基于Dahlin算法变风量空调自校正PID控制

变风量空调具有时变、延时、非线性等特点,在此类系统中传统的PID控制算法难以取得最佳的控制效果.基于广义预测自校正的控制算法,控制精度高,具有较强的跟踪性能,但是以增大的计算量和频繁的调节系统参数为代价的.文中提出一种基于改进的Dahlin参数整定方法的自校正PID控制器,该算法在理论建模的基础上,采用在线的带遗忘因子最小二乘法,实现传递函数的参数估计,建立了系统PID参数之间的关系式,实现变风量空调系统的末端控制.算法仿真运算及实验表明,与传统PID及广义预测自校正控制方法相比,文中方法控制精度高于传统PID,同时计算复杂度低于通常广义预测自校正控制.     

Adaptive learning algorithm for Cerebellar model articulation controller

Cerebellar model articulation controller (CMAC) was developed two decades ago, yet lacks an adequate learning algorithm. Examining the performance of a CMAC based controller showed that the control system become unstable after a long period of real time runs. A new adaptive learning algorithm is proposed. The resultant controller is applied for the trajectory tracking control of a piezoelectric actuated tool post. The performance of the proposed controller is compared with those of conventional controllers (PI controller and the conventional CMAC based controller). The experimental results showed that performance of the CMAC based controller using the proposed learning algorithm is stable and more effective than that of the conventional controllers.     

Accommodation of time delay variations in automatic infusion ofsodium nitroprusside

A new self-tuning regulator for control of mean arterial blood pressure using sodium nitroprusside is presented. In addition to adapting to various patient response gains and time constants, the controller can accommodate variations in pure time delays, a problem that has not been addressed fully in previously proposed controllers. The control algorithm derivation is based on incorporating a self-turning controller with an adaptive discrete time delay compensator. The derivation, however, is general and can potentially be applied to other pharmacological agents in addition to sodium nitroprusside. The a priori information required for implementation of the controller is the estimate of the order of the patient response transfer function and the range of the pure time delay. An attractive feature of the controller is its capability to optimize the level of the infused drug during the transient phase of the control without creating an output offset. This feature tends to reduce the overall level of the infused drug. Hence, it may be useful when the cost of a drug or its long-term administration side effects is of concern, such as in the case of sodium nitroprusside     

Design of incremental fuzzy PI controllers for a gas-turbine plant

In this paper, incremental fuzzy proportional integral (PI) speed and temperature controllers for a heavy-duty gas-turbine plant are presented. To improve performance, an analysis of incremental fuzzy PI control is provided, and new fuzzy control rules are proposed. In applying the fuzzy PI control to a gas-turbine plant, all gains are optimized by an adaptive genetic algorithm. We show the performance improvement of the proposed controller compared with conventional PI controller via simulations.     

Hybrid fault adaptive control of a wheeled mobile robot

A fault adaptive control methodology for mobile robots is presented. The robot is modeled as a continuous system with a supervisory controller. The physical processes of the robot are modeled using bond graphs, and this forms the basis of a combined qualitative reasoning and quantitative model-based estimation scheme for online fault detection and isolation during robot operation. A hierarchical-control accommodation framework is developed for the supervisory controller that determines a suitable control strategy to accommodate the isolated fault. It is shown that for small degradations in actuation effort, a robust controller achieves fault accommodation without significant loss of performance. However, for larger faults, the supervisor needs to switch among several controllers to maintain acceptable performance. The switching stability among a set of trajectory tracking controllers is presented. Simulation results verify the proposed fault adaptive control technique for a mobile robot.     

An improved trajectory tracking decentralized adaptive controllerfor robot manipulators

A decentralized adaptive nonlinear controller for a robot manipulator is presented in this paper. Based on the promising results obtained by the decentralized adaptive PID control algorithms proposed by Seraji and other researchers, the authors redesigned the Lyapunov function, and as a result, achieved a further simplification of the control algorithm and better trajectory tracking performance. The main advantages of the proposed controller over similar controllers are the considerably faster convergence of tracking error, relatively simpler structure, and smoother control activity. Another advantage of this controller is that it only requires local position and velocity measurements, and it does not make use of the exact centralized mathematical model of the robot manipulator. Finally, the authors demonstrate through computer simulation the robustness of their controller against parameter variations and disturbances     

Synthesis of optimally sensitive systems

The ideal design objective is an optimally adaptive control which yields an optimal performance index for all values of unknown plant parameters within a given domain. An optimally sensitive (neighboring optimum) control matches the optimally adaptive control to first order in the variations of the unknown plant parameters from a given nominal set of values, In this paper an analytical synthesis procedure for feedback controllers generating the optimally sensitive control is developed. The optimal sensitivity equations are derived for obtaining opthmal sensitivity functions which are the principal quantities involved in the synthesis. A numerical example is included.     

Design of an Adaptive Controller for Microcomputer Implementation

A design procedure for an adaptive controller is described and applied to the design of a velocity controller for small dc motors. The basic concept has been to determine a small set of controllers each of which is capable of maintaining stability and acceptable performance over a specific region of motor load parameters. Optimal control theory is used to define the control coefficients while cluster analysis and decision function techniques from pattern recognition theory are used to determine each controller's region of applicability. Simulation results are presented to verify performance improvements using the design procedure. The design procedure produces an adaptive controller which is computationally feasible for implementation in small microcomputer systems.     

Adaptive time-delay controller

A time-delay controller is proposed in this letter. This controller possesses attractive properties, such as requiring only one tuning parameter. Furthermore, it can be applied to higher order systems as well as systems with dominant delay. The basic structure is integrated with an online estimation algorithm to form an adaptive time-delay controller. An experimental study is included to demonstrate its performance and merits compared with two similar controllers.     

Programmable Analog/Digital Arrays in Control and Simulation

This paper presents an investigation of dynamically reconfigurable mixed-signal circuits implementing adaptive controllers and plant simulators. Motorola Field Programmable Analog Array described in this paper are used to build adaptive controllers and plant simulators for predictive control. The reported experimental studies describe performance and programmability of the Field Programmable Analog Array necessary for application in adaptive control and simulation. The experimental structure is based on 2 parallel Field Programmable Analog Array chips, analog multiplexer and multiplexer's control logic, which is steered by a digital system such as a desktop computer or a Field Programmable Gate Array. Dynamic reconfiguration is used in this system for adaptive control or adaptive processing in general. Modeling and measurements of the transitions in the internal blocks of the two Field Programmable Analog Array chips working in parallel, and analysis of limitations resulting from hardware imperfections are presented.     

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.     

Adaptive minimum energy cognitive lighting control: Integer order vs fractional order strategies in sliding mode based extremum seeking

To achieve comfortable illumination while minimizing the energy consumption in hybrid lighting, a minimum energy point tracking algorithm is developed to achieve the minimized energy usage despite of environmental variations in this paper. A hardware-in-the-loop prototype of an adaptive minimum energy cognitive lighting control is proposed, designed and built. A sliding mode based extremum seeking controller (SM–ESC) including integer order (IO) and fractional-order (FO) strategies is firstly employed to minimize energy usage in the lights, while a PID controller is applied to maintain a light level. The performance of the designed controller is compared with both fractional order and integer order controllers which are designed based the proposed lighting system. The hardware-in-the-loop experimental results are presented to demonstrate the practicality and effectiveness of the proposed cognitive lighting control scheme.     

PID Self-Tuners: Some Theoretical and Practical Aspects

The appearance in the market of off-the-shelf PID self-tuning (PID/ST) controllers (see Cont. Eng., Oct. 1981) reflects the industry's need to satisfy increasingly stringent control objectives which can only be attained with adaptive algorithms. Further studies on the relatively mature adaptive control theory must be directed toward providing the field engineer with the fundamentals required to utilize this algorithm confidently and profitably. It is in this sense that the study of an adaptive version of the ubiquitous PID controller (with its unquestionably appealing characteristics) must be encouraged. This paper reviews the work done on PID/ST design, the major ideas are briefly discussed, and some specific algorithms presented. The performance of various PID/ST's is illustrated by simulated examples.     

基于等级域的多服务Agent协作求解问题研究

在合同网当中引入等级域的概念,建立相应的基于等级域的多服务Agent模型GF-CNM,并采用随机TOP-N算法对等级域中不同等级的各个服务Agent的等级跃迁进行了算法描述和分析。该模型能减少任务协作时引起的网络通信量,避免对不相关服务Agent求解的时间开销,并均衡协作任务的分布,在一定程度上避免了“忙者越忙,闲者越闲”的“马太效应”,有效地缓解了资源受限条件下的任务协作求解问题。     

Adaptive frequency domain control of PWM switched power lineconditioner

An adaptive frequency domain control algorithm for a pulse width modulation (PWM) switched active power line conditioner (PLC) is discussed. An active power line conditioner that minimizes harmonic current in the AC line and improves power factor to unity is recommended. A six switch pulse width modulated current source inverter implements the active PLC. Simulation results of the PLC adaptive frequency domain control using the PWM switching algorithm are presented     

Biological systems drug infusion controller using FREN with sliding bounds

In this paper, a direct adaptive control for drug infusion of biological systems is presented. The proposed controller is accomplished using our adaptive network called Fuzzy Rules Emulated Network (FREN). The structure of FREN resembles the human knowledge in the form of fuzzy IF-THEN rules. After selecting the initial value of network's parameters, an on-line adaptive process based on Lyapunov's criteria is performed to improve the controller performance. The control signal from FREN is designed to keep in the region which is calculated by the modified Sliding Mode Control (SMC). The simulation results indicate that the proposed algorithm can satisfy the setting point and the robust performance.     

Multiple-model adaptive predictive control of mean arterialpressure and cardiac output

A multiple-model adaptive predictive controller has been designed to simultaneously regulate mean arterial pressure and cardiac output in congestive heart failure subjects by adjusting the infusion rates of nitroprusside and dopamine. The algorithm is based on the multiple-model adaptive controller and utilizes model predictive controllers to provide reliable control in each model subspace. A total of 36 linear small-signal models were needed to span the entire space of anticipated responses. To reduce computation time, only the six models with the highest probabilities were used in the control calculations. The controller was evaluated on laboratory animals that were either surgically or pharmacologically altered to exhibit symptoms of congestive heart failure. During trials, the controller performance was robust with respect to excessive switching between models and nonconvergence to a single dominant model. A comparison is also made with a previous multiple-drug controller design.     

Performance Assessment of Run-to-Run EWMA Controllers

An iterative method is developed to optimize a performance criterion for best achievable performance for discrete integral controllers used in run-to-run control. An analytical expression is derived so that a realistic assessment of the given integral controller is obtained. Using the theoretical equivalence of discrete integral and exponentially weighted moving average (EWMA) controllers, the method is then extended to performance assessment of EWMA controllers. A semiconductor manufacturing example is used to illustrate the utility of the method.     

Fuzzy adaptive force control of industrial robot manipulators with position servos

A fuzzy adaptive force control algorithm is suggested for commercialized industrial robots equipped with position servo drives in cascade with a software filter for the control of acceleration/deceleration profile to avoid vibrational shocks due to a sudden start or stop, where the control algorithm is composed of a Fuzzy Interpolation Logic Controller (FILC) and a Fuzzy Adaptive Stiffness Estimator (FASE FILC determines a control action according to the magnitude of an environmental stiffness in such a way that good force response is maintained regardless of changes of environmental stiffness. Specifically, some fuzzy controllers are designed for several representative environmental stiffness values, and then a control action for an estimated environmental stiffness value which is not the same as any representative stiffness values is decided by fuzzily aggregating different control actions of those fuzzy controllers. Here, FASE plays the role of estimating an environmental stiffness value and transfers the estimated stiffness value to FILC. To show the validity of the proposed adaptive fuzzy force controller, several numerical examples and some experimental results are illustrated, where soft, medium and hard environments are considered.