Hybrid Decompositional Verification for Discovering Failures in Adaptive Flight Control Systems

The paper addresses initial steps involved in the development and flight implementation of new metrics driven L1 adaptive flight control system. The work concentrates on (i) definition of appropriate control driven metrics that account for the control surface failures; (ii) tailoring recently developed L1 adaptive controller to the design of adaptive flight control systems that explicitly address these metrics in the presence of control surface failures and dynamic changes under adverse flight conditions; (iii) development of a flight control system for implementation of the resulting algorithms onboard of small UAV; and (iv) conducting a comprehensive flight test program that demonstrates performance of the developed adaptive control algorithms in the presence of failures. As the initial milestone the paper concentrates on the adaptive flight system setup and initial efforts addressing the ability of a commercial off-the-shelf autopilot with and without adaptive augmentation to recover from control surface failures.

Robustly Stable, Discrete-time, Adaptive Flight Control using the Euler Operator

The current development process especially of small unmanned aircraft systems from automatic to semi-autonomous or even fully autonomous behaviour demands reliable and precise navigation solutions as well as robust control strategies. For a truly autonomous system adequate as well as secure reactions towards nonlinearities arising for example from unknown environmental conditions or system damages are a prerequisite. An adaptive flight control system, which has to cancel the undesirable effects of such disturbances, depends on constantly available accurate navigation data. To satisfy these boundary conditions, it is sensible to stronger intertwine the development process of miniaturised navigation systems and adaptive flight controllers. As the controller design and its validation is based on nonlinear simulations, it is advisable to model the integrated navigation system and its low-cost components in a detailed way, so that its distinct dynamic properties can already be taken into account during the development process of the controller. In this context, a tightly coupled integrated navigation scheme is presented, where the characteristics of the sensor components are identified and modelled using the Allan variance analysis. This information can be used during the validation process of the proposed adaptive flight control system, which is based on the concept of nonlinear dynamic inversion combined with artificial neural networks. This paper gives an overview on how miniaturised navigation systems can be modelled and utilised for adaptive flight control schemes of small unmanned aircraft.

SummaryIn this study, we present a reinforcement learning (RL)‐based flight control system design method to improve the transient response performance of a closed‐loop reference model (CRM) adaptive control system. The methodology, known as RL‐CRM, relies on the generation of a dynamic adaption strategy by implementing RL on the variable factor in the feedback path gain matrix of the reference model. An actor‐critic RL agent is designed using the performance‐driven reward functions and tracking error observations from the environment. In the training phase, a deep deterministic policy gradient algorithm is utilized to learn the time‐varying adaptation strategy of the design parameter in the reference model feedback gain matrix. The proposed control structure provides the possibility to learn numerous adaptation strategies across a wide range of flight and vehicle conditions instead of being driven by high‐fidelity simulators or flight testing and real flight operations. The performance of the proposed system was evaluated on an identified and verified mathematical model of an agile quadrotor platform. Monte‐Carlo simulations and worst case analysis were also performed over a benchmark helicopter example model. In comparison to the classical model reference adaptive control and CRM‐adaptive control system designs, the proposed RL‐CRM adaptive flight control system design improves the transient response performance on all associated metrics and provides the capability to operate over a wide range of parametric uncertainties.

Adaptive traffic control systems continuously monitor traffic conditions on an arterial and adjust traffic signal timings to accommodate variation in traffic volume and minimize stops and delays. This paper describes currently practical adaptive control systems which have been used in the United States and worldwide, which include Adaptive Control System-Lite (ACS-Lite), Optimization Policies for Adaptive Control (OPAC), Real-Time Hierarchical Optimized Distributed and Effective System (RHODES), Sydney Coordinated Adaptive Traffic System (SCATS) and Split Cycle Offset Optimization (SCOOT). The comparisons of the benefits from past experiences, cost and hardware requirements will help the traffic engineers and decision makers a guideline to select the optimized adaptive control system based on research objectives.

Human Pilot Interaction with Fast Adapting Flight Control System

SummaryAt the present time, there is a great deal of interest in the adaptive control of systems with rapidly varying parameters. Such problems arise in different forms and in many disciplines including finance, sociology, biology, and engineering. In general, these problems are intractable mathematically and the time variations have to be classified in some form to obtain rigorous results. In this paper, we consider the adaptive identification and control of linear systems with periodically varying parameters (referred to as linear time‐varying periodic (LTP) systems). The class of systems with known periodic parameters has been investigated widely since the pioneering work of Floquet and lends itself to rigorous mathematical analysis. However, very little was known until the early years of the last decade concerning the adaptive control of such systems. The problem was introduced by Xu and explored in detail by Narendra and Zhiling in 2006 and 2009, respectively. In 2012, a new method was introduced in adaptive control by Han and Narendra and is referred to as second‐level adaptation. In this paper, the adaptive identification and control of LTP systems using second‐level adaptation is discussed.

Inertial stabilization systems are designed to maintain the angular orientation of an optical system or other device in inertial space. Applications include surveillance, target tracking, flight control, weapon pointing, navigation, communication and others. Typically, some variation of a closed loop control system is employed which uses feedback from a gyro. The object of this paper is to consider the potential benefits and some approaches to adaptive control algorithms. Ideally, an adaptive control system would adjust itself to achieve "optimal performance" with respect to some criteria in the presence of changing plant characteristics, changing noise characteristics and a changing disturbance environment. Potentially, such a design could provide robust control for a variety of system configurations, mission conditions and applications resulting in significant reductions in both development and production costs as well as improved performance. First, the general stabilization problem and some conventional control system designs are briefly reviewed. Then a variety of adaptive techniques are surveyed. Finally, the current status and results of a joint research project between Texas Instruments and the University of Texas at Arlington whose goal is to develop an adaptive stabilization control system is discussed.

Considered influence of nonlinear system and disturbance, a new adaptive fault-tolerant control method based on neural network model-following adaptive inversion control is introduced for flight control system in the presence of control surface damage. To restrain modeling uncertainties caused by fault system, neural network PID and inversion controllers are design for fault-tolerant flight control system. It is based on BP (back-propagation) neural network which is designed by applying improved algorithm of on-line training and parameter optimization. Moreover the output of fault aircraft can perfectly track the trajectory of the reference model. The scheme is demonstrated through simulations applying the flight control system of a fighter. The simulation results show fault-tolerant flight control system maintains stability and handling qualities.

Purpose. The purpose of the article is to develop a methodology for creating cone crusher adaptive control system based on the model-based design method for automated software generation of microprocessor controllers. Methodology. A method based on a block-oriented predictive model was used to generate control signals for the cone crusher. The parameters and structure of this model were identified in real time using measured data from the plant. A prototype of the control system was created in MATLAB/Simulink. Then, the model-based design method was used to generate software for digital signal processors. Mathematical statistics methods were employed to analyze the experimental results. Findings. A method of model-based design of an adaptive control system for a cone crusher has been developed. This system uses a predictive model of a block-oriented structure. This model adjusts the predictive controller’s structure and parameters directly during operation. This approach makes it possible to divide the functions of identifying the process model and generating controls between the two digital controllers. Consequently, the average computational time is reduced while ensuring the stabilization of the degree of homogeneity of ore crushing and separate output of the control size class, with respective standard deviation coefficients not exceeding 3.42 and 1.83 %, respectively. Originality. The regularity of the effect of the closed-side setting and the eccentric speed on the particle size distribution of crushed ore has been established. This shows that high homogeneity of the crushed product is ensured by simultaneously adjusting these input coordinates. We propose a new method for synthesizing an adaptive cone crusher control system based on a model-based design approach. This method provides automated real-time generation of software for microprocessor-based controllers, allowing the system to quickly adjust to changes in rock mass characteristics and other disturbances. Practical value. A hardware and software implementation of an adaptive control system for a cone crusher is proposed. This system is based on a block-oriented predictive model. The model ensures the stabilization of the required ore particle size distribution. This stabilization is achieved by adjusting the closed-side setting and the eccentric speed. The system is based on 16-bit, low-cost digital signal processors. A prototype of the system was tested in a crushing plant at a metallurgical enterprise.

This paper discusses flying objects’ adaptive control with direct application to the flight of helicopters. Two new automatic adaptive control systems are suggested: the former is used for pitch angle control, while the latter is used for control of helicopter pitch angle and velocity; this second system is an extension of the first one. The adaptive control is based on the dynamic inversion principle and the use of neural networks. The two adaptive control systems have reference models, linear dynamic compensators, linear observers, and neural networks. The adaptive components of the automatic control laws compensate for the approximation errors of the dynamic model’s nonlinear functions. The used actuators are linear or nonlinear. To eliminate the neural networks’ adapting difficulties, a pseudo-control hedging (PCH) block is inserted in the adaptive system; it limits the adaptive pseudo-control by means of a component that represents the estimation error of the actuator dynamics. Thus, the PCH ...

Intelligent electrical discharge machining (EDM) molybdenum‑titanium‑zirconium alloy by an extended adaptive control system

An adaptive system for linear systems with unknown parameters is a nonlinear system. The analysis of such adaptive systems requires similar techniques to analyse nonlinear systems. Therefore it is natural to treat adaptive control as a part of nonlinear control systems. Nonlinear and Adaptive Control Systems treats nonlinear control and adaptive control in a unified framework, presenting the major results at a moderate mathematical level, suitable for MSc students and engineers with undergraduate degrees. Topics covered include introduction to nonlinear systems; state space models; describing functions for common nonlinear components; stability theory; feedback linearization; adaptive control; nonlinear observer design; backstepping design; disturbance rejection and output regulation; and control applications, including harmonic estimation and rejection in power distribution systems, observer and control design for circadian rhythms, and discrete-time implementation of continuous-time nonlinear control laws.

A New Approach to the Design of an Adaptive Control System

This paper analyzes the advisability of using the adaptive system on the CTX gamma 2000 TC multi-operation machine to improve the processing efficiency. Since modern multi-operation machines are very expensive, it influences on the cost of machining. Therefore, it is of practical interest to develop and study adaptive control systems that improve the accuracy and productivity of machining. The combined effect of all the elementary errors of the system arising during processing is taken into account. The process of setting the cutting tool to a size is described, the expressions for the optimal tuning size are generalized. To solve the problem of minimizing the influence of these errors on the machining process, a system of adaptive control of limiting regulation was proposed. The principle of the system operation has been described and the structural scheme of the system has been given. As a regulation parameter, the cutting power was used; it being the key factor in the processing efficiency. Due to the adaptive control system, both the efficiency and accuracy of machining were improved through a decrease in depreciation deductions from the machine. The economic feasibility of using such a system was established. In the same way, a method for calculating the adjustment size has been developed, based on the accuracy of machining balance. Thus, the improvement of machine tools due to adaptive control systems of limiting regulation is an actual problem of domestic engineering. In this paper, an adaptive diagram has been drawn up, which makes it possible to compile a universal general equation of the accuracy balance of a technological operation, and to determine the calculated control system for the CTX gamma 2000 TC machine