Adaptive Fuzzy Control of Nonlinear in Parameters Uncertain Chaotic Systems Using Improved Speed Gradient Method

This paper presents an adaptive fuzzy controller for Nonlinear in Parameters (NLP) chaotic systems with parametric uncertainties. In the proposed controller, the unknown parameters are estimated by the novel Improved Speed Gradient (ISG) method, which is a modification of Speed Gradient (SG) algorithm. ISG employs the Lagrangian of two suitable objective functionals for on-line estimation of system parameters. The most significant advantage of ISG is that it is applicable to NLP systems and it results in a faster rate of convergence for the estimated parameters than the SG method. Estimated parameters are used to design the fuzzy controller and to calculate the Lyapunov exponents of the chaotic system adaptively. Furthermore, established on the well-known Takagi–Sugeno (T-S) fuzzy model, a LMI (Linear Matrix Inequality)-based fuzzy controller is designed and is tuned using estimated parameters and Lyapunov exponents. Throughout the controller design procedure, several important issues in fuzzy control theory including relaxed stability analysis, control input performance specifications, and optimality are taken into account. Combination of ISG parameter estimation method and T-S-based fuzzy controller yields an adaptive fuzzy controller capable to suppress uncertainties in parameters and initial states of NLP chaotic systems. Finally, simulation results are provided to show the effectiveness of the ISG and adaptive fuzzy controller on chaotic Lorenz system and Duffing oscillator.     

Robust adaptive tracking control of uncertain electrostatic micro-actuators with H-infinity performance

A novel adaptive robust tracking control scheme is proposed for a class of single-degree-of-freedom (1DOF) electrostatic micro-actuator systems in the presence of parasitics, parameter uncertainties and external disturbances. This method integrates the adaptive dynamic surface control and H-infinity control techniques. Based on this method, both the design procedure and the derived tracking controller itself are simplified, and the controller guarantees that the output tracking error satisfies the H-infinity tracking performance. In addition, the tracking accuracy can be adjusted by an appropriate choice of the design parameters of the controller. Simulation results show that prescribed transient output tracking performance can be achieved, and the closed-loop system exhibits good robustness to system uncertainties.     

Nonlinear adaptive control of direct-drive brushless DC motors andapplications to robotic manipulators

Robust adaptive nonlinear control of brushless DC (BLDC) motors is considered. A controller is designed for the plant that is robust to parametric and dynamic uncertainties in the entire electromechanical system. These uncertainties are shown to be bounded by polynomials in the states. In addition, the controller can reject any bounded unmeasurable disturbances entering the system. A model for the motor incorporating magnetic saturation is used to design voltage-level control inputs for the motor. The design methodology is based on our earlier work on adaptive control of nonlinear systems. The overall stability of the system is shown using Lyapunov techniques. The tracking error is shown to be globally uniformly bounded. The design procedure is shown to be also applicable to multilink manipulators actuated by BLDC motors. The performance of the controller is verified through simulations and comparisons with a proportional-integral-derivative-type controller are made     

Integrated design of speed-sensorless and adaptive speed controller for a brushless DC motor

The study develops a design of an integrated new speed-sensorless approach that involves a torque observer and an adaptive speed controller for a brushless dc motor (BLDCM). The system is based on the vector control drive strategy. The speed-sensorless approach first employs a load observer to estimate the disturbed load torque, and then the estimated load torque is substituted into the mechanical dynamic equation to determine the rotor speed, and thus develop a speed-sensorless algorithm. Additionally, the mechanical rotor inertia constant and the friction coefficient, which are the inputs of the load observer, are estimated using the recursive least-square rule. Therefore, the proposed speed-sensorless approach is unaffected by the time-variant motor parameters nor is affected by the integrator drift problem. It also has a simpler computing algorithm than the extended Kalman filter for estimating the speed. The modified model reference adaptive system algorithm, an adaptive control algorithm, is adopted as a speed controller of the BLDCM to improve the performance of the speed-sensorless approach. Simulation and experimental results confirm that the performance of the design of a new integrated speed-sensorless approach and the adaptive speed controller is good.     

Adaptive Fuzzy Controller of the Overhead Cranes With Nonlinear Disturbance

Overhead cranes are common industrial structures that are used in many factories and harbors. They are usually operated manually or by some conventional control methods, such as the optimal and PLC-based methods. The theme of this paper is to provide an effective all-purpose adaptive fuzzy controller for the crane. This proposed method does not need the complex dynamic model of the crane system, but it uses trolley position and swing angle information instead to design the fuzzy controller. An adaptive algorithm is provided to tune the free parameters in the crane control system. The ways to speed the transportation and reduce the computational efforts are also given. Therefore, the designing procedure of the proposed controller will be very easy. External disturbance, such as the wind and the hit, which always deteriorates the control performance, is also discussed in this paper to verify the robustness of the proposed adaptive fuzzy algorithm. At last, several experimental results with different wire length and payload weight compare the feasibility and effectiveness of the proposed scheme with conventional methods     

Adaptive control of arterial blood pressure with a learningcontroller based on multilayer neural networks

The authors discuss a two-model multilayer neural network controller for adaptive control of mean arterial blood pressure (MABP) using sodium nitroprusside. A model with an autoregressive moving average (ARMA), representing the dynamics of the system, and a modified backpropagation training algorithm are used to design the control system to meet specified objectives of design (settling time and undershoot/overshoot) and clinical constraints. The controller is associated with a weighting-determinant unit (WDU) to determine and update the output weighting factor of the parallel two-model neural network for adequate control action and a control-signal modification unit (CMU) to comply with clinical constraints and to suppress the effect of adverse noise and to improve the WDU performance. Extensive computer simulations indicate satisfactory performance and robustness of the proposed controller in the presence of much noise, over the full range of plant parameters, uncertainties, and large variations of parameters     

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。     

Development and application of an integrated framework for small UAV flight control development

This paper presents an integrated framework for small Unmanned Aerial Vehicle (UAV) flight control development. The approach provides a systematic procedure for flight control design process with a set of design tools that enables control engineers to rapidly synthesize, analyze and validate a candidate controller design. A model-based environment integrated with control synthesis, off-line and real-time simulation is developed for flight control synthesis, analysis and testings. The effectiveness of the proposed integrated framework is demonstrated by applying the framework approach to a small UAV testbed. Software-in-the-loop, processor-in-the-loop and flight testings are conducted with the synthesized controller implemented. Closed-loop performance and robustness results obtained are presented.     

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.     

Fuzzy adaptive vector control of induction motor drives

This paper deals with the design and experimental realization of a model reference adaptive control (MRAC) system for the speed control of indirect field-oriented (IFO) induction motor drives based on using fuzzy laws for the adaptive process and a neuro-fuzzy procedure to optimize the fuzzy rules. Variation of the rotor time constant is also accounted for by performing a fuzzy fusion of three simple compensation strategies. A performance comparison between the new controller and a conventional MRAC control scheme is carried out by extensive simulations confirming the superiority of the proposed fuzzy adaptive regulator. A prototype based on an induction motor drive has been assembled and used to practically verify the features of the proposed control strategy     

Multiple-Model Adaptive Control of Blood Pressure Using Sodium Nitroprusside

This paper presents a multiple-model adaptive control procedure for sodium nitroprusside regulation of arterial pressure. Pole-placement, via state-variable feedback, is included in the controller to achieve desired performance characteristics. A Smith predictor in the controller effectively removes the infusion delay time, thus simplifying the control analysis and design. Proportional-plus-integral control is used to achieve zero steady-state error. Computer simulations on linear transfer function models with varying gains, time constants, and infusion delays demonstrate the robustness of this multiple-model controller. Additional simulations on nonlinear, pulsatile-flow cardiovascular models lend further support to the ultimate use of this controller for blood-pressure regulation.     

A Model-Based Direct Power Control for Three-Phase Power Converters

The direct power control (DPC) technique has been widely used as a control strategy for three-phase power rectifiers due to its simplicity and good performance. DPC uses the instantaneous active and reactive power to control the power converter. The controller design has been proposed as a direct control with a lookup table and, in recent works, as an indirect control with an inner control loop with proportional-plus-integral controllers for the instantaneous active and reactive power errors. In this paper, a model-based DPC for three-phase power converters is designed, obtaining expressions for the input control signal, which allow the design of an adaptive control law that minimizes the errors introduced by parameter uncertainties as the smoothing inductor value or the grid frequency. A controller design process, a stability study of the system, and experimental results for a synchronous three-phase power rectifier prototype are presented to validate the proposed controller.     

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.     

Model Reference Adaptive Control Design for a Shunt Active-Power-Filter System

In this paper, model reference adaptive control (MRAC) is proposed for a single-phase shunt active power filter (APF) to improve line power factor and to reduce line current harmonics. The proposed APF controller forces the supply current to be sinusoidal, with low current harmonics, and to be in phase with the line voltage. The advantages of using MRAC over conventional proportional-integral control are its flexibility, adaptability, and robustness; moreover, MRAC can self-tune the controller gains to assure system stability. Since the APF is a bilinear system, it is hard to design the controller. This paper will solve the stability problem when a linearization method is used to solve the nonlinearity of the system. Moreover, by using Lyapunov's stability theory and Barbalat's lemma, an adaptive law is designed to guarantee an asymptotic output tracking of the system. To verify the proposed APF system, a digital signal controller (dsPIC30F4012) is adopted to implement the algorithm of MRAC, and a 1-kVA laboratory prototype is built to test feasibility. Experimental results are provided to verify the performance of the proposed APF system.     

Adaptive Approach to Motion Controller of Linear Induction Motor with Friction Compensation

In this paper, we propose a nonlinear adaptive controller and an adaptive backstepping controller for linear induction motors to achieve position tracking. A nonlinear transformation is proposed to facilitate controller design. In addition, the very unique end effect of the linear induction motor is also considered and is well taken care of in our controller design. We also consider the effect of friction dynamics and employ observer-based compensation to cope with the friction force. A stability analysis based on Lyapunov theory is also performed to guarantee that the controller design here can stabilize the system. Also, the computer simulations and experiments are conducted to demonstrate the performance of our various controller design.     

Adaptive Fuzzy Output-Feedback Tracking Control for a Class of Switched Stochastic Nonlinear Time-Delay Systems

In this paper, a design scheme for an adaptive fuzzy tracking controller is proposed for a class of switched stochastic nonlinear time-delay systems via dynamic output-feedback. First, a reduced-order observer is introduced to estimate the unmeasurable states of the switched system. During the adaptive controller design procedure, an appropriate stochastic Lyapunov–Krasovskii functional deals with the time-delay terms, and fuzzy logic systems are employed to approximate the unknown nonlinearities. Based on the designed controller, the semi-globally uniform ultimate boundedness of all the closed-loop signals is guaranteed and the tracking error converges to a small neighborhood of the origin. Finally, a simulation example is given to illustrate the validity of the proposed approach.     

A nonlinear speed control for a PM synchronous motor using a simpledisturbance estimation technique

A nonlinear speed control for a permanent-magnet (PM) synchronous motor using a simple disturbance estimation technique is presented. By using a feedback linearization scheme, the nonlinear motor model can be linearized in the Brunovski canonical form, and the speed controller can be easily designed based on the linearized model. This technique, however, gives an undesirable output performance under the mismatch of the system parameters and load conditions. An adaptive linearization technique and a sliding-mode control technique have been reported. Although good performance can be obtained, the controller designs are quite complex. To overcome this drawback, the controller parameters are estimated by using a disturbance observer theory where the disturbance torque and flux linkage are estimated. Since only the two reduced-order observers are used for the parameter estimation, the observer designs are considerably simple and the computational load of the controller for parameter estimation is negligibly small. The nonlinear disturbances caused by the incomplete linearization can be effectively compensated by using this control scheme. Thus, a desired dynamic performance and a zero steady-state error can be obtained. The proposed control scheme is implemented on a PM synchronous motor using a digital signal processor (TMS320C31) and the effectiveness is verified through the comparative simulations and experiments     

自适应压电振动盘控制器设计

振动给料机是工业控制自动化中常用的设备,压电振动盘是目前效率和稳定性最高的振动给料机,但在使用过程中存在共振点变化导致效率下降的问题。针对该问题,设计了适用于压电振动盘的自适应控制器。该控制器能够对压电振动盘的振动频率和幅值进行连续调节,模糊PID控制使得振动盘输出幅值保持稳定,自适应算法使振动盘始终工作在最佳振动频率。控制器采用准谐振开关电源,可以消除电网电压波动对系统工作的影响。设计结果表明:该控制器工作性能稳定,驱动效率高且产生的噪音较小。     

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。     

Adaptive robust pressure control of variable displacement axial piston pumps with a modified reduced-order dynamic model

Variable displacement axial piston pumps (VDAPPs) are wildly used in mobile working machines and they play a key role in machine’s energy-saving load sensing (LS) systems. Typically, electric load sensing (ELS) systems utilize traditional linear control methods, which only can realize limited control flexibility and performance. This study proposes and experimentally verifies an adaptive robust pressure control strategy for a VDAPP system. To facilitate the model-based controller design, a modified reduced-order dynamic modeling of VDAPPs is proposed. Furthermore, an adaptive robust backstepping control strategy is designed to deal with the dynamic nonlinearities and parametric uncertainties of the VDAPP system for achieving accurate pressure tracking. The controller design consists of two steps, processing the pump pressure tracking and the axial angle control, respectively. Comparative experiments and simulations with different working conditions were performed to validate the advantages of the proposed control strategy. The proposed controller achieved higher pressure tracking accuracy and it showed great capability in dealing with dynamic nonlinearities, uncertainties, and time-varying disturbances.