Showing papers in "IEEE Transactions on Control Systems and Technology in 2012"

MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle

Abstract: A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by utilizing a planetary gear set to split and combine the power produced by electric machines and a combustion engine. Because of the different modes of operation, devising a near optimal energy management strategy is quite challenging and essential for these vehicles. To improve the fuel economy of a power-split HEV, we first formulate the energy management problem as a nonlinear and constrained optimal control problem. Then two different cost functions are defined and model predictive control (MPC) strategies are utilized to obtain the power split between the combustion engine and electrical machines and the system operating points at each sample time. Simulation results on a closed-loop high-fidelity model of a power-split HEV over multiple standard drive cycles and with different controllers are presented. The results of a nonlinear MPC strategy show a noticeable improvement in fuel economy with respect to those of an available controller in the commercial Powertrain System Analysis Toolkit (PSAT) software and the other proposed methodology by the authors based on a linear time-varying MPC.

Model Predictive Control for the Operation of Building Cooling Systems

Abstract: This brief presents a model-based predictive control (MPC) approach to building cooling systems with thermal energy storage. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. First, simplified models of chillers, cooling towers, thermal storage tanks, and buildings are developed and validated for the purpose of model-based control design. Then an MPC for the chilling system operation is proposed to optimally store the thermal energy in the tank by using predictive knowledge of building loads and weather conditions. This brief addresses real-time implementation and feasibility issues of the MPC scheme by using a simplified hybrid model of the system, a periodic robust invariant set as terminal constraints, and a moving window blocking strategy. The controller is experimentally validated at the University of California, Merced. The experiments show a reduction in the central plant electricity cost and an improvement of its efficiency.

Command Filtered Adaptive Backstepping

Abstract: Implementation of adaptive backstepping controllers requires analytic calculation of the partial derivatives of certain stabilizing functions. It is well documented that, as the order of a nonlinear system increases, analytic calculation of these derivatives becomes prohibitive. Therefore, in practice, either alternative control approaches are used or the derivatives are neglected in the implementation. Neglecting the derivatives results in the loss of all guarantees proven by Lyapunov methods for the adaptive backstepping approach and may result in instability. This paper presents a new implementation approach for adaptive backstepping control. The main objectives are to facilitate the derivation and implementation of the adaptive backstepping approach, with performance guarantees proven by Lyapunov methods, for applications that were prohibitively difficult using the standard analytic implementation approach. The new approach uses filtering methods to produce certain command signals and their derivatives which eliminates the requirement of analytic differentiation. The approach also introduces filters to generate certain compensating signals necessary to compute compensated tracking errors suitable for adaptive parameter estimation. We present a set of Lemmas and Theorems to analyze the performance both during the initialization and the operating phases. We show that the initialization phase is of finite duration that can be controlled by selection of a design parameter. We also show that all signals within the system are bounded during this short initialization phase. During the operating phase, we show that the command filtered implementation approach has theoretical properties identical to those of the conventional approach. The general approach is presented and analyzed for systems in generalized parameter strict feedback form. Extensions of the approach are presented to demonstrate the application of the method to a land vehicle trajectory following application. Application and effectiveness of the proposed method is shown by simulation results.

Adaptive Sliding Mode Fault Tolerant Attitude Tracking Control for Flexible Spacecraft Under Actuator Saturation

Abstract: A novel fault tolerant attitude tracking control scheme is developed for flexible spacecraft with partial loss of actuator effectiveness fault. Neural networks are first introduced to account for system uncertainties, and an adaptive sliding mode controller is derived by using on-line updating law to estimate the bound of actuator fault such that any information of the fault is not required. To further address actuator saturation problem, a modified fault tolerant control law is then presented to ensure that the resulting control signal will never incur saturation. It is shown that the roll, pitch and yaw angle trajectories can globally asymptotically track the desired attitude in the face of faulty actuator, system uncertainties, external disturbances and even actuator saturation. A simulation example of a flexible spacecraft is given to illustrate the effectiveness of the proposed controller.

Control of Multiple UAVs for Persistent Surveillance: Algorithm and Flight Test Results

Abstract: Interest in control of multiple autonomous vehicles continues to grow for applications such as weather monitoring, geographical mapping fauna surveys, and extra-terrestrial exploration. The task of persistent surveillance is of particular significance in that the target area needs to be continuously surveyed, minimizing the time between visitations to the same region. This distinction from one-time coverage does not allow a straightforward application of most exploration techniques to the problem, though ideas from these methods can still be used. The aerial vehicle dynamic and endurance constraints add additional complexity to the autonomous control problem, whereas stochastic environments and vehicle failures introduce uncertainty. In this work, we investigate techniques for high-level control, that are scalable, reliable, efficient, and robust to problem dynamics. Next, we suggest a modification to the control policy to account for aircraft dynamic constraints. We also devise a health monitoring policy and a control policy modification to improve performance under endurance constraints. The Vehicle Swarm Technology Laboratory-a hardware testbed developed at Boeing Research and Technology, Seattle, WA, for evaluating a swarm of unmanned air vehicles-is then described, and these control policies are tested in a realistic scenario.

Impact Time and Angle Guidance With Sliding Mode Control

Abstract: A novel sliding mode-based impact time and angle guidance law for engaging a modern warfare ship is presented in this paper. In order to satisfy the impact time and angle constraints, a line-of-sight rate shaping process is introduced. This shaping process results in a tuning parameter that can be used to create a line-of-sight rate profile to satisfy the final time and heading angle requirements and to yield acceptable normal acceleration values. In order to track the desired line-of-sight rate profile in the presence of uncertainties, a novel robust second-order sliding mode control law is developed using a backstepping concept. Due to the robustness of the control law, it can be applied to many realistic engagement scenarios which include uncertainties such as target motion. Numerical simulations with different warship engagements are presented to illustrate the potential of the developed method.

Wide-Area Control of Power Systems Through Delayed Network Communication

Abstract: Like general network communication, there are network-induced delays, data packet dropout and disordering in the communication of wide-area measurement systems. What impact do these factors have on the control of wide-area closed-loop power systems? This study aims at developing methods in order to take these factors into account in control of wide-area power systems. First, a networked control system model is constructed for wide-area closed-loop power systems; in this model, network-induced delays, data packet dropout, and disordering are captured by time-varying delays in wide-area measurement systems. Then, linear matrix inequality based methods are applied to design a controller for better power system performance using wide-area information as feedback signals. The controller can tolerate network-induced delays, data packet dropout, and disordering in the communication of wide-area measurement systems. Finally, we give some simulation results showing the effectiveness of our approach.

A Motion Planning-Based Adaptive Control Method for an Underactuated Crane System

Abstract: This brief proposes a motion planning-based adaptive control strategy for an underactuated overhead crane system. To improve the transportation efficiency and enhance the safety of the crane system, the trolley is required to reach the desired position fast enough, while the swing of the payload needs to be within an acceptable domain. To achieve these objectives, a novel two-step design strategy consisting of a motion planning stage and an adaptive tracking control design stage, is proposed to control such an underactuated system as an overhead crane. Specifically, a novel desired trajectory, which satisfies physical constraints of an overhead crane, is proposed for the trolley by fusing theoretical analysis results with the conventional empirical trajectory planning methods. An adaptive control law is then constructed in the second step to make the trolley track the planned trajectory, where some online update mechanism is introduced to ensure that the controller works well with different working conditions. As shown by Lyapunov techniques, the proposed adaptive controller guarantees asymptotic tracking result even in the presence of uncertainties including system parameters and various disturbance. Some experiment results demonstrate that the proposed control method achieves superior performance for the underactuated cranes.

Design and Implementation of Secure Networked Predictive Control Systems Under Deception Attacks

Abstract: This brief addresses the security issues of data transmitted in networked control systems (NCSs), especially confidentiality, integrity and authenticity. A secure networked predictive control system (SNPCS) architecture is presented, which integrates the Data Encryption Standard (DES) algorithm, Message Digest (MD5) algorithm, timestamp strategy, and recursive networked predictive control (RNPC) method. The former three parts are used to form a secure transmission mechanism between the controller side and the plant side, which is responsible for enforcing the data confidentiality and checking the data integrity and authenticity. To guarantee the control system performance when suffering from deception attacks, the RNPC method based on round-trip time delays is proposed to compensate for the adverse effects introduced by the deception attacks as well as the network communication constraints, such as time-varying network delay, packet disorder and packet dropout. A theoretical result using the switched system theory is obtained for the closed-loop stability of the RNPC system. Practical experiments are performed to demonstrate the effectiveness of the proposed SNPCS.

Route Preview in Energy Management of Plug-in Hybrid Vehicles

Abstract: This brief evaluates the use of terrain, vehicle speed, and trip distance preview to increase the fuel economy of plug-in hybrid vehicles. Access to future information is classified into full, partial, or no future information and for each case an energy management strategy with the potential for a real-time implementation is proposed. With full knowledge of future driving conditions, dynamic programming (DP) provides a best-achievable benchmark. A partial preview level has access to future trip terrain and requires velocity estimation. Equivalent consumption minimization strategy (ECMS) is deployed as an instantaneous real-time minimization strategy with parameters adjusted by estimated future driving conditions and obtained either from DP or from a backward solution of ECMS. To reduce the requirement for future velocity and detailed terrain information, another partial preview level only assumes known trip distance to the next charging station and elevation changes (if available). In this level, the parameter of the real-time ECMS is estimated based on the remaining trip distance, the battery's state-of-charge, and elevation changes if included. The results are evaluated against cases with no preview. Results from a number of simulation case studies indicate that the fuel economy can be substantially enhanced with only partial preview.

An Energy Management Controller to Optimally Trade Off Fuel Economy and Drivability for Hybrid Vehicles

Abstract: Hybrid vehicle fuel economy performance is highly sensitive to the energy management strategy used to regulate power flow among the various energy sources and sinks. Optimal non-causal solutions are easy to determine if the drive cycle is known a priori. It is very challenging to design causal controllers that yield good fuel economy for a range of possible driver behavior. Additional challenges come in the form of constraints on powertrain activity, such as shifting and starting the engine, which are commonly called “drivability” metrics and can adversely affect fuel economy. In this paper, drivability restrictions are included in a shortest path stochastic dynamic programming (SP-SDP) formulation of the real-time energy management problem for a prototype vehicle, where the drive cycle is modeled as a stationary, finite-state Markov chain. When the SP-SDP controllers are evaluated with a high-fidelity vehicle simulator over standard government drive cycles, and compared to a baseline industrial controller, they are shown to improve fuel economy more than 11% for equivalent levels of drivability. In addition, the explicit tradeoff between fuel economy and drivability is quantified for the SP-SDP controllers.

A Stochastic Approach to “Dynamic-Demand” Refrigerator Control

Abstract: Dynamic demand management is a very promising research direction for improving power system resilience. This paper considers the problem of managing power consumption by means of “smart” thermostatic control of domestic refrigerators. In this approach, the operating temperature of these appliances and thus their energy consumption, is modified dynamically, within a safe range, in response to mains frequency fluctuations. Previous research has highlighted the potential of this idea for responding to sudden power plant outages. However, deterministic control schemes have proved inadequate as individual appliances tend to “synchronize” with each other, leading to unacceptable levels of overshoot in energy demand, when they “recover” their steady-state operating cycles. In this paper we design decentralized random controllers that are able to respond to sudden plant outages and which avoid the instability phenomena associated with other feedback strategies. Stochasticity is used to achieve desynchronization of individual refrigerators while keeping overall power consumption tightly regulated.

Optimal Control of a Fractional-Order HIV-Immune System With Memory

Abstract: The fact that fractional-order models possess memory leads to modeling a fractional-order HIV-immune system. We discuss the necessary conditions for the optimality of a general fractional optimal control problem whose fractional derivative is described in the Caputo sense. Using an objective function that minimizes the infectious viral load and count of infected T cells, the optimal control problem is solved for the fractional-order optimality system with minimal dosage of anti-HIV drugs and the effects of mathematically optimal therapy are demonstrated. Simulation results show that the fractional-order optimal control scheme can achieve improved quality of the treatment.

Robust Adaptive Boundary Control of a Vibrating String Under Unknown Time-Varying Disturbance

Abstract: In this paper, robust adaptive boundary control is developed for a class of flexible string-type systems under unknown time-varying disturbance. The dynamics of the string system is represented by a nonhomogeneous hyperbolic partial differential equation (PDE) and two ordinary differential equations. Boundary control is proposed at the right boundary of the string based on the original distributed parameter system model (PDE) to suppress the vibration excited by the external unknown disturbance. Adaptive control is designed to compensate the system parametric uncertainty. With the proposed robust adaptive boundary control, all the signals in the closed-loop system are guaranteed to be uniformly ultimately bounded. The state of the string system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Simulations are provided to illustrate the effectiveness of the proposed control.

Optimal Equivalent Fuel Consumption for Hybrid Electric Vehicles

Abstract: In this brief, we show that using Pontryagin's minimum principle (PMP) can be a good alternative which achieves near-optimal solutions when the future driving schedule can be known in real-time. The control concept can be solved forward in time when a constant costate value is decided. The constant costate can be interpreted as an equivalent ratio linking fuel usage and electric energy consumption. If the fuel economy is the only objective function to minimize, the control concept based on PMP is near global optimal when the final is close to a desired value. In this brief, a method to calculate the proper costate to sustain the within a reasonable range is suggested. In this method, two parameters, and , which depend on the driving patterns, are used to link the driving cycle to the optimal costate. Simulation results show that we can achieve near optimal control, in which 86% of final values stay within 4% of the desired final value for tested city driving schedules.

Decentralized Adaptive Robust Control of Robot Manipulators Using Disturbance Observers

Abstract: In this paper, we propose a decentralized adaptive robust controller for trajectory tracking of robot manipulators. In each local controller, a disturbance observer (DOB) is introduced to compensate for the low-passed coupled uncertainties, and an adaptive sliding mode control term is employed to handle the fast-changing components of the uncertainties beyond the pass-band of the DOB. In contrast to most of the local controllers using DOB for robot manipulators that are based on linear control theory, in this study, by some special nonlinear damping terms, the boundedness of the signals of the overall nonlinear system is first ensured. This paves the way to analyze how the DOB and adaptive sliding mode control play in a cooperative way in each local subsystem to achieve an excellent control performance. Simulation results are provided to support the theoretical results.

Disturbance Compensating Model Predictive Control With Application to Ship Heading Control

Abstract: To address the constraint violation and feasibility issues of model predictive control (MPC) for ship heading control in wave fields, a novel disturbance compensating MPC (DC-MPC) algorithm has been proposed to satisfy the state constraints in the presence of environmental disturbances. The capability of the novel DC-MPC algorithm is first analyzed. Then, the proposed DC-MPC algorithm is applied to solve the ship heading control problem, and its performance is compared with a modified MPC controller, which considers the estimated disturbance in the optimization directly. The simulation results show good performance of the proposed controller in terms of reducing heading error and satisfying yaw velocity and actuator saturation constraints. The DC-MPC algorithm has the potential to be applied to other motion control problems with environmental disturbances, such as flight, automobile, and robotics control.

Model Predictive Idle Speed Control: Design, Analysis, and Experimental Evaluation

Abstract: Idle speed control is a landmark application of feedback control in automotive vehicles that continues to be of significant interest to automotive industry practitioners, since improved idle performance and robustness translate into better fuel economy, emissions and drivability. In this paper, we develop a model predictive control (MPC) strategy for regulating the engine speed to the idle speed set-point by actuating the electronic throttle and the spark timing. The MPC controller coordinates the two actuators according to a specified cost function, while explicitly taking into account constraints on the control and requirements on the acceptable engine speed range, e.g., to avoid engine stalls. Following a process proposed here for the implementation of MPC in automotive applications, an MPC controller is obtained with excellent performance and robustness as demonstrated in actual vehicle tests. In particular, the MPC controller performs better than an existing baseline controller in the vehicle, is robust to changes in operating conditions, and to different types of disturbances. It is also shown that the MPC computational complexity is well within the capability of production electronic control unit and that the improved performance achieved by the MPC controller can translate into fuel economy improvements.

Fast and Global Optimal Energy-Efficient Control Allocation With Applications to Over-Actuated Electric Ground Vehicles

Abstract: This paper presents a fast and global optimization algorithm for an energy-efficient control allocation (CA) scheme, which was proposed for improving the operational energy efficiency of over-actuated systems. For a class of realistic actuator power and efficiency functions, a Karush-Kuhn-Tucker (KKT)-based algorithm was devised to find all the local optimal solutions, and consequently the global minimum through a further simple comparison among all the realistic local minima and boundary values for such a non-convex optimization problem. This KKT-based algorithm is also independent on the selections of initial conditions by transferring the standard nonlinear optimization problem into classical eigenvalue problems. Numerical examples for electric vehicles with in-wheel motors were utilized to validate the effectiveness of the proposed global optimization algorithm. Simulation results, based on the parameters of an electric ground vehicle actuated by in-wheel motors (whose energy efficiencies were experimentally calibrated), showed that the proposed global optimization algorithm was at least 20 times faster than the classical active-set optimization method, while achieving better control allocation results for system energy saving.

On the State-Space Realization of LPV Input-Output Models: Practical Approaches

Abstract: A common problem in the context of linear parameter-varying (LPV) systems is how input-output (IO) models can be efficiently realized in terms of state-space (SS) representations. The problem originates from the fact that in the LPV literature discrete-time identification and modeling of LPV systems is often accomplished via IO model structures. However, to utilize these LPV-IO models for control synthesis, commonly it is required to transform them into an equivalent SS form. In general, such a transformation is complicated due to the phenomenon of dynamic dependence (dependence of the resulting representation on time-shifted versions of the scheduling signal). This conversion problem is revisited and practically applicable approaches are suggested which result in discrete-time SS representations that have only static dependence (dependence on the instantaneous value of the scheduling signal). To circumvent complexity, a criterion is also established to decide when an linear-time invariant (LTI)-type of realization approach can be used without introducing significant approximation error. To reduce the order of the resulting SS realization, an LPV Ho-Kalman-type of model reduction approach is introduced, which, besides its simplicity, is capable of reducing even non-stable plants. The proposed approaches are illustrated by application oriented examples.

Hybrid Fault-Tolerant Flight Control System Design Against Partial Actuator Failures

Abstract: A model to represent loss of control effectiveness in an aircraft is developed by analyzing physical faults in the hydraulically-driven control surfaces. A hybrid fault-tolerant control system (FTCS) that combines the merits of passive and active FTCSs is proposed to accommodate this kind of partial actuator failures. The hybrid FTCS is able to first slow down the rate of fault induced system deterioration with minimal fault information so that the fault detection and diagnosis (FDD) schemes can have additional time to achieve more accurate fault diagnosis. Once the correct fault information is obtained, the hybrid FTCS can counteract the faults effectively through an optimal reconfigurable controller. Depending on the availability of actuator redundancies, the passive FTCS and the reconfigurable controller are designed in the framework of linear matrix inequality (LMI) approach. Case studies of an aircraft subject to different degree of loss of control effectiveness have been carried out to prove the effectiveness of this new approach to FTCS.

Neural-Adaptive Output Feedback Control of a Class of Transportation Vehicles Based on Wheeled Inverted Pendulum Models

Abstract: The wheeled inverted pendulum (WIP) models have been widely applied in the transportation vehicles formed by a mobile wheeled inverted pendulum system with an operator (demonstrated in Fig. 1 ). In this paper, we focus on the study of nonlinear control design for the WIP model-based vehicles, for which accurate dynamics could not be obtained beforehand due to the presence of uncertainties caused by the human operator as well as the vehicle. We develop an output feedback adaptive neural network (NN) control incorporating a linear dynamic compensator to achieve stable dynamic balance and tracking of the desired given trajectories. Comparison simulation studies demonstrate guaranteed tracking performance and stable dynamics balance in the presence of uncertainties and thus verify the efficiency of the developed nonlinear controller.

FX-RLS-Based Feedforward Control for LIDAR-Enabled Wind Turbine Load Mitigation

Abstract: An adaptive feedforward controller based on a filtered-x recursive least square (FX-RLS) algorithm and a non-adaptive feedforward controller based on a zero-phase-error tracking control (ZPETC) technique have been designed to augment a collective pitch proportional-integral (PI) feedback controller for wind turbine rotor speed regulation and component load reduction when the wind turbine is operating above rated wind speed The inputs to the adaptive feedforward controller include measurements of the rotor speed error and the incoming wind speed, where wind speed would be provided by a commercial light detection and ranging (LIDAR) system Simulation results are based on comparison with a PI feedback only controller Simulations show that augmenting the baseline PI feedback control with ZPETC feedforward control improves the blade loads but worsens the tower loads The FX-RLS feedforward algorithm gives better performance than both the baseline PI feedback and the ZPETC feedforward in both tower (fore-aft and side-to-side) and blade (flapwise and edgewise) bending moment mitigation Even with realistic 1 Hz LIDAR data update rate, the FX-RLS feedforward strategy can effectively mitigate the tower and blade bending moment while providing better rotor speed tracking and only a small energy drop

Image-Based Pointing and Tracking for Inertially Stabilized Airborne Camera Platform

Abstract: This paper describes a novel image-based pointing-tracking feedback control scheme for an inertially stabilized double-gimbal airborne camera platform combined with a computer vision system. The key idea is to enhance the intuitive decoupled controller structure with measurements of the camera inertial angular rate around its optical axis. The resulting controller can also compensate for the apparent translation between the camera and the observed object, but then the velocity of this mutual translation must be measured or estimated. Even though the proposed controller is more robust against longer sampling periods of the computer-vision system then the decoupled controller, a sketch of a simple compensation of this delay is also given. Numerical simulations are accompanied by laboratory experiments with a real benchmark system.

A Minimum Parameter Adaptive Approach for Rejecting Multiple Narrow-Band Disturbances With Application to Hard Disk Drives

Abstract: Many servo systems are subjected to narrow-band disturbances that generate vibrations at multiple frequencies. One example is the track-following control in a hard disk drive (HDD) system, where the airflow-excited disk and actuator vibrations introduce strong and uncertain spectral peaks to the position error signal. Such narrow-band vibrations differ in each product and can appear at frequencies above the bandwidth of the control system. We present a feedback control scheme that adaptively enhances the servo performance at multiple unknown frequencies, while maintaining the baseline servo loop shape. A minimum parameter model of the disturbance is first introduced, followed by the construction of a novel adaptive multiple narrow-band disturbance observer for selective disturbance cancellation. Evaluation of the proposed algorithm is performed on a simulated HDD benchmark problem.

Lyapunov-Based Control Scheme for Single-Phase Grid-Connected PV Central Inverters

Abstract: A Lyapunov-based control scheme for single-phase single-stage grid-connected photovoltaic central inverters is presented. Besides rendering the closed-loop system globally stable, the designed controller is able to deal with the system uncertainty that depends on the solar irradiance. A laboratory prototype has been built as a proof of concept for the proposed control technique. A nonlinear passive adaptive controller has been programmed in a field-programmable gate array.

Model Predictive Discrete-Time Sliding Mode Control of a Nanopositioning Piezostage Without Modeling Hysteresis

Abstract: This paper proposes an enhanced model predictive discrete-time sliding mode control (MPDSMC) with proportional-integral (PI) sliding function and state observer for the motion tracking control of a nanopositioning system driven by piezoelectric actuators One distinct advantage of the proposed controller lies in that its implementation only requires a simple second-order model of the system, whereas it does not need to know neither the hysteresis model nor the bounds on system uncertainties The unmodeled hysteresis is eliminated by the one-step delayed disturbance estimation technique and the neglected residual modes are suppressed by employing a properly-designed state observer Moreover, the reasons why the model predictive control methodology and PI action can eliminate the chattering effects and produce a low level of tracking error are discovered in state-space framework Experimental results demonstrate that the performance of the proposed MPDSMC controller is superior to both conventional PID and DSMC methods in motion tracking tasks A precise tracking is achieved by the nanopositioning stage along with the hysteretic nonlinearity mitigated to a negligible level, which validates the feasibility of the proposed controller in the domain of micro-/nanomanipulation

Robust Active Chatter Control in the High-Speed Milling Process

Abstract: Chatter is an instability phenomenon in machining processes which limits productivity and results in inferior workpiece quality, noise and rapid tool wear The increasing demand for productivity in the manufacturing community motivates the development of an active control strategy to shape the chatter stability boundary of manufacturing processes In this work a control methodology for the high-speed milling process is developed that alters the chatter stability boundary such that the area of chatter-free operating points is increased and a higher productivity can be attained The methodology developed in this paper is based on a robust control approach using -synthesis Hereto, the most important process parameters (depth of cut and spindle speed) are treated as uncertainties to guarantee the robust stability (ie, no chatter) in an a priori specified range of these process parameters Effectiveness of the proposed methodology is demonstrated by means of illustrative examples

Closed-Loop Neural Network-Based NMES Control for Human Limb Tracking

Abstract: Closed-loop control of skeletal muscle is complicated by the nonlinear muscle force to length and velocity relationships and the inherent unstructured and time-varying uncertainties in available models. Some pure feedback methods have been developed with some success, but the most promising and popular control methods for neuromuscular electrical stimulation (NMES) are neural network (NN)-based methods. Efforts in this paper focus on the use of a NN feedforward controller that is augmented with a continuous robust feedback term to yield an asymptotic result (in lieu of typical uniformly ultimately bounded stability). Specifically, an NN-based controller and Lyapunov-based stability analysis are provided to enable semi-global asymptotic tracking of a desired limb time-varying trajectory (i.e., non-isometric contractions). The developed controller is applied as an amplitude modulated voltage to external electrodes attached to the distal-medial and proximal-lateral portion of the quadriceps femoris muscle group in non-impaired volunteers. The added value of incorporating a NN feedforward term is illustrated through experiments that compare the developed controller with and without the NN feedforward component.

Complementary PID Controller to Passivity-Based Nonlinear Control of Boost Converters With Inductor Resistance

Abstract: Since the DC-DC boost converter exhibits highly nonlinear and non-minimum phase properties, it is not an easy task to design a controller that is robust against load perturbations. This paper presents a dynamic output feedback controller for a DC-DC boost converter that has a practical inductor and a series resistance. In order to maintain its robust output voltage regulation, the proposed controller adopts a simplified parallel-damped passivity-based controller (PD-PBC). A complementary proportional-integral-differential (PID) controller to the PD-PBC has been designed for removing the steady state error owing to the parasitic resistance. We present sufficient conditions for the asymptotic stability of the augmented system with an additional dynamic system. Computer simulations and experimental tests under reference step changes and load perturbations confirm the improved performance of the proposed approach.