Output Linearization Research Articles

A study and analysis of input output linearization control of permanent magnet synchronous motor based on fuzzy observer

A study and analysis of input output linearization control of permanent magnet synchronous motor based on fuzzy observer

Revisiting the Normal Form of Input–Output Linearization

Revisiting the Normal Form of Input–Output Linearization

Proposed Feedback-Linearized Integral Sliding Mode Control for an Electro-Hydraulic Servo Material Testing Machine

High-precision tracking of an electro-hydraulic servo material testing machine’s force control system was achieved using a proposed integral sliding mode control method based on feedback linearization to improve the machine’s force control performance and anti-interference ability. First, the electro-hydraulic servo system’s nonlinear mathematical model was established, and its input–output linearization was realized using differential geometry theory. Second, integral sliding mode control was introduced into the controller and the feedback-linearized integral sliding mode controller was designed. The controller’s stability was proven based on the Lyapunov stability principle. Finally, a simulation model of the electro-hydraulic servo material testing machine’s force control system was established using AMESim/Simulink software. The designed controller was simulated and verified, and the control effects of the system’s different amplitudes and frequency signals were analyzed. The results showed that the feedback-linearized integral sliding mode control algorithm could effectively improve the system’s force tracking accuracy and parameter adaptability, yielding better robustness and a better control effect.

Linearization of the tunneling magnetoresistance sensors through a three-step annealing process

For tunneling magnetoresistance (TMR) sensors using magnetic tunnel junctions (MTJ), the sensor output linearization is of great importance for practical applications. The current study employs a three-step magnetic annealing procedure for linearizing the double-pinned MTJs, setting the magnetization of the free layer to be orthogonal to that of the reference layer. Compared to the traditional two-step annealing procedure, the three-step annealing procedure benefits from a lower annealing temperature and excellent linearity performance. Utilizing the three-step annealing procedure, the sensitivity and the detectivity of 1.57 mV/V/Oe and 29.3 nT Hz0.5 @ 10 Hz, respectively, was achieved in a full Wheatstone bridge TMR sensor. Our results reveal a new pathway for linearization of the TMR sensors through three-step annealing process.

Vehicle Stability Analysis by Zero Dynamics to Improve Control Performance

Good stability performance is a prerequisite for the motion of vehicles controlled either by a human driver with advanced driver-assistance systems or by highly automated driving systems. Hence, vehicle motion control has become the core technology for vehicles driving everywhere in all conditions. However, vehicles might start to skid or spin, i.e., lose motion stability, even if some vehicle motion controllers are active. This motivates our work with a twofold contribution. First, we show that there are zero dynamics when input–output linearization is used in controller design. By analyzing the behavior of the zero dynamics, we identify why and under what conditions the vehicle starts to slide sideways and then spins. Second, based on the analysis results, we propose a cooperative control scheme to improve the motion stability of vehicles. A linear combination of the yaw rate and the sideslip angle is used as the control output, and we discuss which linear combinations are appropriate. Finally, simulation results, including a hardware-in-the-loop simulation, and experimental results with a refitted DongFeng E70 vehicle are given to illustrate the effectiveness of our method.

Design and Analysis of an Input–Output Linearization-Based Trajectory Tracking Controller for Skid-Steering Mobile Robots

This manuscript presents a control law based on the kinematic control concept and the input–output linearization approach. More specifically, the given approach has the structure of a two-loop controller. A rigorous closed-loop system analysis is presented by using known theory on perturbed systems. By assuming that the desired velocity in the body frame is persistently exciting, the uniform bound of the tracking error in earth coordinates is ensured. A simulation study using practical mobile robot parameters shows the viability of the introduced approach. In addition, two known trajectory tracking controllers are simulated in order to compare the performance of the proposed technique. Better tracking accuracy is obtained with the proposed control approach, even if uncertainties in the knowledge of the friction coefficients are presented.

Modeling and control for PEMFC hydrogen management subsystem based on neural network compensation and prescribed tracking accuracy

The hydrogen management subsystem plays an important role in fuel cell automobiles as it impacts the output performance of the polymer electrolyte membrane fuel cell (PEMFC). This paper investigates the design and implementation of the neural network-based control for anode pressure and hydrogen excess ratio (HER). Firstly, the coupled nonlinear multiple-input-multiple-output (MIMO) system is transformed into canonical form one by using input–output linearization. Secondly, the neural network control scheme is proposed to regulate the anode pressure and HER without using the exact prior knowledge of the system. Moreover, a prescribed performance function (PPF) is proposed to guarantee the overshoot and steady-state tracking errors within the quantitative boundaries. To avoid the laborious parameter selection, the restriction that the initial error is within the performance function bound is relaxed by proposing a tuning function. Finally, the Lyapunov stability theory, numerical simulations and hardware-in-loop (HIL) experiments show the effectiveness of the proposed controller. Simulation and HIL experiment results show that the proposed control strategy is effective in realizing quantitative adjustment of tracking error while showing the best performance indexes including the root mean square error (RMSE) and minimum deviation of tracking error (DTE) compared with the neural network controller (NNC) and traditional prescribed performance controller (TPPC).

Pressure and oxygen excess ratio control of PEMFC air management system based on neural network and prescribed performance

The simultaneous control of the pressure and oxygen excess ratio (OER) plays an important role in improving the performance and safety of polymer electrolyte membrane fuel cell (PEMFC). Nevertheless, the coupling characteristics and nonlinearity existing in the PEMFC air management system need to be solved. To this end, the original coupled nonlinear PEMFC air management system is first transformed into the cathode pressure and OER subsystems by using input–output linearization. For the cathode pressure subsystem, the neural network (NN) control scheme is proposed to maintain the stable tracking of cathode pressure. For the OER subsystem, a prescribed performance function (PPF) is proposed and therefore, the overshoot and steady state of OER tracking error is guaranteed within the quantitative boundary. Moreover, the restriction that the initial error is within the performance function bound is relaxed by proposing a tuning function. Finally, the Lyapunov stability theory, numerical simulations and hardware-in-loop (HIL) experiments show the effectiveness of the proposed controller. Compared with the proportional integral derivative (PID) controller, NN controller without PPF and NN controller with conventional PPF, the NN controller with the proposed PPF can realize the quantitative adjustment of OER and give more improvements to the system safety.

Development of a Motion Control for a highly dynamic, self-propelled driving simulator

The new concept of the self-propelled driving simulator comprises a hexapod, a yaw joint and a wheel-based motion platform with four individually steerable wheels. This concept provides a theoretically unlimited motion range, which especially enables highly dynamic drive maneuvers. To ensure an omnidirectional motion, the motion platform has to accelerate instantly in any direction. This requirement leads to the main challenges in the control system of the simulator: taking into account the nonlinear and transient tire characteristics and generating the target accelerations as expected by the driver. According to these requirements, the Motion Control is only for controlling the horizontal dynamics of the motion platform. The Motion Control presented in this paper includes various model definitions, especially regarding the essential tire characteristics considered within an extended HSRI (Highway Safety Research Institute) tire model. The Motion Control as Two-Degrees-of-Freedom control contains a Feedforward for generating target body forces, a Control Allocation for an optimal force distribution to the wheels, a Single Wheel Control as a specific control of the tire forces, and a Compensation Control on acceleration level. Investigation of this control by simulation, using a simplified reference model, already revealed a high controller performance regarding accuracy and quality. The optimal force distribution leads to an equal adhesion utilization and the Compensation Control compensates the remaining Single Wheel Control deviations. Difficulties only occur for the steering angle in the case of low velocity up to a standstill. Due to the exact input–output linearization, the Single Wheel Control leads to a singularity and instability. Therefore, the steering angle requires exceptional control in this case.

Robust Optimal Output-Tracking Control of Constrained Mechanical Systems With Application to Autonomous Rovers

This article presents a robust optimal output-tracking control strategy for underactuated mechanical systems whose motion is restricted by mixed holonomic and nonholonomic constraints. Autonomous rovers/cars and unmanned underwater/aerial vehicles are a few examples of such systems that must often operate in harsh environments and under uncertain conditions. We present a comprehensive control analysis of this large class of nonlinear systems, including the existing studies on local reachability and static state feedback linearization. We also propose a local observability analysis of the feedback transformed input–output linearized systems. Based on the input–output linearization of the holonomically restricted nominal system, we develop a sliding mode control strategy that is robust against the projected effects of uncertainties and disturbances on the system’s output. Asymptotic stability of the output toward a bounded desired trajectory is proven using Lyapunov’s direct method, while the system’s internal stability (in the sense of boundedness) is investigated based on the notion of tracking-error zero dynamics. Time-dependent bounded matched uncertainties in the inertia parameters and disturbance forces arising in the unrestricted system are considered in this study. We propose an optimal sliding manifold according to the finite-horizon linear-quadratic regulator design problem with split boundary value conditions. The developed control strategy is implemented on a six-wheel autonomous Lunar rover in a simulation environment and its performance is compared with that of an optimal proportional–integral–derivative feedback, feedforward controller. The optimal sliding mode controller shows superior performance in trajectory tracking with acceptable control actions.

Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

For fuzzy systems to be implemented effectively, the fuzzy membership function (MF) is essential. A fuzzy system (FS) that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output (SISO) FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output. Utilizing a variety of non-linear techniques, a SISO FS is simulated. The results of FS experiments conducted in comparable conditions are then compared. The simulated results and the results of the experimental setup agree fairly well. The findings of the suggested model demonstrate that the relative error is abated to a sufficient range (≤ ± 10%) and that the mean absolute percentage error (MPAE) is reduced by around 66.2%. The proposed strategy to reduce MAPE using an FS improves the system’s performance and control accuracy. By using the best input and output MFs protocol, the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs. The proposed fuzzy system performed far better than other modern days approaches available in the literature.

An Intrinsically Stable MPC Approach for Anti-Jackknifing Control of Tractor-Trailer Vehicles

Tractor-trailer vehicles are affected by jackknifing, a phenomenon that consists in the divergence of the trailer hitch angle and ultimately causes the vehicle to fold up. For the case of backward motions, in which jackknifing can occur at any speed, we present a control method that drives the vehicle along generic reference Cartesian trajectories while avoiding the divergence of the hitch angle. This is obtained thanks to a feedback control law that combines two actions: a tracking term, computed using input–output linearization, and a corrective term, generated via IS-MPC, an intrinsically stable MPC scheme which is effective for stable inversion of non-minimum phase systems. The successful performance of the proposed anti-jackknifing control is verified through simulations and experiments on a purposely built one-trailer prototype. To show the generality of the approach, we also apply and test the proposed method on a two-trailer vehicle.

Effect of Defective Five Levels Inverter on Input Output Linearization Control for Induction Motor

Induction Motor (IM) speed control is an area of research that has been in prominence for some time now. In this paper, a nonlinear controller is presented for induction motor drives. The nonlinear controller is designed based on input-output feedback linearization control technique. The input-output feedback linearization control decouples the flux control from the speed control and makes the synthesis of linear controllers possible. This article presented input output linearization control for induction motor associated with NPC three levels inverter defective, the inverter faults are usually caused by operating faults in the switch elements. Switching faults occur in rectifier diodes, the capacitor and IGBT switches of the inverter. The inverter switches defected reduce the performance of the motor, in our study , we apply the input-output linearization control to test their robustness and performance for detection of fault influence on the physical parameters of the motor, for this purpose we apply two faults, we start by creating a fault in two switches K1 and K7 which have in the first and second arm of the inverter then we create a faults in the three switches K1, K7 and K10 which are in the three arms of the inverter. The simulations results are done by the use of MATLAB/Simulink that show the detection fault effect by input output linearization control on the different induction motor responses.

Control of drill string torsional vibrations using optimal static output feedback

Failures during oil extraction can have impacts on exploration costs and time, especially those caused by vibrations during the drilling phase. A proper way to reduce these detrimental failures is to design an active controller that efficiently mitigates vibrations with the constraint of using the limited amount of real time data available in field operations. In this context, a technique that might be suitable for the controller design is the optimal static output feedback (OSOF), that considers only the measured signals for feedback. Recently, the authors proposed some modifications to this technique, involving the addition of sensors locations and a new way of dealing with the dependence of the controller parameters on the system initial condition. The aim of this contribution is to apply this novel control technique to suppress drill string torsional vibrations and address all the particular aspects regarding the application of the proposed controller for the drill string problem, i.e., reformulation of the equations of motion as a stabilization problem, determination of the output matrix and linearization. Analyses and comparison of the proposed controller with proportional–integral (PI) and linear quadratic regulator (LQR) controllers are performed considering numerical simulations of a finite element model with a non-regularized dry friction function. Results indicate that the proposed controller has a performance similar to the LQR with a significantly reduced number of sensors, and also exhibits superior performance and robustness with an equivalent control effort over the PI controller.

Trajectory tracking control of a self-balancing robot via adaptive neural networks

In order to ensure trajectory tracking on a two degrees-of-freedom self-balancing robot (SBR) a control scheme, based on the combination of adaptive neural networks and input–output linearization, is presented in this paper. Both external and internal dynamics are analyzed and proof of uniform ultimate boundedness of the position errors is given. The controller performance is assessed via real-time experiments and compared with respect to other control schemes. Better tracking accuracy and disturbance rejection capability is produced by the introduced controller.

Nonlinear Control Design for Regenerative and Hybrid Antilock Braking in Electric Vehicles

The use of regenerative braking and recent electrohydraulic or electromechanical brake-by-wire systems with enhanced dynamics enables improved antilock braking control performance in electric vehicles. However, the possible benefit is often limited by suboptimal control architectures with separate distributed controllers for electric motor (EM) and friction brake torque requests. Furthermore, oversimplifications in synthesis models for brake control design constrain the achievable performance. Instead, to date, threshold-based algorithms are widely used in modern production vehicles despite having been designed for slower hydraulic brake systems originally. To exploit the full potential, this work proposes a continuous nonlinear control design using input–output linearization for robust wheel speed tracking control. The design uses a unified controller for the redundant actuators. In addition, it explicitly considers drivetrain oscillations induced by regenerative braking using onboard EMs in the synthesis model. The stability for the resulting zero dynamics is shown through the Lyapunov analysis. The nonlinear controller is augmented by a subsequent control allocation for splitting the control effort on the redundant actuators. The allocation design ensures consistent control performance for both regenerative braking and hybrid braking while simultaneously aiming for high-energy recovery. The overall antilock braking control design is implemented in an electric test vehicle. Its tracking performance, disturbance attenuation, and robustness are experimentally validated through various emergency braking maneuvers on different road surfaces.

Phase-Unsynchronized Power Decoupling Control of MMC Based on Feedback Linearization

The traditional power decoupling control of modular multilevel converter (MMC) is based on the phase synchronization achieved by the phase-locked loop (PLL), and MMC intricate inner dynamics are ignored, which jeopardizes the system stability and power decoupling. To improve the performance, a nonlinear phase-unsynchronized power decoupling control for MMC is proposed without PLL. At first, the MMC model as a nonlinear multi-input multi-output system is developed. The developed model illustrates that the MMC power coupling is caused by the ac current as well as the MMC capacitor voltages. Then, through the input–output linearization method, the proposed method decouples the MMC output powers without the phase synchronization. Involving integral control, the dynamics of MMC output powers are designed as second-order systems so that the control parameters can be easily determined based on the required transient performance. To verify the accuracy and correctness of the developed model, a comparison of MMC dynamics with the proposed control is conducted between the numerical solution in MATLAB and the EMT simulation in PSCAD/EMTDC. Moreover, seven cases in the EMT simulations and four cases in the experimental results demonstrate the desired phase asynchronization and power decoupling performance of the proposed method compared with the conventional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dq$</tex-math></inline-formula> control. At last, the system zero dynamic stability is investigated and the influences of MMC control parameters, ac-side voltage, dc-side voltage and equilibrium points are analyzed to evaluate the stability performance of the proposed method.

Sliding Mode Control Based on Linear Extended State Observer for DC-to-DC Buck–Boost Power Converter System With Mismatched Disturbances

This article presents a new control strategy merging a sliding mode control (SMC) with a linear extended state observer (LESO) to regulate the output voltage of the buck&#x2013;boost power converter system affected by matched and mismatched disturbances. The SMC&#x2013;LESO schema uses the input&#x2013;output linearization approach and the equivalent control method to determine the boundary layer around the sliding surface. This boundary layer depends on the sliding surface and the equivalent control value, depending on the estimated variables obtained through a LESO. With this schema, the unknown matched, and mismatched disturbances are observed and compensated by an adaptation of the SMC. The proposed approach minimizes the sliding surface chattering and improves the performance against sudden static and dynamic load changes as well as voltage variations on the power supply input. An experimental comparison with traditional sliding mode control and classical proportional&#x2013;integral&#x2013;derivative control is performed, confirming the proposal&#x0027;s effectiveness. The closed-loop stability (observer-controller-plant) is guaranteed in the input-to-state stability framework.

Fuzzy model predictive MPPT control of interconnected wind turbines drivetrain

AbstractThis paper provides a novel fuzzy‐based model predictive control (MPC) of a hydraulic‐based drivetrain that can combine the harvested energy of multiple wind turbines into one generator. The modeling is based on the sector nonlinearity approach and Takagi–Sugeno (T‐S) fuzzy. In addition, prediction and quadratic programming (QP) are formulated to obtain suitable control commands for the displacement of hydraulic pumps. Input–output linearization is utilized to control the generator torque load to regulate the speed. Minimizing the cost function for T‐S fuzzy‐based interconnected hydraulic wind turbine systems is converted to a QP problem to reduce the computation complexity. Therefore, the proposed nonlinear fuzzy‐based MPC scheme is more realistic to be implemented in real time. The feasibility of the optimization problem is improved when a new ellipsoidal terminal constraint is considered in the controller design process. Offline linear matrix inequalities (LMIs) are utilized to obtain the terminal cost of T‐S fuzzy‐based nonlinear systems. The simulation results demonstrate a significant reduction in computations and control efforts compared to LMI‐based MPC, kω2 approach, and (proportional–integral) PI controllers. It is also demonstrated that the system response to a sudden wind speed variation contains less overshoot and settles in a shorter time.

Output Tracking of Some Class Non-Minimum Phase Nonlinear Systems via linearization Input-Output

We present an output tracking problem for a non-minimum phase nonlinear system. In this paper, the input control design to solve the output tracking problem is to use the input output linearization method. The use of the input output linearization method cannot be initiated from output causing the system to be non-minimum phase. Therefore the output of the system will be redefined such that the system will become minimum phase with respect to a new output.