随后运用具有最优结构的DBN模型直接从原始振动信号中提取低维故障特征,并将其输入到Soft-max分类器中识别轴承的故障模式。该算法与支持向量机、BP神经网络、DBN、堆叠降噪自编码等方法进行对比分析,实验结果表明,PSO改进的DBN算法具有更高的准确率以及更好的鲁棒性。