Underactuated nonlinear adaptive control approach using U-model for multivariable underwater glider control parameters

Air-Handling or Air-handler Units (AHUs) as a section of a Heating, Ventilating, and Air-Conditioning (HVAC) system are used for refrigerating and filtering the environment. AHU provides comfortable conditions by adjusting the control actuators of the air and water in a field-level of industrial automation. This device has strong interacting nonlinearities characteristics with complicated coupling between states and variable parameters. Thus, designing a controller has been always a challenging issue. In this work, a multivariable Nonlinear Model Predictive Controller (NMPC) centered on fmincon algorithm for AHU with a single zone in a Variable Air Volume (VAV) is pro-posed. The NMPC structure is focused on iterative optimization (online) of a cost function with a finite horizon of operating performance and using the future behavior of air-handler to generate the control effort without considering the past error. Simulation results indicate good efficiency of the NMPC approach in estimation and tracking the desired set-point compared to Linear Quadratic Regulator (LQR).

Constrained nonlinear multivariable control of a fluid catalytic cracking process

Autonomous underwater vehicles (AUVs) are an indispensable tool for marine scientists to study the world's oceans. The Slocum glider is a buoyancy driven AUV designed for missions that can last weeks or even months. Although successful, its hardware and layered control architecture is rather limited and difficult to program. Due to limits in its hardware and software infrastructure, the Slocum glider is not able to change its behavior based on sensor readings while underwater. In this paper, we discuss a new programming architecture for AUVs like the Slocum. We present a new model that allows marine scientists to express AUV missions at a higher level of abstraction, leaving low-level software and hardware details to the compiler and runtime system. The Slocum glider is used as an illustration of how our programming architecture can be implemented within an existing system. The Slocum's new framework consists of an event driven, finite state machine model, a corresponding compiler and runtime system, and a hardware platform that interacts with the glider's existing hardware infrastructure. The new programming architecture is able to implement changes in glider behavior in response to sensor readings while submerged. This crucial capability will enable advanced glider behaviors such as underwater communication and swarming. Experimental results based on simulation and actual glider deployments off the coast of New Jersey show the expressiveness and effectiveness of our prototype implementation.

A new multivariable adaptive nonlinear predictive controller is designed using a general nonlinear input-output model and variable tansformations. The controller is similar in form to typical linear predictive controllers and can be tuned analogously or by specifying a single parameter for each controlled variable. In addition, the design procedure is computationally efficient The new controller is compared to a multi-loop PI control system with one-way static decoupling, and to a nonadaptive, nonlinear predictive controller through tests on a simulated nonlinear distillaton column.

The multiple autonomous underwater vehicle (AUV) formation plays an important role in underwater missions, such as oceanographic sampling and water pollution monitoring. This article presents the mechatronic design, modeling, formation control, and experiments of multiple AUVs. The structure of the AUV and a simplified mathematical model for tracking control are described. To achieve formation control, we formulate a control framework for the multiple AUVs. The upper layer is a formation algorithm based on a novel leader-follower control law. The bottom layer is a dynamic controller based on active disturbance rejection control (ADRC). The formation algorithm is in charge of calculating reference values for the followers to maintain a desired pattern with the leader. The stability and convergence properties of the algorithm have been analyzed using the Lyapunov stability method. Meanwhile, an ADRC approach-based dynamic controller is established to track the reference values. Numerical simulations are carried out to analyze formation control and validate the control framework. The multiple AUVs can switch and maintain the formation between the one-line pattern and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> pattern. Finally, extensive formation field experiments involving the one-line pattern and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> pattern show the good motion ability of the self-designed AUVs and also verify the feasibility of the proposed control approach. Note to Practitioners—The motivation of the article is to design a practical formation control approach for multiple AUVs and verify the control approach in the field. Although there have been a lot of prior research studies on multiple AUVs, how to design a formation control approach subjected to communication bandwidth constraints and how to develop multiple AUVs and verify the effectiveness of the control method in the field are worthy of intense investigation. Hence, this article builds the mechatronic design and dynamic model of the AUV and proposes a novel leader-follower formation control approach based on the dynamics and kinematic model of AUV. Besides, the stability of formation control for multiple AUVs is proven by the Lyapunov theorem. Multiple AUVs can switch and maintain formation between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> pattern and one-line pattern with a smaller error. Finally, the performance of the proposed formation control strategy is experimentally verified using three self-made AUVs inside a large reservoir. The proposed method is suitable for multiple AUVs missions involving underwater surveillance, underwater pipeline inspection in the ocean.

This paper focuses on developing a data-driven trajectory tracking control approach for autonomous underwater vehicles (AUV) under uncertain external disturbance and time-delay. A novel model-free adaptive predictive control (MFAPC) approach based on a fuzzy state observer (FSO) was designed to achieve high precision. Concretely, the mathematical model of AUV motion was analyzed, and simplified via model decoupling, thus providing the model basis with an explicit physical explanation for the controller. Second, the MFAPC scheme for a multiple-inputs and multiple-outputs (MIMO) discrete time system was derived, that estimates system external disturbance. The controller can online estimate and predictive time-varying parameter pseudo-Jacobian matrix (PJM) to establish equivalent state space data-model for AUV motion system. Third, the Takagi–Sugeno (T–S) fuzzy model based state observer was designed to combine with the MFAPC scheme for the first time, which was used to online decline the state error generated by system uncertain time-delay. In addition, the stability of the proposed control scheme was analyzed. Finally, two trajectory tracking scenarios were designed to verify the effectiveness and robustness of the proposed FMFAPC scheme, and the simulations are implemented using the realistic parameters of T-SEA AUV.

The control problem of the single machine infinite bus system with TCSC is dealt with. Based on the maximization of the external disturbances on the system model, an adaptive nonlinear controller for large disturbance attenuation and a parameter updating law are designed by using the backstepping method. The parameter uncertainty of the transmission line is considered, as well as the influences of large external disturbances to the system output are mainly discussed. The nonlinear controller does not have the sensitivity to the influences of external disturbances, but also has strong robustness for system parameters variation. The simulation results show that the control effect of the large disturbance attenuation controller more advantages by comparing with the control performance of conventional nonlinear robust controller.

A new method is presented for the resolution of the problem of input saturation in nonlinear multivariable process control by means of elementary nonlinear decoupling (END). Input saturation can have serious consequences particularly in multivariable control because it may lead to very undesirable system behaviour and quite often system instability. Many authors have searched for systematic techniques for designing multivariable control systems in which saturation may occur in any of the control variables (inputs, manipulated variables). No generally accepted method seems to have been presented so far which gives a solution in closed form. The method of elementary nonlinear decoupling (END) can be applied directly to the case of saturation control variables by deriving as many control strategies as there are combinations of saturating control variables. The method is demonstrated by the multivariable control of a simulated Fluidized Catalytic Cracker (FCC) with very convincing results

Robust Nonlinear EGR and VGT Control with Integral Action for Diesel Engines

In this chapter an adaptive sliding mode controller for the Furuta pendulum is proposed. The Furuta pendulum is a class of underactuated mechanical systems commonly used in many control systems laboratories due to its complex stabilization which allows the analysis and design of different nonlinear and multivariable controllers that are useful in some fields such as aerospace and robotics. Sliding mode control has been extensively used in the control of mechanical systems as an alternative to other nonlinear control strategies such as backstepping, passivity based control etc. The design and implementation of an adaptive sliding mode controller for this kind of system is explained in this chapter, along with other sliding mode controller variations such as second order sliding mode (SOSMC) and PD plus sliding mode control (PD \(+\) SMC) in order to compare their performance under different system conditions. These control techniques are developed using the Lyapunov stability theorem and the variable structure design procedure to obtain asymptotically stable system trajectories. In this chapter the adaptive sliding mode consist of a sliding mode control law with an adaptive gain that makes the controller more flexible and reliable than other sliding mode control (SMC) algorithms and nonlinear control strategies. The adaptive sliding mode control (ASMC) of the Furuta pendulum, and the other SMC strategies shown in this chapter, are derived according to the Furuta’s pendulum dynamic equations making the sliding variables, position errors and velocity errors reach the zero value in a specified reaching time. The main reason of deriving two well known sliding mode control strategy apart from the proposed control strategy of this chapter (adaptive sliding mode control) is for comparison purposes and to evince the advantages and disadvantages of adaptive sliding mode control over other sliding mode control strategies for the stabilization of the Furuta pendulum. A chattering analysis of the three SMC variations is done, to examine the response of the system, and to test the performance of the ASMC in comparison with the other control strategies explained in this chapter.

PurposeThe purpose of this paper is to develop a new easy-to-implement distribution-free integrated multivariate statistical process control (MSPC) approach with an ability to recognize out-of-control points, identify the key influential variable for the out-of-control state, and determine necessary changes to achieve the state of statistical control.Design/methodology/approachThe proposed approach integrates the control chart technique, the Mahalanobis-Taguchi System concept, the Andrews function plot, and nonlinear optimization for multivariate process control. Mahalanobis distance, Taguchi’s orthogonal array, and the main effect plot concept are used to identify the key influential variable responsible for the out-of-control situation. The Andrews function plot and nonlinear optimization help to identify direction and necessary correction to regain the state of statistical control. Finally, two different real life case studies illustrate the suitability of the approach.FindingsThe case studies illustrate the potential of the proposed integrated multivariate process control approach for easy implementation in varied manufacturing and process industries. In addition, the case studies also reveal that the multivariate out-of-control state is primarily contributed by a single influential variable.Research limitations/implicationsThe approach is limited to the situation in which a single influential variable contributes to out-of-control situation. The number and type of cases used are also limited and thus generalization may not be debated. Further research is necessary with varied case situations to refine the approach and prove its extensive applicability.Practical implicationsThe proposed approach does not require multivariate normality assumption and thus provides greater flexibility for the industry practitioners. The approach is also easy to implement and requires minimal programming effort. A simple application Microsoft Excel is suitable for online implementation of this approach.Originality/valueThe key steps of the MSPC approach are identifying the out-of-control point, diagnosing the out-of-control point, identifying the “influential” variable responsible for the out-of-control state, and determining the necessary direction and the amount of adjustment required to achieve the state of control. Most of the approaches reported in open literature are focused only until identifying influencing variable, with many restrictive assumptions. This paper addresses all key steps in a single integrated distribution-free approach, which is easy to implement in real time.

The underwater glider (UG) is a cost-effective and high-endurance autonomous underwater vehicle (AUV) for marine research. Powered by buoyancy adjustment, UGs are sensitive to ocean currents and must navigate through complex seabed topography. This study proposes an improved path-planning algorithm for UGs by integrating the Voronoi diagram and the D* algorithm (UG-VD*). Compared to traditional D* algorithms, UG-VD* generates shorter paths, uses favorable currents to enhance efficiency, and avoids obstacles, ensuring better safety performance. The Petrel-L completed a successful sea trial from the Mariana Trench to the northern South China Sea, covering 5506 km and navigating complex terrains, such as the Luzon Strait and the Kuroshio Current using the UG-VD* algorithm. The algorithm’s effectiveness and safety were evaluated using the sea trial data. Furthermore, the proposed algorithm can be applied to other marine environments for the path planning of UGs.

This article develops a novel event-triggered sliding mode control (ETSMC) approach with variable threshold to deal with trajectory tracking matters of autonomous underwater vehicles (AUVs) accompanied by actuator saturation and external disturbances, which can effectively reduce the communication burden between the controller and actuator. The proposed scheme will be very practical when some extreme situations occur. First, the closed-loop system is split into two parts: fixed terms determined by the system itself and nonlinear terms caused by uncertain factors. The nonlinear terms are estimated through adaptive technique. Then the apposite event-triggered mechanism, adaptive laws, and modeled actuator saturation characteristics are designed. The correctness of the presented scheme is illustrated via the stability analysis in the sequel, and the Zeno phenomenon is certificated to be excluded simultaneously. Finally, two different reference motion trajectories are adopted in the simulation experiments, which can indicate that the proposed ETSMC possesses performance superiority and only requires to consume a small amount of communication resources in trajectory tracking control of AUVs. Significance Statement Through the research of this article, we propose a novel event-triggered sliding mode control method with variable threshold applied to autonomous underwater vehicles (AUVs). When conducting ocean exploration work, we usually need the AUVs to follow particular trajectories. By using the proposed method, it can greatly reduce the loss of communication resources inside the system.

The ITER tokamak, the next experimental step in the development of nuclear fusion reactors, will explore the burning plasma regime in which temperature is sustained mostly by fusion heating. Control of the fusion power through modulation of fueling and heating sources, referred to as burn control, will be essential for achieving and maintaining desired operating points and ensuring stability. We utilize a spatially averaged nonlinear transport model to design a multi-variable nonlinear burn control strategy that can reject large perturbations and move between operating points. The controller uses the available actuation techniques in tandem to ensure good performance, even if one or more of the actuators saturate. We propose the use of a model-based optimization scheme to drive the system to a state that minimizes a given cost function. A simulation study shows the performance of the control scheme with a cost function weighting fusion power and temperature tracking errors.

This work proposes a control configuration and a nonlinear multivariable model‐based feedback controller for the reduction of thermal gradients inside the crystal in the Czochralski crystal growth process after the crystal radius has reached its final value. Initially, a mathematical model which describes the evolution of the temperature inside the crystal in the radial and axial directions and accounts for radiative heat exchange between the crystal and its surroundings and motion of the crystal boundary is derived from first principles. This model is numericully solved using Galerkin's method and the behaviour of the crystal temperature is studied to obtain valuable insights which lead to the precise formulation of the control problem, the design of a new control configuration for the reduction of thermal gradients inside the crystal and the derivation of a simplified 1‐D in a space dynamic model. Then, a model reduction procedure for partial differential equation systems with time‐dependent spatial domains (Armaou and Christofides, 1999) based on a combination of Galerkin's method with approximate inertial manifolds is used to construct a fourth‐order model that describes the dominant thermal dynamics of the Czochralski process. This low‐order model is employed for the synthesis of a fourth‐order nonlinear multivariable controller that can be readily implemented in practice. The proposed control scheme is successfully implemented on a Czochralski process used to produce a 0.7 m long silicon crystal with a radius of 0.05 m and is shown to significantly reduce the axial and radial thermal gradients inside the crystal. The robustness of the proposed controller with respect to model uncertainty is demonstrated through simulations.