Superior Control Performance Research Articles

Design of a robust optimal decentralized PI controller based on nonlinear constraint optimization for level regulation: an experimental study

This paper presents the development of a new robust optimal decentralized PI controller based on nonlinear optimization for liquid level control in a coupled tank system. The proposed controller maximizes the closed-loop bandwidth for specified gain and phase margins, with constraints on the overshoot ratio to achieve both closed-loop performance and robustness. In the proposed work, a frequency response fitting model reduction technique is initially employed to obtain a first order plus dead time &#x0028 FOPDT &#x0029 model of each higher order subsystem. Furthermore, based on the reduced order model, a proposed controller is designed. The stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainties. The performance of the proposed optimal robust decentralized control scheme has been compared with that of a decentralized PI controller. The proposed controller is implemented in real-time on a coupled tank system. From the obtained results, it is shown that the proposed optimal decentralized PI controller exhibits superior control performance to maintain the desired level, for both the nominal as well as the perturbed case as compared to a decentralized PI controller.

An Enhanced Coupling PD with Sliding Mode Control Method for Underactuated Double-pendulum Overhead Crane Systems

An enhanced coupling PD with sliding mode control method, called ECPD-SMC in short, is presented for double-pendulum overhead crane systems in this paper. The proposed method replaces the PD controller with the equivalent part of traditional SMC method, without any system parameters. The ECPD-SMC algorithm is composed of PD control part and the SMC control part. The SMC part is utilized for constructing the frame of a controller, providing strong robustness with uncertain model, different system parameters, and external disturbances. The PD control part is used to stabilize the control system. Moreover, the coupling behavior between the trolley movement and the payload swing is enhanced, and hence, leads to an improved control performance. As shown by Lyapunov techniques and Schur complement, the proposed ECPD-SMC guarantees asymptotic result even in the presence of uncertainties, including model, system parameters, and various disturbances. Some simulation results are included to demonstrate the correctness and superior control performance of the designed controller.

Harmonic Analysis and Suppression Method of Output Current Distortion for Medium-Voltage Motor Drives With Modular Multilevel Converter

The main technical challenge of the medium-voltage motor drives with modular multilevel converter (MMC) is the serious low-frequency fluctuation on the capacitor voltages, and the existing suppression methods inevitably inject high-frequency circulating currents in MMC. However, the output currents are evidently distorted by the large injected components, while little research has been done to study and solve this problem. This paper analyzes and proves the essential interactions between the output current distortion and the injected components, and the specific dominant output harmonic groups are derived and summarized, revealing the nature of the problem, and it can be taken as a favorable reference for the controller designing. Based on the derivations, this paper proposes a novel suppression method for the greatly improved harmonic characteristics of the output currents during the low-frequency operation. The output harmonics can be accurately measured by the proposed control and effectively suppressed for much lower current total harmonic distortion, benefiting for the superior output control performances with improved efficiency. The simulation results on PSCAD/EMTDC and experimental verifications on a 2.2 kW MMC-based motor drive prototype prove the validity of the presented harmonic analysis and suppression method.

Design of Dynamic Automatic Control System for Mariculture Water Quality under Internet of Things Environment

Zhang, S. and Chen, J., 2018. Design of dynamic automatic control system for mariculture water quality under internet of things environment. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 637–641. Coconut Creek (Florida), ISSN 0749-0208.The dynamic automatic control system of mariculture water quality was proposed, the main control processor of system used Mini2440 development board. The data transmission module used ZigBee wireless communication module and USB WIFI module to complete the data transmission network, which was forwarded by the Zigbee router. The control part communicated through the communication module and the control cabinet of aquaculture pond site, so as to realize the remote control and dynamic automatic control for farming equipment. Users could complete the remote access through the monitoring platform. Experimental result shows that the system can effectively improve the level of dynamic management of mariculture water quality. It has superior control performance.

Robust fixed-time sliding mode control for fractional-order nonlinear hydro-turbine governing system

This paper proposes a nonlinear sliding mode controller for effective fixed-time stabilization of a hydro-turbine governing system (HTGS). The HTGS is a highly nonlinear, multi-variable coupled and non-minimum phase system to maintain frequency stability by regulating the generation output in response to load variations. The state trajectories of HTGS under random load disturbances exhibit unstable behaviours on the rotor angle, rotor speed, and guide vane opening, which would affect the stable operation of hydroelectric stations. In the proposed approach, the fractional calculus technique is adopted to model the nonlinear dynamics of fractional-order hydro-turbine governing system (FOHTGS). In order to validate the system stabilization and convergence within a bounded time, a robust sliding mode controller is proposed to force the FOHTGS to reach the equilibrium point based on the fixed-time stability and Lyapunov stability theories. The proposed controller can guarantee the superior stabilization of FOHTGS with the bounded and quantifiable convergence time, and thus overcome the drawback of finite-time controllers whose convergence characteristics are heavily dependent on the initial conditions. Finally, numerical simulations have been implemented to confirm the effectiveness and superior performance of the fixed-time controller compared with the existing finite-time control methods.

Pattern-based NN control for uncertain pure-feedback nonlinear systems

Implementing human-like learning and control for nonlinear dynamical systems operating in different control situations is an important and challenging issue. This paper presents a pattern-based neural network (NN) control strategy for nonlinear pure-feedback systems via deterministic learning (DL). Firstly, an appropriately designed adaptive neural dynamic surface controller is proposed to achieve the finite time tracking control. By analyzing the recurrent property of NN input signals, a partial persistent excitation (PE) condition for radial basis function (RBF) network is established, the implicit desired control dynamics under different control situations are accurately identified via DL in the case that the dimension of NN input is reduced. And a set of pattern-based experienced actual and virtual controllers is constructed using the learned knowledge. Secondly, to classify different control situations, when the system is operating in different control situations but controlled by current normal experienced controller, the dynamics of each subsystem are accurately identified via DL, n sets of dynamical estimators are constructed using the learned knowledge. Thirdly, in the recognition phase, n sets of residuals are achieved by comparing each set of estimators with the monitored system, sudden change in the control situation is rapidly recognized based on the principle of the earliest occurrence of the minimum residual. Finally, in the control phase, according to the recognition result, the correct experienced actual and virtual controllers will be selected to control the plant, guaranteed stability and superior control performance are achieved without any further re-adaptation online. Simulation studies are given to verify the proposed scheme can not only acquire and memorize knowledge like humans, but also reuse the learned knowledge to achieve rapid recognition and control of current control situation.

New hybrid optimal controller applied to a vibration control system subjected to severe disturbances

Abstract This study presents a new hybrid optimal controller to enhance the robustness and stability of dynamic systems subjected to uncertainties and external disturbances. In the formulation of the proposed control system, the Bolza–Meyer (BM in short) criterion of the optimal controller is modified to meet the system variation, and the sliding-mode controller is modified to obtain the classical control property with the prescribed performance. The proposed controller also includes the H-infinity technique as a bridge for connecting the sub-controls and improving the robust performance of the system. Fuzzy model are used as filters to choose the optimal values for the next calculation. Hence, many advantages of fuzzy model are acquired, related to optimal control, sliding mode control, prescribed performance, and H-infinity techniques. To demonstrate these advantages, the proposed hybrid controller is applied to a vehicle seat suspension for vibration control. Superior control performance over existing hybrid controllers is demonstrated in both time and frequency domains.

A Novel Energy-Coupling-Based Hierarchical Control Approach for Unmanned Quadrotor Transportation Systems

Cable-suspended transportation is an important way of transferring goods and materials by rotorcraft in complex and hazardous environments, where external disturbances, system uncertainties, and, especially, the underactuated characteristic bring great challenges to realize safe and smooth deliveries. In terms of the aforementioned problems, this paper presents an enhanced coupling hierarchical control scheme for both quadrotor positioning and payload swing elimination. By exploiting the cascade property of quadrotor transportation systems, we can design controllers for the inner loop and the outer one separately, provided that the growth restriction condition is well satisfied, which greatly simplifies the design procedure. As the outer loop describes the quadrotor translation and payload swing motion, our central work focuses on the controller design of this subsystem. Specifically, a generalized payload signal is introduced to increase the state coupling, based on which we construct a novel energy storage function. Consequently, an energy coupling control law is proposed, which ensures that the equilibrium point of the system is asymptotically stable by using Lyapunov techniques and LaSalle's invariance theorem. Experimental results are presented to illustrate the superior control performance, even in the presence of external disturbances and parameter uncertainties.

Discrete-time adaptive iterative learning control with incorporation of a priori process knowledge

This work explores utilization of a priori model knowledge to achieve better control performance of iterative learning control systems. Two specific discrete-time adaptive iterative learning control approaches are proposed for different control cases to exactly utilize known information in order to improve control performance. In more detail, a novel time-difference estimator based discrete-time adaptive iterative learning control system is proposed for a time-invariant parametric uncertain system, and a mixed-difference estimator based discrete-time adaptive iterative learning control is proposed for a hybrid parametric uncertain system. Both of these control methods are shown to perform well even though the initial states and the reference trajectories vary randomly along the iteration direction. We show that superior control performance can be achieved compared with the existing iteration-difference estimator based discrete-time adaptive iterative learning control systems by applying the two proposed control methods, which fully utilize known information about the model. Mathematical proof and a simulation study on a permanent magnet linear motor verify the efficiency of the proposed discrete-time adaptive iterative learning control approaches.

Application of explicit model predictive control to a vehicle semi-active suspension system

The vehicle semi-active suspension is a typical multiple-input multiple-output system with strong couplings, actuator constraints and fast dynamics. This paper addresses the damping force regulation of shock-absorber in vehicle semi-active suspensions using an explicit model predictive control (EMPC) approach, which allows minimizing the system control objective function while satisfying the actuator constraints. The main advantage of the proposed approach is that the control law computation requirement is low, and thus the EMPC system is suitable for implementation in a standard automotive microcontroller. The design of the EMPC system consists of mathematical modeling, objective function determination, controller formulation and simulation validation. Presented simulation results verify that a superior control performance of the vehicle semi-active suspension system is achieved by the proposed EMPC control approach compared with the performance obtained using conventional control method.

Enhanced-coupling adaptive control for double-pendulum overhead cranes with payload hoisting and lowering

Overhead cranes, which have been extensively studied, are mostly simplified as single pendulums. However, in practice, the existence of lifting hook usually makes the crane present double-pendulum swing, i.e., hook swing with respect to the trolley and payload swing with respect to the hook. Therefore, a severe gap between theory and practice is generated. Due to this fact, lots of researchers are now working on the automation for double-pendulum overhead cranes (DPOC). However, with an extra unactuated degree of freedom, the control of DPOC is much more challenging than that of the simplified system due to its complicated dynamics, which is made even worse when considering payload hoisting/lowering and uncertain system parameters. To solve this problem, an enhanced-coupling adaptive controller is proposed for DPOC in this paper. Specifically, the payload hoisting/lowering motion is elaborately considered. Moreover, to improve the swing suppression performance, more swing information are incorporated into the construction of control inputs. Particularly, the uncertain payload mass is online estimated by a new adaption law ensuring precise identification, which further enhances the robustness of the proposed method. By utilizing Lyapunov techniques and LaSalle’s Invariance Theorem, the closed-loop system is proven to be asymptotically stable around the desired equilibria. Finally, convincing hardware experimental results are presented to demonstrate the efficiency and superior control performance of the proposed method.

Sliding-Mode-Based Trajectory Tracking and Load Sway Suppression Control for Double-Pendulum Overhead Cranes

Overhead cranes with double-pendulum effect seem more practical than those with single-pendulum effect. However, in this case, the dynamic performance analysis and the controller design become more difficult. Moreover, achieving both high-precision tracking control and load sway suppression is a more significant issue for crane systems. In order to solve the aforementioned problems, the nonlinear dynamics of a 2-D overhead crane with double-pendulum effect is derived for controller design. Then, a novel sliding mode controller with nonlinear sliding surface is presented. Unlike a traditional linear sliding surface, the nonlinear one can change the damping ratio of the closed-loop system from its initial low value to final high value. The low value can provide a quick response, whereas the high value one can eliminate overshoot to make the cart track the given trajectory more precisely. The stability of the whole system is confirmed by the Lyapunov technique. The numerical simulation and experimental results validate that the proposed method has superior control performance and robustness with respect to parameter variations and external disturbance.

Novel Adaptive Hierarchical Sliding Mode Control for Trajectory Tracking and Load Sway Rejection in Double-Pendulum Overhead Cranes

Overhead cranes with double-pendulum effect seem more practical than those with single-pendulum effect. However, in this case, the dynamic performance analysis and the controller design become more difficult. Moreover, achieving both high-precision trajectory tracking and load sway rejection is a more significant issue for crane systems. For these purposes, the nonlinear dynamics of an overhead crane with double-pendulum effect is derived for the controller design. Then, a novel adaptive hierarchical sliding mode controller is presented. Unlike a traditional approach, the proposed one can make fixed sliding mode surface active to search the state trajectories. Such an adaptive design can make the states of the system to enter the desired sliding surface as soon as possible and, at the same time, can also improve the cart tracking precision. The Lyapunov technique and LaSalle's principle are employed to confirm the stability of the whole system. The experimental results validate that the proposed method has superior control performance and robustness with respect to parameter variations.

Steering Controller Design for Smart Autonomous Surface Vessel Based on CSF L2 Gain Robust Strategy

During the mission at sea, the yaw motion of the smart autonomous surface vessel (SASV) is always subject to the external disturbances. These issues make the design of a robust controller for steering autopilot system a quite challenging task. This paper focuses on the utilization of closed-loop shaping filter (CSF) to improve the SASV steering L2 gain robust control scheme. First, the mathematical model of the SASV is proposed and the parameters of the SASV model are obtained by the recursive least squares method (RLSM) based on the SASV turning test. Then the CSF L2 gain nonlinear robust controller design for SASV steering autopilot is described, and the stability and robustness of the proposed controller are proved based on the Lyapunov synthesis. Finally, the strong robustness and superior control performance are demonstrated through the practical SASV experiments. At the same time, the concise controller structure and definite physical meaning of SASV controller parameters are also concluded.

A Dual-Loop Control Approach of Active Magnetic Bearing System for Rotor Tracking Control

Rotor tracking control, which can be implemented by active magnetic bearing (AMB) system with high precision, can realize many functions, such as attitude control and special surface processing. However, large-motion rotor tracking control is difficult to implemented, due to AMB's highly nonlinear characteristics. In this paper, a dual-loop neural network sliding mode control (DL-NNSMC) system of AMB is proposed for rotor radial tracking control. The complete model of the AMB system is established and the dual-loop control system is designed. A circuit model that considers the rotor motion is established and the model-based inner loop of current control is established, conjointly for dealing with the influence of rotor motion on the current response and the unknown characteristics of the power amplifier. In the outer loop, a nonlinear electromagnetic force model is applied and a wavelet neural network sliding mode control algorithm is designed for accurate position control. Two cases of rotor trajectory tracking are simulated, and the simulation results demonstrate the validity of the proposed control system for large-motion rotor tracking control and its far superior control performance in terms of precision compared with common approaches based on sliding mode control (SMC).

Frequency Control of Isolated Wind-Diesel Microgrid Power System by Double Equivalent-Input-Disturbance Controllers

In a wind-diesel isolated microgrid, the fluctuating output power of wind turbine generator (WTG) and the perturbation of the load demand will lead to power imbalance and frequency deviation in the system. Appropriate frequency control schemes are indispensable to guarantee power quality through maintaining the power balance. In view of the outstanding performance of Equivalent-Input-Disturbance (EID) method, it is taken to the task of load frequency control (LFC) problem of this microgrid. In this paper, novel double EID controllers are proposed for the frequency control of a wind-diesel isolated microgrid. In this integrated control design, one single EID controller is applied to the pitch angle control system to smooth output power of WTG by controlling the pitch angle. At the diesel generator side, another single EID controller is applied to adjust the output power of the diesel generator to maintain the power balance of the system and finally preserve frequency in the normal range. Battery energy storage system is connected as auxiliary regulation. The simulation studies show superior flexibility and control performance of the proposed strategy compared to the conventional PI method in a wind-diesel isolated microgrid in MATLAB.

6-DOF Multi-Constrained Adaptive Tracking Control for Noncooperative Space Target

This paper addresses the problem of six-degree-of-freedom (6-DOF) relative position and attitude-tracking control for noncooperative space targets subject to multiple constraints, including input and full-state constraints. First, a novel 6-DOF integrated relative motion dynamics with control command coupling is established for the final approaching stage of a rendezvous task. Second, a Barrier Lyapunov function (BLF)-based controller is developed to guarantee the relative position, attitude, and velocity constraints of the spacecraft at all times. The requirement of less restrictive initial conditions compared with the quadratic Lyapunov function is presented via a detailed theoretical analysis. Third, a novel 6-DOF integrated multi-constrained adaptive tracking control scheme is proposed to simultaneously deal with input constraints, full-state constraints, unknown disturbances, and uncertainties. In particular, a solution to the chattering phenomenon, arising from the existence of sign function, is explored in an adaptive manner. Detailed controller design procedures and rigorous theoretical proof of all related closed-loop uniform ultimate bounded (UUB) stability are provided. In addition, the numerical simulation results are also exhibited to demonstrate the effectiveness and superior control performance of the proposed control scheme.

Finite-Time Trajectory Tracking Control for Overhead Crane Systems Subject to Unknown Disturbances

In this paper, a finite-time trajectory tracking control method for overhead crane systems with unknown disturbances is proposed. The controller is designed in the framework of observer-based control design, therefore, it works well even in the presence of internal and external disturbances. Prior knowledge of the system parameters, including the payload mass, the trolley mass, the cable length, and the friction-related coefficients, is not required for the designed controller. Moreover, the proposed controller can prove the state convergence on the sliding surface without any approximations to the original dynamic equations. More precisely, to estimate unknown disturbances, a terminal sliding mode observer is designed. And then, a finite-time trajectory tracking controller is synthesized by using the estimated information. As shown by Lyapunov techniques, the designed controller guarantees the finite-time tracking result. The simulation results are provided to illustrate the superior control performance and strong robustness of the proposed finite-time trajectory tracking control method.

Anti-Disturbance Neural-Sliding Mode Control for Inertially Stabilized Platform With Actuator Saturation

To consider the environment during ground vehicle driving, the inertially stabilized platform (ISP) can be used for electro-optical tracking instruments to isolate the senor's line of sight (LOS) from the carrier's vibrations with high precision and stability. This paper proposes the combination of a backstepping sliding mode controller with the adaptive neural networks approach (BSMC-NN) for ISP that achieves output torque saturation and considers parametric uncertainties, friction, and gimbal mass imbalance. An adaptive radial basis function neural network is adopted to approximate uncertain disturbances in this dynamic system. In contrast to the existing saturated control structures, an auxiliary function is designed to compensate for any error between the designed and the actual control torque. The closed-loop stability and asymptotic convergence performance are guaranteed based on the Lyapunov stability theory. Finally, the simulation and experimental results demonstrate that this proposed controller can effectively regulate the gimbal rotation angle under different external disturbances. This offers superior control performance despite the existence of the nonlinear dynamics and control input constraints.

A New Design Method for PI‐PD Control of Unstable Fractional‐Order System with Time Delay

In this paper, a practical PI‐PD controller parameter tuning method is proposed, which uses the incenter of the triangle and the Fermat point of the convex polygon to optimize the PI‐PD controller. Combined with the stability boundary locus method, the PI‐PD controller parameters that can ensure stability for the unstable fractional‐order system with time delay are obtained. Firstly, the parameters of the inner‐loop PD controller are determined by the centre coordinates of the CSR in the kd − kf plane. Secondly, a new graphical method is used to calculate the parameters of the PI controller, in which Fermat points in the CSR of (kp − ki) plane are selected. Furthermore, the method is extended to uncertain systems, and the PI‐PD controller parameters are obtained by using the proposed method through common stable region of all stable regions. The proposed graphical method not only ensures the stability of the closed‐loop system but also avoids the complicated optimization calculations. The superior control performance of this method is illustrated by simulation.