Application of an Adaptive Pole-Zero Placement Controller to Chemical Processes with Variable Dead Time

Discrete Time Multivariable Adaptive Control for Non-Minimum Phase Plants with Unknown Dead Time

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.

This paper presents a zero voltage switching (ZVS) inverter and the corresponding modulation scheme, which uses variable and fixed switching frequency operation during the sinusoidal period to limit the switching frequency range. It is suitable for generating sinusoidal output waveforms and ensuring lossless switching over the entire fundamental period. ZVS requires a sufficiently long dead time during both switches are turned off. Typically, standard modulation schemes apply a fixed dead time. First experiments operate the ZVS inverter with a fixed dead time. Despite achieving lossless switching, unexpected high device temperatures of the Gallium Nitride half-bridge switches occur. The origin of these high temperatures need to be figured out in order to minimize the losses of the power stage. A study shows that significant reverse conduction losses occur during the half-bridge’s dead time. Thus, this paper introduces an adaptive dead time method, which shows its effectiveness at an experimental setup by reducing the semiconductor’s device case temperature by 10 K. Since the computational effort for this optimal procedure is relatively high, the paper finally presents a simplified, computationally less costly variant that achieves almost the same improvements regarding the device temperatures.

An Adaptive Feedforward Control Algorithm for Computer Control of Wastewater Neutralization

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.

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.

The model reference adaptive control approach is used here for the design of an adaptive control system for force control in end milling operations by feedrate manipulation. The expected benefit from such a control system is more efficient material removal. The proposed adaptive control system uses an on-line cutting process parameter estimation scheme which has performed successfully on experimental cuts. The controller form and adaptation mechanism are derived using MRAC formulation. The proposed adaptive controller is evaluated using a previously validated simulation of the end milling process. It performs force control more effectively and is stable over a wider range of cutting conditions as compared to a conventional fixed-gain proportional - integral controller, based on simulation.

Tuning of proportional integral and derivative (PID) controllers using an optimization method to minimize integral time weighted absolute error is proposed. It will use differential evolution technique for various types of integrating systems. The processes considered in the paper include a pure integrator with dead time, a stable/unstable first-order plus time delay (FOPTD) system with integrator with/without a zero, pure double integrating systems with time delay and a stable FOPTD system with a double integrator. The zero may be positive or negative. The performance of the PID controller designed by the proposed method is compared with the recently reported methods. The stability and robustness of the controller are reported and compared with the recently reported methods. Simulation studies on various transfer function models are given to show the effectiveness of the proposed controller for perfect model parameters and uncertainty in model parameters.

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.

A low-complexity adaptive tracking control strategy for a class of pure feedback nonlinear systems is developed in the presence of completely unknown non-affine nonlinearities, prescribed tracking performance constraints, full states constraints, and input saturation constraint. To handle the input saturation nonlinearity and the constrained states without pre-calculating the bound of virtual controllers, a new coordinate transformation approach is presented with an arc-tangent function and an auxiliary first-order dynamics. Subsequently, to deal with the completely unknown non-affine nonlinearities and the problem of prescribed performance, an inverse hyperbolic tangent function and a Nussbaum function are introduced at each step of back-stepping design, which can guarantee the control errors to vary with prescribed performance functions in time and avoid the “explosion of complexity” issue simultaneously. Then, an adaptive controller is derived from the recursive design procedure without the use of any approximators. The semi-globally uniformly ultimate boundedness of all signals in the closed-loop system and the satisfaction of all constraints are rigorously proved through Lyapunov stability analysis. Finally, the simulation studies on a hypersonic flight vehicle system are worked out to demonstrate the effectiveness and the applicability of the investigated approach.

In this paper, we discuss the application of a novel adaptive reset controller to the GTM to improve a transient performance in addition to remedy the uncertainties present in the system. A reset controller is a linear controller that resets some of its states to zero when its input is zero. On the other hand, an adaptive controller is a nonlinear controller that automatically changes its gains to maintain stability even with uncertainties present in the system. Borrowing some of the key ideas from the reset control, this paper addresses the impact of introducing a resetting scheme into an adaptive controller on stability and performance improvement. An adaptive controller with an integral action and a resetting strategy is proposed to address the uncertainties. Two dierent algorithms are proposed to carry out the resetting. In each case, we show that the controllers lead to improved system performance, as well as that the stability of the closed-loop system is guaranteed. The controllers are then evaluated for command following with the NASA Langley Generic Transport Model (GTM) is discussed. Simulations of a high-delity GTM simulink model are carried out to validate the proposed adaptive controllers. The results show that the adaptive reset controller leads to a signicantly improved performance compared to a nonadaptive controller and to an adaptive controller without any resetting strategies.

A fully actuated system approach: Desired compensation adaptive robust control for uncertain nonlinear systems

This paper concerns the problem of adaptive compensation tracking control for a class of time-varying delay nonlinear systems with unknown structures and unknown actuator dead zones where time-varying delays are unknown. First, a variable separation approach is used to overcome the difficulty in dealing with a nonstrict-feedback structure, and multilayer neural networks are used to approximate the unknown nonlinear structures with time-varying delays. On this basis, we designed an adaptive multilayer neural-network compensation controller to reduce the error of multilayer neural networks. Furthermore, for unknown actuator dead zones, this paper separates the controller and adopts multilayer neural networks to deal with unknown actuator dead zones. In order to reduce the error of the dead-zone controller, wer designed an adaptive compensation controller for the dead zones. Lastly, this paper proves the stability of the systems with the Lyapunov method, and simulation results demonstrate the effectiveness of the scheme.

Adaptive Output Feedback Control System Design with Adaptive PFC for Combustion Control of Diesel Engine

High-order systems are commonly encountered in production processes. Due to the uncertainty and load disturbances, the performance of the systems which are designed with reduced approximated model are often not satisfactory. In this paper, a reduced-order model plus dead time is used to approximate high-order systems and to compensate the inertial lag. Based on the Popov hyperstability theorem, an adaptive mechanism is designed to minimize the broad sense predictive output error. Simulation results show that the adaptive reduced-order model has good ability to auto-approximate high-order systems and to compensate the inertial lag. Based on the adaptive dead-time compensation, an H/sub /spl infin// robust controller is designed for high-order systems. Control simulation results prove that the H/sub /spl infin// robust control system, which is described by the adaptive low-order model with dead time, has good robust stability and dynamical performance.