State Observation Model Research Articles

A novel learning-based robust model predictive control strategy and case study for application in optimal control of FCEVs

With the gradual implementation of carbon-neutral policies, fuel cell electric vehicles (FCEVs) have gained widespread recognition as a promising solution. Due to multi-source electromechanical coupling, the powertrain of FCEVs constitutes a complex nonlinear system, necessitating an advanced energy management strategy for effective control to enhance economic performance. In this study, a novel learning-based robust model predictive control (LRMPC) is proposed for a 4WD FCEV, integrating a high-confidence velocity estimation model and an accurate state observation model to enhance energy-saving potential, robustness, and real-time application performance under various driving conditions. To enhance robustness and adaptability, deep forest with superior feature fusion abilities is employed to generate future velocity reference datasets based on FCEV operating scenarios. To improve state observation accuracy and control effectiveness, a novel built-in state observation model based on machine learning algorithms is established, leveraging knowledge of nonlinear systems within the FCEV powertrain. Subsequently, to enhance the real-time applicability of LRMPC, an explicit control scheme for multidimensional mapping based on explicit data tables is utilized for accurate state transformation, incorporating input features such as vehicle states and component states. Simulation evaluations and hardware-in-the-loop tests demonstrate the improved economic performance and real-time application ability of LRMPC, showcasing its promising performance.

Research on longitudinal control technology in the transition section of tilt-rotor aircraft based on linear active disturbance rejection control

To solve the problem of strong coupling and control redundancy in the transition section of the tilt-rotor, this paper presents a longitudinal control method based on linear active disturbance rejection control (LADRC), verified by simulation. Firstly, the nonlinear dynamics model of tilt-rotor aircraft based on the mechanism method is established, and the model’s accuracy is verified by comparing the trim result with the generic tilt-rotor aircraft simulation (GTRS) model verified by flight test in XV-15. Then, the control and redundant rudder surface assignment strategies for tilt-rotor aircraft are proposed. Then, the vertical and vertical control law of tilt-rotor aircraft is designed, the linear expansion state observer (LESO) model is analyzed and derived, and the linear active disturbance rejection controller suitable for attitude loop control of tilt-rotor is designed. The altitude and velocity loops are controlled. Finally, the proposed linear active disturbance rejection control (LADRC) controller is verified by simulation, and the whole process of tilting transition is simulated. The results show that the proposed control strategy and the controller designed in this paper have strong anti-disturbance ability, fast response speed, and high control precision and can be used in the transition section of tilt-rotor aircraft. The vertical direction shall be well controlled.

Disturbance estimation for the hydraulic drive system based on the state observer and neural network

The hydraulic servo drive system has the ability to operate stably with high reliability in conditions of continuous operation and large capacity. Aside from these advantages, the system always has nonlinear and uncertain factors that affect and reduce the quality of the controller. These factors include the affected disturbances and the uncertainty of the system model. This paper proposes a method to identify the sum of nonlinear effects and disturbances for the hydraulic drive system, which uses the axial piston pump, based on the application of the Radial Basic Functions neural network (RBF) and state observer model. The simulation results show that the total disturbances and nonlinear effects can be accurately estimated in real time through the weight updating rule.

Observer-based SMC design for stochastic systems with Levy noise

Abstract This work addresses the problem of sliding mode control (SMC) design for a continuous-time non-linear stochastic system with Levy-type noise. A state observer model is constructed to estimate the unavailable state information. Furthermore, Levy-type noise is considered to analyse small perturbations and to characterize the appearance of large samples that will occur in the system. Lyapunov stability and SMC theory are used to provide some sufficient conditions that ensure the stochastic stability of the error system and reachability of the predefined sliding surface. Finally, an example is given to demonstrate the feasibility of the proposed approach.

Kernel Reinforcement Learning-assisted Adaptive Decoder Facilitates Stable and Continuous Brain Control Tasks.

Brain-Machine Interfaces (BMIs) assist paralyzed people to brain control (BC) the neuro-prosthesis continuously moving in space. During the BC process, the subject imagines the movement of the real limb and adapts the brain activity according to the sensory feedback. The neural adaptation in the closed-loop control results in complex and changing brain signals. Simultaneously, the decoder interprets the time-varying functional mapping between neural activity and continuous trajectory. It is crucial and challenging to accurately and adaptively track the mapping to help the subject accomplish the BC task with a stable performance. Existing Kalman Filter (KF) based decoders achieve continuous trajectory control by linearly interpreting neural firing observations into self-evolving prosthetic states. However, the linear neural-state mapping might not accurately reflect the movement intention of the subject. In this paper, we propose a novel method that allows subjects to achieve continuous brain control efficiently and stably. The proposed method incorporates a kernel reinforcement learning method into a state-observation model to decode the nonlinearly neural observation into a continuous trajectory state. The state transition function ensures the continuity of the prosthetic state. The kernel reinforcement learning allows the quick adaptation of the nonlinear neural-movement mapping during the BC process. The proposed method is tested in an online brain control reaching task for rats. Compared with KF, our method achieved more successful trials, faster response time, shorter inter-trial time, and remained stable over days. These results demonstrate that the proposed method is an efficient tool to assist subjects in brain control tasks.

Estimation of Liquid Level in a Harsh Environment Using Chaotic Observer

The increased demand for liquid level measurement has been a key factor in designing accurate and reliable control systems. Here, a study was carried out to calculate the liquid level in a tank using a pressure sensor for changes in inlet liquid parameters like temperature, density and velocity. Prediction of their variables for the long term is essential due to the randomness present in the input and measurement. Hence, observer design for state estimation of a non-linear dynamic system with uncertainties in the measurement and process becomes important. This work provides a feedback observer solution for a system with multiple inputs and single measurable output. A full state observer model is developed to estimate a system’s states with a sensor placed at a definite position from the pipe’s input point through which the liquid flows at different densities and temperatures. Using the observability properties, Luenberger full state observer is designed by various methods, verified using MATLAB and SIMULINK for the system state estimation. To incorporate process noise and measurement noise, the Kalman estimator is integrated with the system. Chaotic systems are susceptible to initial conditions, variations in parameters and are complex dynamic systems. However, providing consistently precise measurements through particular meters necessitates time-consuming computations that can be reduced by employing machine learning approaches that make use of optimizers. The results obtained are compared with the prediction models obtained using Artificial Neural Networks and are validated through the readings obtained from the experimental setup.

State estimation of Lipschitz continuous switched systems with unknown switching signals

For Lipschitz continuous switched systems the problems of reconstructing the state of the system and of the switching signal are tackled assuming no knowledge of the switching signal, except its dwell time. To do so, algorithms to estimate the switching times and the current mode of the system, were developed that take into account the non-smoothness of the vector fields and the output map of the system. Although an specific state observer model was used in the simulations, the algorithm that reconstructs the switching signal runs irrespective of the state estimator selected.

State Estimation of Time-Varying MRI with Radial Golden Angle Sampling

We propose a state estimation approach to time-varying magnetic resonance imaging utilizing a priori information. In state estimation, the time-dependent image reconstruction problem is modeled by separate state evolution and observation models. In our method, we compute the state estimates by using the Kalman filter and steady-state Kalman smoother utilizing a data-driven estimate for the process noise covariance matrix, constructed from conventional sliding window estimates. The proposed approach is evaluated using radially golden angle sampled simulated and experimental small animal data from a rat brain. In our method, the state estimates are updated after each new spoke of radial data becomes available, leading to faster frame rate compared with the conventional approaches. The results are compared with the estimates with the sliding window method. The results show that the state estimation approach with the data-driven process noise covariance can improve both spatial and temporal resolution.

A hybrid fault prediction method for control systems based on extended state observer and hidden Markov model

AbstractFault diagnosis and prediction for complex control systems rely either on the collection of rich data for training neural networks or on the system models and prior knowledge of faults. These methods are difficult to apply directly in complex integrated systems due to the large uncertainties in practical scenarios. A new fault diagnosis and prediction technique that is based on extended state observer (ESO) and a hidden Markov model (HMM) for control systems is proposed in this paper. Real‐time and predictive information that is obtained by ESO of the active disturbance rejection control (ADRC) is utilized to improve the HMM method for the fault prediction of control systems with large uncertain disturbances. The proposed approach realizes a high recognition rate with a small demand for data, and the dependence on the system model is weak without prior knowledge of faults. Fault prediction of the control system output can be realized without additional sensors. The proposed solution is evaluated in simulations of an asynchronous servo motor control system against the traditional control method and the ADRC control. The results indicate that the proposed method performs well in fault prediction and outperforms the traditional method in terms of control when disturbances and failures occur.

Research on a Real-Time Estimation Method of Vehicle Sideslip Angle Based on EKF.

In this article, a real-time vehicle sideslip angle state observer is proposed, which is based on the EKF algorithm. Firstly, based on a 2-DOF dynamical model and the tire lateral force model, the vehicle sideslip angle state observer model with a self-adapted truncation procedure is established by combining the EKF and the least squares methods. The calculation of the Jacobi matrix in the time domain is transformed into a calculation in the frequency domain. A self-adapted update noise estimation method and an initial value setting strategy are proposed as well. Finally, a hardware-in-the-loop simulation is carried out by Matlab/Simulink-CarSim-dSPACE, and the real-time reliability of the estimation method is verified and analyzed by RMSE.

Model predictive control of hydraulic drive unit considering input delay

Hydraulic drive unit (HDU) is a typical actuator, but the characteristic of input delay hinders the application of many advanced control methods in HDU. First, in this paper, a mathematical model of HDU with input delay is established and the parameters are identified. Then, aiming at the input delay problem in HDU, a Smith estimated compensation model predictive control (SECMPC) strategy is proposed. On the one hand, the input delay state equation is employed to be a mathematical pattern for the state observation and predictive model. However, the combination between model predictive control (MPC) and Smith estimated compensation (SEC) is realized, the system state at k + d (d is the time delay coefficient) time is estimated in advance at k time to compensate the delay of the state. And then the prediction model based on input delay state equation is used for model prediction and rolling optimization. Thus, the delay system which is unstable is promoted to a stable system without delay. The effectiveness of SECMPC is proved with the HDU experiment and simulation; the maximum experimental displacement error of traditional MPC control is 15 mm, while that of SECMPC control is 8 mm. The SECMPC have some guiding significance for the control of systems with input delay.

Parameter estimation based robust liquid level control of quadruple tank system — Second order sliding mode approach

This article proposes a liquid level control scheme for a quadruple tank process (QTP) plant. A robust control is proposed using higher order sliding mode (HOSM) control. The four tank setup has opposite tanks actuated by a single pump and unmeasured liquid outflow from upper tanks affecting the levels in the lower tanks. This renders nonlinearity in the dynamics with uncertain behavior. A novel experimentation based estimator is proposed for identification of the unknown control valve ratios of the plant. The estimated parameter is used for robust control design and state observer model. The unmeasurable states are estimated using a sliding mode observer and the required derivatives of liquid levels in the tanks are evaluated using a HOSM differentiator. The complete schema is tested in simulation and validated experimentally on a QTP setup.

A Multi Target Algorithm for Image Target Tracking

As technology advances, improving the accuracy of image target tracking is extremely important for medicine, aerospace and military, industry and agriculture. Image target tracking usually apply the method of manual extraction, which is not only a waste of time and power, but also has many difficulties in extraction. However, multi target algorithm can make up for its shortcomings, and multi target algorithm develops from underlying characteristics to multi-level, abstract features in the form of data. This paper, proposed a novel multi target algorithm for image target tracking for solving the above shortcomings. In the multi-target tracking problem, the random set is actually the number of elements and a set of random variables. When the number of targets is unknown or changing, the number of targets is a discrete random variable, the dimension of the state space will be different with the target value of the number of changes, so the multi target state model and observation model can be expressed as random finite sets form. Simulation results show that compared with traditional method, this method has higher accuracy and low loss rate.

A state estimation strategy for a nonlinear switched system with unknown switching signals

A strategy is presented to estimate the state of a nonlinear autonomous switched system, with no knowledge of the switching signal, except its dwell time. To do so, algorithms to estimate the switching times and the current mode of the system are developed. The estimation of the switching times is based on approximating the second ( generalised) derivative of the output of the system via a convolution of this signal with a suitable function and on detecting the corresponding spikes. To estimate the modes, a scheme based on the use of a bank of observers (one for each mode) and of a bank of subsystems (for each step of the estimation process a suitable subset of the subsystems of the switched system) is developed. The algorithms run regardless of the state observer model, as long as its output error norm decays exponentially with a controlled decay rate.

Extended dissipative sliding mode control for nonlinear networked control systems via event-triggered mechanism with random uncertain measurement

This work concentrates on addressing the sliding mode control problem of continuous-time nonlinear networked control systems. Considering the state information may not be utterly available in practice, a state observer model is designed to estimate the state information. Meanwhile, a type of discrete-time event-triggered mechanism is utilized to filter the sampled signal for reducing the occupation of network bandwidths and the transmission rate of resources. In addition, a random variable obeying the Bernoulli distribution is adopted to describe the phenomenon of uncertainties randomly occurring in the measurement. With the aid of the Lyapunov stability and sliding mode control theory, some sufficient criteria are given to both guarantee the mean-square asymptotic stability of the overall closed-loop system with an extended dissipative performance, and the reachability of predefined sliding surface. Whereafter, the event-triggered weighting matrix, and gains of sliding mode controller and with observer are obtained by solving the matrix convex optimization problem. Finally, the feasibility of the presented scheme is demonstrated through two illustrative examples.

A real-time calibration method for the systematic errors of a star sensor and gyroscope units based on the payload multiplexed

With the star sensor and gyroscope units integrated for attitude determination, it will provide continuous and accurate attitude information for satellites with a high update rate. This integration could take full advantage of each other. In order to guarantee the accuracy of satellite attitude determination, the calibration of its systematic errors is of critical importance. In this paper, a calibration method for the star tracker and GUs integrated system is proposed based on the satellite payload multiplexed. Firstly, the satellite attitude kinematics model is introduced. Then the measurement model with respect to the systematic errors including the installation errors and GUs drift errors is built for the star sensor, the payload and the GUs. Finally, the state model and observation model are established, according to which the systematic errors are calibrated by a Kalman filter. Simulation results indicate that the proposed method is effective at estimating the systematic errors of the star sensor and GUs integrated system.

Stable control of the high-speeding magnetically suspended rotor based on extended state observer and two-degree freedom internal model control for control moment gyros with serious moving-gimbal effects

For a magnetically suspended control moment gyro (MSCMG), which is an ideal attitude actuator for its large outputting control moment and fast response, the moving-gimbal effects due to the coupling between the moving gimbal and high-speeding rotor will make the magnetically suspended rotor (MSR) unstable. To improve control precision, both the dynamic model of MSR and the feedback linearization control are done to decouple tilting motion, and poles of the system are reconfigured to reduce control error. To suppress the varying disturbance moments caused by moving-gimbal effects, an extended state observer (ESO) is originally designed to estimate and compensate them timely and accurately. To improve system robustness, a two-degree freedom internal model control (2-DOF IMC) is researched to suppress model error. Compared with existing proportional integral derivative (PID) control method, simulations done on a single gimbal MSCMG with 200 N.m.s angular momentum indicated that this presented control method with ESO and 2-DOF IMC can suppress the moving-gimbal effects more effectively and make the rotor suspension more stable.

A Nonlinear State Observer for Sensorless Speed Control of IPMSM

This paper presents a nonlinear state observer model for sensorless speed control of an interior permanent magnet synchronous motor (IPMSM) drive. First, an augmented state space model of the IPMSM and the system uncertainty dynamics is obtained in the d − q synchronously rotating reference frame. Then, the proposed nonlinear state estimator based on an extended Luenberger observer is constructed by using rotor position, rotor speed, q-axis stator current, and system uncertainty vector as the state variables. Hence, the estimated system parameter uncertainties can be used to dramatically improve the performance of the speed controller without any additional observer. The stability of the proposed observer at low and high speed is asymptotically guaranteed by Lyapunov stability analysis. In addition, the proposed model is simple to carry out, so it does not require a complex calculation. Simulation results obtained via a Matlab/Simulink software indicate that the proposed observer model can assure an excellent state estimation over a wide speed range even when the rotor speed or load torque changes abruptly as well as the difference in the initial values of real and estimated rotor position.

Observer-Based Adaptive Fuzzy Control for Intelligent Ship Autopilot with Input Saturation

In this paper, the design problem of intelligent ship autopilot with unmeasured yaw rate and input saturation is investigated based on adaptive fuzzy control. First, an adaptive fuzzy output feedback controller is proposed, then, to improve the convergence speed of the tracking error in the sense of finite time, a finite-time adaptive fuzzy output feedback controller is also designed to improve the control performance. By employing fuzzy logic system (FLS) to estimate the unknown nonlinear function, an adaptive fuzzy state observer is designed to estimate the unmeasured state. An adaptation auxiliary signal for input saturation is also established to compensate the mismatch between the controller signal and the actuator signal. Combining state observer model with backstepping method, two different adaptive fuzzy output-feedback controllers with composite parameter adaptive laws are constructed, respectively. Based on Lyapunov theory, it is proved that all signals in the closed-loop systems are bounded. Finally, the simulation results are conducted to demonstrate the effectiveness of the proposed schemes.

Simultaneous Control in Belief Space for Circular Insertion in Precision Assembly

Simultaneously inserting multiple objects is an essential topic in precision assembly to compose complicated shapes. This task involves the acquisition difficulty of unobservable interaction states, which makes it hard to plan the insertion. To solve it, this article investigates the circular assembly of multiple objects and proposes a strategy to control the simultaneous insertion in belief space. We first present the insertion state transition and observation models, in which the stochastic parts are modeled as Gaussian noise, and then estimate the belief state using an extended Kalman filter. An optimization approach is discussed, for the compensational movement planning, to decrease the estimated radial interaction forces and the toward-center movement is thus determined, considering the optimized compensational movement and the belief state. Experiments are carried out to demonstrate the validation of the proposed method.