An adaptive predictive control strategy based on Laguerre function sequence for chopper cascade speed control system

This paper proposes a new adaptive fuzzy nonlinear control strategy that allows designers to systematically construct fuzzy control. The control strategy proposes the use of fuzzy logic systems within a well understood control structure that is called nonlinear internal model control (NIMC) structure. The attractive feature of the NIMC structure is that the relations between some designed parameters and the performance of the control system can be found explicitly. Thus, this control structure allows designers to systematically construct fuzzy control. In addition to using fuzzy logic systems within a well understood control structure, an adaptive control strategy is also proposed in this paper. The control strategy is applied to control the inverted pendulum model. The results show that the proposed strategy can maintain the system stability and give a good control performance.

High share of energy consumption in buildings and subsequent increase in greenhouse gas emissions along with stricter legislations have motivated researchers to look for sustainable solutions in order to reduce energy consumption by using alternative renewable energy resources and improving the efficiency in this sector. Today, the smart building and socially resilient city concepts have been introduced where building automation technologies are implemented to manage and control the energy generation/consumption/storage. Building automation and control systems can be roughly classified into traditional and advanced control strategies. Traditional strategies are not a viable choice for more sophisticated features required in smart buildings. The main focus of this paper is to review advanced control strategies and their impact on buildings and technical systems with respect to energy/cost saving. These strategies should be predictive/responsive/adaptive against weather, user, grid and thermal mass. In this context, special attention is paid to model predictive control and adaptive control strategies. Although model predictive control is the most common type used in buildings, it is not well suited for systems consisting of uncertainties and unpredictable data. Thus, adaptive predictive control strategies are being developed to address these shortcomings. Despite great progress in this field, the quantified results of these strategies reported in literature showed a high level of inconsistency. This is due to the application of different control modes, various boundary conditions, hypotheses, fields of application, and type of energy consumption in different studies. Thus, this review assesses the implementations and configurations of advanced control solutions and highlights research gaps in this field that need further investigations.

Nonlinear Adaptive Control for Turning Processes

Recently, transformable Unmanned Aerial Vehicles (UAVs) have become a subject of great interest in the field of flying systems, due to their maneuverability, agility and morphological capacities. They can be used for specific missions and in more congested spaces. Moreover, this novel class of UAVs is considered as a viable solution for providing flying robots with specific and versatile functionalities. In this paper, we propose (i) a new design of a transformable quadrotor with (ii) generic modeling and (iii) adaptive control strategy. The proposed UAV is able to change its flight configuration by rotating its four arms independently around a central body, thanks to its adaptive geometry. To simplify and lighten the prototype, a simple mechanism with a light mechanical structure is proposed. Since the Center of Gravity (CoG) of the UAV moves according to the desired morphology of the system, a variation of the inertia and the allocation matrix occurs instantly. These dynamics parameters play an important role in the system control and its stability, representing a key difference compared with the classic quadrotor. Thus, a new generic model is developed, taking into account all these variations together with aerodynamic effects. To validate this model and ensure the stability of the designed UAV, an adaptive backstepping control strategy based on the change in the flight configuration is applied. MATLAB simulations are provided to evaluate and illustrate the performance and efficiency of the proposed controller. Finally, some experimental tests are presented.

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

The purpose of this chapter is to redesign the standard adaptive control schemes by using hybrid structure composed by Model Reference Adaptive Control (MRAC) or Adaptive Pole Placement Control (APPC) strategies, associated to Variable Structure (VS) schemes for achieving non-standard robust adaptive control strategies. The both control strategies is now on named VS-MRAC and VS-APPC. We start with the theoretical base of standard control strategies APPC and MRAC, discussing their structures, as how their parameters are identified by adaptive observers and their robustness properties for guaranteeing their stability. After that, we introduce the sliding mode control (variable structure) in each control scheme for simplifying their design procedure. These design procedure are based on stability analysis of each hybrid robust control scheme. With the definition of both hybrid control strategies, it is analyzed their behavior when controlling system plants with unmodeled disturbances and parameter variation. It is established how the adaptive laws compensates these unmodeled dynamics. Furthermore, by using simple systems examples it is realized a comparison study between the hybrid structures VS-APPC and VSMRAC and the standard schemes APPC and MRAC. As the hybrid structures use switching laws due to the sliding mode scheme, the effect of chattering is analyzed on the implementation and consequently effects on the digital control hardware where sampling times are limiting factor. For reducing these drawbacks it is also discussed possibilities which kind of modifications can employ. Finally, some practical considerations are discussed on an implementation on motor drive systems.

Compactly supported radial basis functions for adaptive process control

An Adaptive Predictive Control Strategy for RMPPT under Partially Shaded Conditions

With the increase of on-orbit maintenance and support requirements, the application of a space manipulator is becoming more promising. In actual operation, the strong coupling of the free-floating space robot itself and the unknown disturbance of the contact target caused a major challenge to the robot base posture control. Traditional Reaction Null Space (RNS) motion planning and control methods require the construction of precise dynamic models, which is impossible in reality. In order to solve this problem, this paper proposes a new Adaptive Reaction Null Space (ARNS) path planning and control strategy for the contact of free-floating space robots with unknown targets. The ARNS path planning strategy is constructed by the Variable Forgetting Factor Recursive Least Squares (VFF–RLS) algorithm. At the same time, a robust adaptive control strategy based on the Strategy Self-Adaption Differential Evolution–Extreme Learning Machine (SSADE–ELM) algorithm is proposed to track the dynamic changes of the planned path. The algorithm enables us to intelligently learn and compensate for the unknown disturbance. Then, this paper constructs a robust controller to compensate model uncertainty. A striking feature of the proposed strategy is that it does not require an accurate system model or any information about unknown attributes. This design can dynamically implement RNS path tracking performance. Finally, through simulation and experiment, the proposed algorithm is compared with the existing methods to prove its effectiveness and superiority.

The article describes three strategies for adaptive control of cooperative dual‐arm robots. In the position‐position control strategy, the adaptive controllers ensure that the end‐effector positions of both arms track desired trajectories in Cartesian space despite unknown time‐varying interaction forces exerted through the load. In the position‐hybrid control strategy, the adaptive controller of one arm controls end‐effector motions in the free directions and applied forces in the constraint directions; while the adaptive controller of the other arm ensures that the end‐effector tracks desired position trajectories. In the hybrid‐hybrid control strategy, the adaptive controllers ensure that both end‐effectors track reference position trajectories while simultaneously applying desired forces on the load. In all three control strategies, the coupling effects between the arms through the load are treated as “disturbances” which are rejected by the adaptive controllers while following desired commands in a common frame of reference. The adaptive controllers do not require the complex mathematical model of the arm dynamics or any knowledge of the arm dynamic parameters or the load parameters such as mass and stiffness. The controllers have simple structures and are computationally fast for on‐line implementation with high sampling rates. Simulation results are given to illustrate the proposed adaptive control strategies.

This paper studies benefits of using simplified re-gime-switching adaptive control strategies in improving performance of insulin control for Type I diabetic patients. Typical dynamic models of glucose levels in diabetic patients are nonlinear. Using a linear time invariant controller based on an operating condition is a common method to simplify control design. On the other hand, adaptive control can potentially improve system performance, but it increases control complexity and may create further stability issues. This paper investigates patient models and presents a simplified switching control scheme using PID controllers. By comparing different switching schemes, it shows that switched PID controllers can improve performance, but frequent switching of controllers is unnecessary. These findings lead to a control strategy that utilizes only a small number of PID controllers in this scheduled adaptation strategy.

PACC: A platoon-based adaptive cruise control strategy based on leader-following information topology to mitigate traffic oscillations under CAV environment

A nonlinear adaptive control strategy is derived from the biological control system that regulates arterial blood pressure. The parallel control system consists of a linear controller and a nonlinear controller that interact via a reciprocal lateral inhibitory mechanism. The linear controller design is based on an empirical linear model, while the nonlinear controller is constructed from locally supported radial basis functions that are updated on-line using a model reference adaptive control technique. The control strategy is evaluated using a simple model of a pH neutralization system.

Adaptive control of automotive electronic throttle

With the continuous development of vehicle intelligence technology and active safety technology, the autonomous emergency braking pedestrian (AEB-P) system has emerged as a crucial element of contemporary automotive active safety systems. However, there are still some deficiencies in the control strategies utilized by the current AEB-P system. It is imperative to design an AEB-P system that can adapt the safety threshold and control strategy in real time based on the current driving conditions. The objective of this paper is to propose an AEB-P system with an adaptive control strategy and analyse its effectiveness from the perspective of pedestrian head injuries. Initially, an algorithm for adaptive collision time threshold model is proposed, and the AEB-P control strategy integrating this threshold is designed. Subsequently, a finite element model of the human head is developed to predict pedestrian head injuries in pedestrian-vehicle collision (PVC) scenarios with AEB-P intervention. Additionally, a simplified finite element model of PVC is established, and real PVC scenarios are reconstructed to assess the effectiveness of various AEB-P control strategies in mitigating pedestrian head injuries. The results demonstrate that the conventional control strategy is ineffective in reducing the risk of pedestrian head injuries at medium to high speeds. Conversely, the AEB-P system, which incorporates an adaptive control strategy, proves to be effective in decreasing the risk of pedestrian head injuries. Overall, the AEB-P system with adaptive control strategy enhances pedestrian safety, particularly in scenarios where PVCs are inevitable.