Showing papers on "Proportional control published in 2008"

Six-DOF Impedance Control of Dual-Arm Cooperative Manipulators

Abstract: In this paper, the problem of impedance control of dual-arm cooperative manipulators is studied. A general impedance control scheme is adopted, which encompasses a centralized impedance control strategy, aimed at conferring a compliant behavior at the object level, and a decentralized impedance control, enforced at the end-effector level, aimed at avoiding large internal loading of the object. Remarkably, the mechanical impedance behavior is defined in terms of geometrically consistent stiffness. The overall control scheme is based on a two-loop arrangement, where a simple proportional integral derivative inner motion loop is adopted for each manipulator, while an outer loop, using force and moment measurements at the robots wrists, is aimed at imposing the desired impedance behaviors. The developed control scheme is experimentally tested on a dual-arm setup composed of two 6-DOF industrial manipulators carrying a common object. The experimental investigation concerns the four different controller configurations that can be achieved by activating/deactivating the single impedance controllers.

Three-Phase Photovoltaic System With Three-Level Boosting MPPT Control

Abstract: This paper proposes a three-phase photovoltaic (PV) system with three-level boosting maximum power point tracking (MPPT) control. A simple MPPT control using a power hysteresis tracks the maximum power point (MPP), giving direct duty control for the three-level boost converter. The three-level boost converter reduces the reverse recovery losses of the diodes. Also, a weighted-error proportional and integral (PI) controller is suggested to control the dc link voltage faster. All algorithms and controllers were implemented on a single-chip microprocessor. Experimental results obtained on a 10-kW prototype show high performance, such as an MPPT efficiency (MPPT effectiveness) of 99.6%, a near-unity power factor, and a power conversion efficiency of 96.2%.

Control Experiment of a Wheel-Driven Mobile Inverted Pendulum Using Neural Network

Abstract: The mobile inverted pendulum is developed and tested for an intelligent control experiment of control engineers. Intelligent control algorithms are tested for the control experiment of a low cost mobile inverted pendulum system. Online learning and control using neural network of a wheel-driven mobile inverted pendulum system is presented. Neural network learning algorithm is embedded on a digital signal processing board along with primary proportional-integral-differential controllers to achieve real time control. Without knowing dynamics of the system, uncertainties in system dynamics are compensated by neural network in an online fashion. Digital filters are designed for a gyro sensor to compensate for a phase lag. Experimental studies of balancing the pendulum and tracking the desired trajectory of the cart for one dimensional motion are conducted. Results show the robustness of the proposed controller even when outer impacts as disturbance are present.

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

Abstract: 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.

PID Control Using Presearched Genetic Algorithms for a MIMO System

Abstract: This correspondence presents a new approach that utilizes evolutionary computation and proportional-integral differential (PID) control to a multi-input multioutput (MIMO) nonlinear system. This approach is demonstrated through a laboratory helicopter called the twin rotor MIMO system (TRMS). The goals of control are to stabilize the TRMS in significant cross-couplings, reach a desired position, and track a specified trajectory efficiently. The proposed control scheme includes four PID controllers with independent input. In order to reduce total error and control energy, all parameters of the controller are obtained by a real-value-type genetic algorithm (RGA) with a system performance index as the fitness function. The system performance index was applied to the integral of time multiplied by the square error criterion to build a suitable fitness function in the RGA. We also investigated a new method for the RGA to solve more than ten parameters in the control scheme. The initial search range of the RGA was obtained by a nonlinear control design (NCD) technique. The NCD provided a narrow initial search range for the RGA. This new method led chromosomes to converge to optimal solutions more quickly in a complicated hyperplane. Computer simulations show that the proposed control scheme conquers system nonlinearities and influence between two rotors successfully.

Performance Improvement of Industrial Drives With Mechanical Elasticity Using Nonlinear Adaptive Kalman Filter

Abstract: This paper deals with the application of the adaptive control structure for torsional vibration suppression in the drive system with an elastic coupling The proportional-integral speed controller and gain factors of two additional feedback loops, from the shaft torque and load side speed, are tuned on-line according to the changeable load side inertia This parameter, as well as other mechanical variables of the drive system (load side speed, torsional and load torques), are estimated with the use of the developed nonlinear extended Kalman filter (NEKF) The initial values of the Kalman filter covariance matrices are set using the genetic algorithm Then, to ensure the smallest state and parameter estimation errors, the on-line adaptation law for the chosen element of the state covariance matrix of the NEKF is proposed The described control strategy is tested in an open and a closed-loop control structure The simulation results are confirmed by laboratory experiments

Adaptive Fuzzy Neural Network Control Design via a T–S Fuzzy Model for a Robot Manipulator Including Actuator Dynamics

Abstract: This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an n-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional-differential control, fuzzy-model-based control, T-S-type FNN control, and robust neural fuzzy network control systems.

Modeling and Control of Shape Memory Alloy Actuators

Abstract: This brief describes a new model for shape memory alloy (SMA) actuators based on the physics of the process and develops control strategies using the model. The model consists of three equations - the temperature dynamics described by Joules heating-convectional cooling, the mole fraction distribution with temperature given by statistics to describe a two state system, and a constitutive equation relating the changes in temperature and mole fraction to the stress and strain induced in the SMA. This model is used to develop two control schemes for controlling the strain in an SMA actuator. The first control scheme describes a gain-scheduled proportional-integral (PI) controller, the gains of which are obtained by means of linear quadratic regulator (LQR) optimization. The second control scheme is an Hinfin loop-shaping controller using normalized coprime stabilization which ensures robust stability by minimizing the effect of unmodeled dynamics at high frequencies. Simulation and experimental results show fast and accurate control of the strain in the SMA actuator for both control schemes.

Augmented Stable Fuzzy Control for Flexible Robotic Arm Using LMI Approach and Neuro-Fuzzy State Space Modeling

Abstract: Designing the control strategy for a flexible robotic arm has long been considered a complex problem as it requires stabilizing the vibration simultaneously with the primary objective of position control. A stable state-feedback fuzzy controller is proposed here for such a flexible arm. The controller is designed on the basis of a neuro-fuzzy state-space model that is successfully trained using the experimental data acquired from a real robotic arm. The complex problem of solving stability conditions is taken care of by recasting them in the form of linear matrix inequalities and then solving them using a popular interior-point-based method. This asymptotically stable fuzzy controller is further augmented to provide enhanced transient performance along with maintaining the excellent steady-state performance shown by the stable control strategy. The controller hence designed has been successfully implemented for a real robotic arm to operate over a long angular range of 180 with several payload conditions and, for situations where the system is operated for a long range and with a large variation in payload conditions, it could successfully outperform the recently proposed proportional derivative and strain controller.

Fuzzy Control System Performance Enhancement by Iterative Learning Control

Abstract: This paper suggests low-cost fuzzy control solutions that ensure the improvement of control system (CS) performance indices by merging the benefits of fuzzy control and iterative learning control (ILC). The solutions are expressed in terms of three fuzzy CS (FCS) structures that employ ILC algorithms and a unified design method focused on Takagi-Sugeno proportional-integral fuzzy controllers (PI-FCs). The PI-FCs are dedicated to a class of servo systems with linear/linearized controlled plants characterized by second-order dynamics and integral type. The invariant set theorem by Krasovskii and LaSalle with quadratic Lyapunov function candidates is applied to guarantee the convergence of the ILC algorithms and enable proper setting of the PI-FC parameters. The linear PI controller parameters tuned by the extended symmetrical optimum method are mapped onto the PI-FC ones by the modal equivalence principle. Real-time experimental results for a dc-based servo speed CS are included.

Direct Power Control of Active Filters With Averaged Switching Frequency Regulation

Abstract: This paper has developed a direct power control (DPC) structure to improve the performance of an active filter. A control algorithm directly uses the instantaneous power terms as control variables to replace the current and voltage variables that are commonly used in proportional-integral (PI) control systems. Compared to the other DPC schemes that have been reported so far, the proposed algorithm is oriented to harmonic current compensation, for which the switching functions are redefined, the bandwidths of the two hysteresis comparators are dynamically adjusted, and consequently, the pulsewidth modulation (PWM) switching frequencies are regulated to eliminate the unnecessary short switching pulses, and the control system can be used directly and effectively for various types of nonlinear load compensation. With the proposed control scheme, full control of the active filter, including the line current and the DC bus voltage, can be realized within an integrated power control loop. The advantages of the proposed control strategy have been verified by simulation and experimental results on a 2-kVA laboratory prototype.

Energy-Balance Modeling and Discrete Control for Single-Phase Grid-Connected PV Central Inverters

Abstract: This paper presents a two-control loop design considering the nonlinear time-varying characteristics of a single-phase grid-connected photovoltaic (PV) full-bridge central inverter. The control scheme design is based on the energy-balance modeling of the PV system and enables the design of a voltage loop linear discrete controller ensuring the stability of the system for the whole range of PV array operating conditions. A set of experimental results carried out on a laboratory prototype is provided to validate the proposed approach.

Kinematic control of free rigid bodies using dual quaternions

Abstract: This paper proposes a new type of control laws for free rigid bodies. The start point is the dual quaternion and its characteristics. The logarithm of a dual quaternion is defined, based on which kinematic control laws can be developed. Global exponential convergence is achieved using logarithmic feedback via a generalized proportional control law, and an appropriate Lyapunov function is constructed to prove the stability. Both the regulation and tracking problems are tackled. Omnidirectional control is discussed as a case study. As the control laws can handle the interconnection between the rotation and translation of a rigid body, they are shown to be more applicable than the conventional method.

Electromechanical Brake Modeling and Control: From PI to MPC

Abstract: The electromechanical brake (EMB) force control problem has been approached in prior work using cascaded proportional-integral (PI) control with embedded feedback loops to regulate clamp force, motor velocity, and motor current/torque. However, this is shown to provide limited performance for an EMB when faced with the challenges of actuator saturation, load-dependent friction, and nonlinear stiffness. There is a significant margin for improvement, and a modified control architecture is proposed using techniques of gain scheduling, friction compensation, and feedback linearization. A further improvement is then achieved by incorporating a model predictive control that better utilizes the available motor torque. Simulation and experimental results are presented to demonstrate the improvement in performance.

Robust Model-Following Control of Parallel UPS Single-Phase Inverters

Abstract: This paper presents a robust control technique applied to modular uninterruptible power-supply (UPS) inverters operating in parallel. When compared to conventional proportional-integral (PI) control, the proposed technique improves the response of the output voltage to load steps and to high distorted output currents, reducing the distortion of the output voltage. Furthermore, an excellent distribution of currents between modules is achieved, resulting in fine power equalization between the inverters on stream. The crossover frequency of the different loop gains involved is moderate, so that robustness to variations of the operation point and to modeling uncertainties is achieved. A comparative study with a two-loop conventional PI control scheme is presented. Experimental results on a 1-kVA modular online UPS system confirm the viability of the proposed scheme.

Electronic control for a hydraulically driven generator

Abstract: Electronic control for a hydraulic system driving an auxiliary power source is provided, with specific application as a system for controlling the operation of a hydraulically driven AC generator. The system may includes a hydraulic pump, a hydraulic motor drivably connected to the generator, a fluid circuit for circulating fluid from the pump to the motor and back. The fluid circuit may contain a bypass conduit to bypass the motor. The system also includes a proportional servo control valve assembly for controlling the fluid circuits and a control circuit for controlling the proportional control valve assembly. The control system can be capable of controlling the flow of hydraulic fluid to the motor powering the electrical or mechanical system. Sensors for measuring the operating parameters of the system and an operator interface module can influence the operation of the system.

A Robust Docking Strategy for a Mobile Robot Using Flow Field Divergence

Abstract: We present a robust strategy for docking a mobile robot in close proximity with an upright surface using optical flow field divergence and proportional feedback control. Unlike previous approaches, we achieve this without the need for explicit segmentation of features in the image, and using complete gradient-based optical flow estimation (i.e., no affine models) in the optical flow computation. A key contribution is the development of an algorithm to compute the flow field divergence, or time-to-contact, in a manner that is robust to small rotations of the robot during ego-motion. This is done by tracking the focus of expansion of the flow field and using this to compensate for ego rotation of the image. The control law used is a simple proportional feedback, using the unfiltered flow field divergence as an input, for a dynamic vehicle model. Closed-loop stability analysis of docking under the proposed feedback is provided. Performance of the flow field divergence algorithm is demonstrated using offboard natural image sequences, and the performance of the closed-loop system is experimentally demonstrated by control of a mobile robot approaching a wall.

Microprocessor-Based Fuzzy Decentralized Control of 2-D Piezo-Driven Systems

Abstract: In this paper, the trajectory tracking of a 2-D piezo-driven system (2DPDS) using microprocessor-based fuzzy decentralized control (MBFDC) is developed. It is known that the piezoelectric actuator contains hysteresis, which is not one-to-one mapping and memoryless nonlinearity. Due to this nonlinearity and the coupling characteristic of the 2DPDS, an effective decentralized control is difficult to design. From the very beginning, the suitable coefficients of switching surface are assigned to stabilize the dynamics of switching surface and to shape the response of tracking error. Based on the data of input/output, two scaling factors are employed to normalize the switching surface and its derivative. According to the concept of if-then rule, an appropriate rule table for the ith subsystem is then achieved. This table is skew symmetric about the diagonal line; the absolute value of this table is proportional to the distance to the diagonal line. According to the system stability, the output-scaling factor is determined. Finally, a sequence of experiments including the trajectory tracking using MBFDC, proportional-integral-differential control, and classic fuzzy control is carried out to confirm the usefulness of the proposed control system.

Control Reconfiguration of Discrete Event Systems With Dynamic Control Specifications

Abstract: This paper defines a reconfiguration method for the class of discrete-event systems (DES) that is subject to linear constraints as their control specifications. Some existing methods for enforcing these constraints make use of Petri-net P-invariants for controller synthesis. These methods are quite appealing because their computational complexity is much more tractable than most other methods for controller synthesis. However, a common limitation of all existing P-invariant-based control architectures for DES plants is the assumption that the linear constraints defining the control specification of the plant do not change over time. Here, we relax this assumption and allow the control specifications to change during controller runtime. Under certain assumptions on DES behavior, we automatically reconfigure the DES controller after the control specification is changed. In addition, if the current state of the controlled DES has become infeasible under the new control specification, we automatically generate a so-called plant reconfiguration procedure whose execution leads the system back to a feasible state. This reconfiguration procedure is optimal in that it seeks to minimize the cost of reconfiguration actions through an Integer Programming (IP) model. The objective function of the IP model can be used to generate reconfiguration solutions that meet some desired properties. Depending on the cost of each reconfiguration action, a minimum cost reconfiguration solution may use only actions contained in the current plant configuration (an internal response), or ask for a change in the plant configuration, for instance, by adding new resources (an external response), or a combination of both strategies. Finally, we illustrate our method by applying it to a hospital control system example. Note to Practitioners-This paper proposes a dynamic reconfiguration framework that can revise the operations of systems whose control requirements change over time. The proposed framework can be applied to systems that satisfy the following two assumptions. First, the behavior of the system under study is described in terms of a set of discrete states and events. Events will cause the system to transition between states. Second, the control requirements must be expressed by linear equalities and inequalities on the system states. Under these circumstances, the proposed framework can identify an optimal transition to a new control policy that satisfies the new control requirements. Moreover, the system under consideration will continue operating while this transition is taking place. One application of this method is in modifying hospital control strategies when a hospital experiences unexpected events. In this case, the hospital operations-such as patient handling, resource assignment, and procedure scheduling-can be represented by discrete state models (e.g., Petri nets). Constraints on these operations can be modeled by linear inequalities on hospital and patient state. Upon a change in the constraints, the proposed reconfiguration method revises the hospital control strategies. For example, a shift in the hospital service demands (e.g., an increase in the flow of patients to the hospital due to a mass casualty situation) can be translated to changes in the constraints. In this case, the hospital operations must be revised to accommodate the new constraints without disrupting the operation of the hospital. The reconfiguration method of this paper provides a framework for modeling the reconfiguration steps and for calculating the least cost reconfiguration solution.

Controller Design for Large-Gap Control of Electromagnetically Levitated System by Using an Optimization Technique

Abstract: In this paper, design and implementation of an optimized controller for a single magnet-based electromagnetic levitation system (EMLS) where an electromagnet of 2.6-kg mass is levitated over a large gap under a fixed ferromagnetic guide-way has been discussed. EMLS is inherently unstable and strongly nonlinear in nature. A single position controller (lead type) along with an outer proportional-integral (PI) controller has been designed that will stabilize the EMLS represented mathematically by three different plants corresponding to three different operating points. An optimization technique utilizing the random search method with interval reduction, proposed by Luus and Jaakola, has been used for designing the controller. A "model matching control" design procedure has been utilized for the design of outer PI controller. The inner controller stabilizes the three unstable plants, whereas the outer PI control action is used to modify the three position outputs as per the desired model response. The combined control action provides good stability and satisfactory performance (like fast response, less overshoot, and zero steady-state error) for the different operating zones of EMLS. Stability has been confirmed by Kharitonov-Nyquist enclosure diagrams. The simulated controllers have been successfully implemented, thus validating the modeling and controller design procedure.

Predictive Current Control Using Identification of Current Ripple

Abstract: This paper presents a new time-discrete predictive current control for permanent-magnet synchronous motor (PMSM) which follows a reference step in two sampling periods. The control stands out by using a permanent identification which allows fast control even if the motor parameters are changing. The identification analyzes the current response of each switch state of the voltage source inverter. This kind of identification works without any test signals. The proposed new predictive current control will be described in detail. The simulation results show the feasibility and effectiveness of the proposed controller, compared to proportional integral control and dead-beat control in the rotor synchronous frame. Furthermore, the new control strategy will be verified in experiments. The control hardware only needs a field-programmable gate array and analog-to-digital converters. To verify the control, a prototype 14 kW PMSM servo-drive system is used.

Experimental study on a hybrid-driven servo press using iterative learning control

Abstract: Servo presses provide flexible punch motions, which satisfy different production needs. To achieve this merit, the control system must maintain the punch motion accurately despite versatile desired trajectories or varied loadings. Against this backdrop, the current paper proposes an iterative learning control (ILC) scheme for a hybrid-driven servo press. A proportional derivative (PD) type ILC controller that contains a closed-loop feedback controller is adopted. The sensitivity Jacobian is introduced into the controlling algorithm as the proportional gain in order to smoothen and increase the error convergence rate. The proposed ILC controller is then developed and verified on a servo press prototype. Experimental validations of a cup-shaped drawing are also carried out. The results show that the proposed ILC scheme effectively made the punch position root-mean-square (RMS) errors converge to less than 0.2 mm within five iterations. The precision was also improved to less than 50 μm that was equivalent to 35–40% of the original level without the ILC.

Voltage/Frequency Proportional Control of Stick-Slip Micropositioning Systems

Abstract: A new control type for stick-slip micropositioning systems is proposed in this brief: the voltage/frequency (U/f) proportional control. It gives more precise results relatively to the classical control algorithm. It is also an assembling of two classical controllers: the sign and the classical proportional controllers. A high stroke model of a stick-slip micropositioning system is first given. Then, we will theoretically analyze the performances of the closed-loop process with the U/f controller. Finally, we will give some experimental results obtained with different values of the proportional gains.

Impulse-Based Control of Joints and Muscles

Abstract: We propose a novel approach to proportional derivative (PD) control exploiting the fact that these equations can be solved analytically for a single degree of freedom. The analytic solution indicates what the PD controller would accomplish in isolation without interference from neighboring joints, gravity and external forces, outboard limbs, etc. Our approach to time integration includes an inverse dynamics formulation that automatically incorporates global feedback so that the per joint predictions are achieved. This effectively decouples stiffness from control so that we obtain the desired target regardless of the stiffness of the joint, which merely determines when we get there. We start with simple examples to illustrate our method and then move on to more complex examples including PD control of line segment muscle actuators.

A Guideline for Low-Force Robotic Guidance for Enhancing Human Performance of Positioning and Trajectory Tracking: It Should Be Stiff and Appropriately Slow

Abstract: This paper considers the application of a low-force robotic manipulator to guide a human user's movements to place a tool (or the user's hand) at a predetermined position or move it along a predetermined trajectory. This application is potentially useful, e.g., skill training for humans, rehabilitation, and human-machine coordination in the manufacturing industry. A proportional-derivative (PD)-type position control can be used for this application, but the parameters for the controller should be appropriately chosen for enhancing the human performance of positioning and trajectory tracking. We hypothesize that the robot's position control should be stiff and appropriately slow, i.e., the proportional gain should be high and the time constant (the ratio of the derivative gain to the proportional gain) should be appropriately large. Such characteristic has been difficult to be realized in ordinary PD position control because it requires direct high-gain velocity feedback. However, our recent technique, which is proxy-based sliding mode control (PSMC), is capable of producing such a hypothetically preferred response and allows us to empirically validate the hypothesis. The results of experiments using two distinctly different robotic devices supported the hypothesis, showing that the time constant should be set around 0.1 s rather than 0.01 and 0.5 s.

A Comparative Study of PI, Fuzzy, and ANN Controllers for Chopper-fed DC Drive with Embedded Systems Approach

Abstract: Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulatio...

Multiple Input Governor Control for a Diesel Generating Set

Abstract: The paper presents a multiple-input single-output fuzzy logic governor algorithm that can be used to improve the transient response of a diesel generating set, when supplying an islanded load. The proposed governor uses the traditional speed input in addition to voltage and power factor to modify the fueling requirements during various load disturbances. The use of fuzzy logic control allows the use of proportional-integral-derivative (PID) type structures that can provide variable gain strategies to account for nonlinearities in the system. Fuzzy logic also provides a means of processing other input information by linguistic reasoning and a logical control output to aid the governor action during transient disturbance. The test results were obtained using a 50 kVA naturally aspirated diesel generator testing facility. Both real and reactive load tests were conducted. The complex load test results demonstrate that, by using additional inputs to the governor algorithm, enhanced generator transient speed recovery response can be obtained.

Robust tracking control for a wheeled mobile manipulator with dual arms using hybrid sliding-mode neural network

Abstract: In this paper, a robust tracking controller is proposed for the trajectory tracking problem of a dual-arm wheeled mobile manipulator subject to some modeling uncertainties and external disturbances. Based on backstepping techniques, the design procedure is divided into two levels. In the kinematic level, the auxiliary velocity commands for each subsystem are first presented. A sliding-mode equivalent controller, composed of neural network control, robust scheme and proportional control, is constructed in the dynamic level to deal with the dynamic effect. To deal with inadequate modeling and parameter uncertainties, the neural network controller is used to mimic the sliding-mode equivalent control law; the robust controller is designed to compensate for the approximation error and to incorporate the system dynamics into the sliding manifold. The proportional controller is added to improve the system's transient performance, which may be degraded by the neural network's random initialization. All the parameter adjustment rules for the proposed controller are derived from the Lyapunov stability theory and e-modification such that uniform ultimate boundedness (UUB) can be assured. A comparative simulation study with different controllers is included to illustrate the effectiveness of the proposed method.

Research on the power electronic load based on repetitive controller

Abstract: The paper present a new test equipment for single-phase ac power source tests named PEL(power electronic load), the proposed architecture consists of two power stages such as input AC-DC imitation converter to imitate various loads and the output grid-connection converter to send back the recycling energy to the utility grid. In PEL, the controllable imitation converter enlarges the load bound that can be imitated, make the system more flexible, it even can simulate the nonlinear load to verify the stability of the tested ac power; and the grid-connection transformer ensures the system operate in security. A simple control scheme based on P(proportion) and PI(proportion and integral) control is proposed to ensure the system operate normally. But the phase delays in the digital control system and the controlled plant, the change of the parameter in the PEL, the unpredictable disturbances in the system will degrade the effectiveness of the simple control scheme. The repetitive control is proposed to solve the problem, it can ensure the system with excellent steady characteristics, to remedy the shortage of the repetitive controller in dynamic characteristics, the traditional P controller is connected with it in parallel connection, the compound controller acts well both in steady state and dynamic state, it even can hold a perfect effect in simulating the nonlinear load. The paper analyzes the repetitive controller in details. At last, the simulation and experimental results are provided to testify the effectiveness of the proposed control scheme.