Unknown Reference Signal Research Articles

Global practical tracking for uncertain nonlinear systems with unknown control coefficients and polynomial growth

This paper studies the problem of global output-feedback tracking control for a class of uncertain nonlinear systems. The system studied in this paper has unknown time-varying control coefficients and unknown reference signal. Notably, the nonlinearities of the system are bounded by the lower triangular linear unmeasured states multiplying the unknown constant, the polynomial-of-output and the polynomial-of-input growth rates, which indicates the presence of serious uncertainties. Motivated by the closely related works, by combining the ideas of universal control and deadzone with backstepping technique, this paper presents a new adaptive tracking control scheme based on two dynamic high-gains in the new forms. The proposed control scheme ensures that the tracking error converges to a specified arbitrarily small interval range after a finite time while the state of the resulting closed-loop system is globally bounded. Finally, the effectiveness of the control scheme is verified by an illustrative example.

Constrained real-time control of hydromechanical powertrains – methodology and practical application

Axial piston motors and pumps are core components in many hydromechanical systems as for instance the powertrain of wheel loader vehicles. The optimal performance of the individual components as well as the entire powertrain requires a fast and exact control of both the motor’s swivel angle as well as the pressure difference over the pump by means of the currently available information and despite their respective a-priori unknown reference signal generated by the driver with a joystick or gas pedal operation. Therefore, it is essential that the swivel angle and pressure controllers fully exploit the possible dynamics and the constrained resources of the nonlinear swivel angle displacement and the pressure adjustment system, respectively. In this work, a flatness-based feedforward control strategy for the motor’s swivel angle as well as a flatness-based tracking two-degree-of-freedom controller for the pump pressure are proposed. For the realization of the flat feedforward and the tracking controller, a constrained online trajectory generation by means of a nonlinear (switched) state variable filter is designed for each. Therefore, both controllers are capable of considering the relevant state and input constraints in real-time and without relying on an online optimization. The proposed control algorithms are both discussed and their performance is illustrated with measurement data from a wheel loader vehicle.

Constrained Gaussian Process Learning for Model Predictive Control

Many control tasks can be formulated as tracking problems of a known or unknown reference signal. examples are motion compensation in collaborative robotics, the synchronisation of oscillations for power systems or the reference tracking of recipes in chemical process operation. Both the tracking performance and the stability of the closed-loop system depend strongly on two factors: Firstly, they depend on whether the future reference signal required for tracking is known, and secondly, whether the system can track the reference at all. This paper shows how to use machine learning, i.e. Gaussian processes, to learn a reference from (noisy) data while guaranteeing trackability of the modified desired reference predictions within the framework of model predictive control. Guarantees are provided by adjusting the hyperparameters via a constrained optimisation. Two specific scenarios, i.e. asymptotically constant and periodic REFERENCES, are discussed.

Constrained online trajectory planning for nonlinear flat SISO systems using a switched state variable filter

The problem of constrained Online Trajectory Planning (OTP) for Flatness-based Feedforward Controllers (FFC) is discussed for online tracking problems with a-priori unknown reference signals for the system output. As plant, nonlinear minimum phase Single Input Single Output (SISO) systems with input constraints as well as polytopic constraints on the flat state are assumed. The goal is to achieve both, fast reference tracking and a prioritization-based constraint satisfaction despite avoiding an online optimization. Therefore, the OTP is set up by means of a switched State Variable Filter (SSVF). The switching results from the limitation of the filter’s highest derivative in accordance to the above requirements and is derived with a piecewise solution of an underlying optimization problem. The paper shows the derivation of the SSVF, discusses necessary stability conditions of the proposed concept, and finally demonstrates the SSVF’s performance by means of a simulation study. A comparison to a respective optimal control problem solution and model predictive controller (MPC) is furthermore provided.

Constrained real-time swivel angle control for hydraulic axial piston motors

Axial piston motors are core components in many hydro-mechanical systems as e.g. the powertrain of wheel loader vehicles. The optimal performance of the motor as well as the powertrain requires a fast and exact control of the motor’s swivel angle according to the a-priori unknown reference signal which is e.g. generated by the driver with a joystick or gas pedal operation. Therefore, it is essential that the swivel angle controller fully exploits the possible dynamics and the constrained resources of the nonlinear swivel angle adjustment system. In this work, a novel flatness-based feedforward control strategy for the swivel angle is presented, which is capable of considering the relevant state and input constraints in real-time, i.e. only by means of the currently available information and without relying on an online optimization. The proposed swivel angle controller is finally demonstrated and discussed with the results of both a simulation study and measurement data from a wheel loader vehicle.

Predictive Model Reference Adaptive Controller to Compensate Heart Motion in Minimally Invasive CABG Surgery.

Heart beating is a major challenge in minimally invasive coronary artery surgery. A promising solution is to develop a motion compensation robotic system that gives the surgeon an impression of operating on motionless tissue by synchronizing the surgical tool automatically with the heart tissue motion. To achieve higher control accuracy, an intelligent controller called Predictive Model Reference Adaptive Controller is presented herein, which is adapted not only by observed reference signals but, also by unknown reference signals that are not observed by a camera but could be predicted by a state space estimator. To develop such a system, first the heart surface motion is tracked by the Lucas-Kanade tracking algorithm and validated by human observation. The results of implementing this control algorithm on a real human heart data set show capability of achieving a motion compensation system with high control accuracy.

Analysis of EMD algorithm for identification and extraction of an acoustic signal in underwater channel against wind driven ambient noise

Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, the application of Empirical Mode Decomposition (EMD) technique to analyze nonlinear and non-stationary signals has gained much attention. It is an empirical approach to decompose a signal into a set of oscillatory modes known as intrinsic mode functions (IMFs). In general, Hilbert transform is used in EMD for the identification of oscillatory signals. In this paper a new EMD algorithm is proposed using FFT to identify and extract the acoustic signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in a shallow water region. Data for analysis are collected at a depth of 5 m and 10 m offshore Chennai at the Bay of Bengal. The algorithm is validated for different sets of known and unknown reference signals. It is observed that the proposed EMD algorithm identifies and extracts the reference signals against various ambient noises. Significant SNR improvement is also achieved for underwater acoustic signals.

Adaptive two-degree-of-freedom control of feed drive systems

Feed drive systems are widely used in industrial applications, and many efforts for improving their precision control have been made thus far. One of the basic approaches for improving the control accuracy of feed drive systems is to design a controller based on the internal model principle, which states that for a control system to track a reference signal without a steady state error, it needs to include a generator of the reference signal. Feed-forward controllers, such as the zero phase error tracking controller (ZPETC) proposed by Tomizuka, are also employed for improving control performance. However, prior knowledge of plant dynamics and/or reference signal properties is required for both the internal model principle and the feed-forward controller based designs. For precision control, plant dynamics should be identified in real time because feed drive dynamics are affected by varying conditions, such as frictional and thermal effects. This paper presents a new type of adaptive control for arbitrary reference tracking, which requires neither plant dynamics nor reference signal properties for controller design. This type of controller can also reduce the effect of unknown disturbances. The control system is designed using a discrete-time plant model and consists of adaptive feed-forward and feedback controllers. This design is then applied to a feed drive system with a ball screw drive. The effectiveness of the proposed design is demonstrated by simulation and experimental results, which was obtained by applying the proposed control system to an unknown reference signal whose property is varied during control.

Visual Tracking For References Generated By A Stochastic Model

ABSTRACTThis paper describes a visual tracking system for an unknown reference signal. A time‐varying reference signal is realized as a random process generated by an auto‐regressive (AR) model, which is identifed by a recursive algorithm. Based on the obtained AR model, the future value of reference signal is predicted. We propose a new visual tracking system using generalized minimum variance control (GMVC) and illustrate its properties through experiments.

Discrete-Time Adaptive Servo Control System Identifying the Internal Model and its Application to a Double Inverted Pendulum Control.

For servo systems with unknown reference inputs, this paper proposes an adaptive servo control system identifying the unknown reference signal model. The controller is for a discrete-time, single-input single-output plant. According to the internal model principle of servo control systems, it is neccessary to include the model of a reference input in the servo controller. The servo controller proposed in this paper includes a model identified on-line as the internal model. The design is based on coprime factrization of the plant model over the ring of stable rational functions. It is proved that all internal signals in the control system, error equations and parameter identification law are uniformaly bounded. The scheme is applied to the cart position control of a double inverted pendulum control system.

A Design of an Adaptive Servo Control System Estimating the Internal Model

An adaptive servo control system estimating the unknown reference signal model for a single-input single-output plant is proposed. In order to follow reference signals as in servo systems, the models of the reference signals should be included in the control laws as internal models. So far, in the cases that the model of the signals is unknow or varies, a servo control law can not be designed. To design a servo system in such cases, in this paper, an adaptive adjusting law is used to identify the unknown parameters of the reference signal model. To simplify the control system, the design is based on coprime factorization of the plant model over the ring of stable proper rational functions. Stability of the closed-loop system including the proposed adaptive servo system is proved.

A Design of an Adaptive Servo Control System Estimating the Internal Model

An adaptive servo control system estimating the unknown reference signal model for a single-input single-output plant is proposed. The design is based on coprime factorization of the plant model over the ring of stable proper rational functions. An adaptive adjusting law is used to identify the unknown parameters of the reference signal model. Stability of the closed-loop system including the proposed adaptive servo system is proved.

Self-tuning servo for stochastic references

In this paper a solution to the problem of tracking an unknown reference signal is proposed. Three-term and error driven mean square optimal controllers are defined. Self-tuning algorithms, providing adaptation to both the process dynamics and the reference signal form are derived from the optimal solution. Their global convergence is proved. Simulation results illustrate their main properties.