Mars entry guidance based on nonlinear model predictive control with disturbance observer

This paper investigates entry guidance of a capsule for pinpoint landing on Mars. In this scenario, the capsule is subject to the external disturbances caused by the atmosphere that can result in control saturation, and then undesired landing errors. To this end, a new guidance scheme to satisfy entry constraints, high-accuracy landing at high elevation sites, is proposed. The technical contributions of this work are two-fold: first, in order to mitigate the effects caused by large disturbance, a function describing the joint constraints of bank angle and slacked height is proposed; based on the nonlinear model predictive control (NMPC), a new algorithm is developed, where the constraints of dynamics, bank angle, slacked height, are sufficiently considered and precisely modeled; second, a state-space observer to improve the prediction of disturbance is introduced, which can significantly improve the accuracy of landing performance. The numerical simulations show the feasibility and validity of the proposed scheme.     

Robust multivariable predictive control for laser-aided powder deposition processes

This paper presents a robust multivariable predictive control for laser-aided powder deposition (LAPD) processes in additive manufacturing. First, a novel control-oriented MIMO process model is derived. Then, the objective of achieving desired geometrical and thermal properties is formulated as one of generating and tracking nominal reference profiles of layer height and melting pool temperature. This is accomplished via a nonlinear model predictive control with guaranteed nominal stability. Furthermore, a local ancillary feedback law is derived to provide robustness to bounded uncertainties. The paper verifies the effectiveness of the proposed control via a case study on a laser cladding process.     

Improving the performance of networked control systems with time delay and data dropouts based on fuzzy model predictive control

This paper proposes a fuzzy model predictive control (FMPC) combined with the modified Smith predictor for networked control systems (NCSs). The network delays and data dropouts are problems, which greatly reduce the controller performance. For the proposed controller, the model of the controlled system is identified on-line using the Takagi – Sugeno (T-S) fuzzy models based on the Lyapunov function. There are two internal loops in the proposed structure. The first is the loop around the FMPC, which predicts the future outputs. The other is the loop around the plant to give the error between the system model and the actual plant. The proposed controller is designed for controlling a DC servo system through a wireless network to improve the system response. The practical results based on MATLAB/SIMULINK are established. The practical results are indicated that the proposed controller is able to respond the networked time delay and data dropouts compared to other controllers.     

K-d tree based approach for point location problem in explicit model predictive control

Explicit Model Predictive Control (EMPC) produces control laws defined over a set of polyhedral regions in the state space, and the online computation of EMPC is to find the corresponding control law according to a given state by searching in a lookup table, called point location problem. This paper presents an approach of constructing a hybrid data structure called constructed k-d tree(CKDT), which combines the k-dimensional tree (k-d tree) with the binary search tree (BST) for point location in such polyhedral sets. To maintain a ‘full’ and balanced constructed tree the number of affine control laws is used as the basis for choosing the candidate hyperplanes (HPs) during the main construction process of CKDT, thus increasing offline efficiency by reducing the number of candidate HPs requiring computation. This methodology can be applied to the EMPC of high dimensional problems as the k-d tree – a main part of the CKDT approach - has already been successfully used to solve high dimensional problems in the field of computer science and engineering. The method involves a trade-off between memory storage requirement and online efficiency. A complexity analysis of the approach in the runtime and storage requirements is provided. The advantages of the method are supported by two examples in the paper.     

Robust distributed tracking control for linear multi-agent systems based on distributed intermediate estimator

This paper considers the distributed tracking control problem for linear multi-agent systems with disturbances and a leader whose control input is nonzero and not available to any follower. Based on the relative output measurements of neighboring agents, a novel distributed observer-based tracking protocol is proposed, where the distributed intermediate estimators are constructed to estimate the leader’s unknown control input and the states of the tracking error system simultaneously, then a distributed tracking protocol is designed based on the derived estimates. It is proved that the states of the tracking error system are uniformly ultimately bounded and an explicit tracking error bound is obtained. A simulation example of aircrafts verifies the effectiveness of the proposed method.     

Tube-based output feedback model predictive control of polytopic uncertain system with bounded disturbances

This paper addresses the output feedback model predictive control (OFMPC) of the constrained polytopic uncertain system in the presence of bounded state and output disturbances. The controller is designed in such a way that the unmeasurable state of the real system is bounded by the tube whose center is the estimated state of the disturbance-free (reference) model. The infinite-horizon reference control sequence is parameterized as a free control move followed by an output feedback law based on the reference state observer. By applying the OFMPC approach, the reference model is asymptotically stable so that robust stability of the real disturbed system is guaranteed. A numerical example is provided to illustrate the effectiveness of the proposed technique.     

Synchronization for chaotic systems via mixed-objective dynamic output feedback robust model predictive control

This paper focuses on mixed-objective dynamic output feedback robust model predictive control (OFRMPC) for the synchronization of two identical discrete-time chaotic systems with polytopic uncertainties, energy bounded disturbances, and input constraint. Using active control strategy, the chaos synchronization is transformed into standard dynamic OFRMPC scenarios tractable through receding horizon min–max optimization. Utilizing the notion of quadratic boundedness, the augmented closed-loop stability is further characterized. Then, the concepts of mixed performance criteria are firstly incorporated into the dynamic OFRMPC scheme to guarantee both the robust stability and the disturbance attenuation ability while preserving better dynamical behaviors. Necessary and/or sufficient conditions for desired mixed-objective dynamic OFRMPC are formulated involving linear matrix inequalities (LMIs). Finally, two numerical examples are given to demonstrate the theoretical results.     

Design of PID controller based on a self-adaptive state-space predictive functional control using extremal optimization method

In proportional-integral-derivative (PID) controller design, obtaining high stability and desired closed-loop response are of great importance for system engineers. Most existing methodologies, which have validated their excellent control performance on the accurate mathematical model, face significant difficulties in the unavoidable model mismatches and disturbance. To overcome these drawbacks, this paper proposes a self-adaptive state-space predictive functional control (APFC) based on extremal optimization method to design PID controller called EO-APFC-PID, wherein, the self-adaptive means, i.e., a forgetting factor recursive least squares (FFRLS) mechanism is embedded into state-space predictive functional control (PFC), and the proposed EO is exploited to alleviate the challenging problem that the elements in weighting factors of APFC technique are lacking analytical knowledge. The performance of the proposed EO-APFC-PID control scheme is demonstrated and compared with one classic PID tuning method and two state-of-the-art control strategies on the chamber pressure control for a coke furnace. The experimental results fully illustrate that the proposed method is more effective and efficient than other existing control strategies for achieving a desired behavior on the most test cases considered in this paper in terms of set point tracking, input disturbance rejection and output disturbance rejection.     

Population extremal optimization-based extended distributed model predictive load frequency control of multi-area interconnected power systems

How to design a set of optimal distributed load frequency controllers for a multi-area interconnected power system is an important but still challenging issue in the field of modern electric power systems. This paper presents an adaptive population extremal optimization-based extended distributed model predictive load frequency control method called PEO-EDMPC for a multi-area interconnected power system. The key idea behind the proposed method is formulating the dynamic load frequency control issue of each area power system as an extended distributed discrete-time state-space model based on an extended state vector, obtaining a distributed dynamic extended predictive model, and rolling optimization of real-time control output signal by adopting an adaptive population extremal optimization algorithm, where the fitness is evaluated by the weighted sum of square predicted errors and square future control values. The superiority of the proposed PEO-EDMPC method to a traditional distributed model predictive control method, a population extremal optimization-based distributed proportional-integral control algorithm and a traditional distributed integral control method is demonstrated by the simulation studies on two-area and three-area interconnected power systems in cases of normal, perturbed system parameters and dynamical load disturbances.     

Robust packet-based nonlinear fuzzy networked control systems

A class of networked nonlinear control systems with norm-bounded uncertainties is presented in this paper. The class is represented by Takagi–Sugeno (T-S) fuzzy models with packet processing. The network loop delay is considered either as known delay or random delay. For the former case, we develop conditions that guarantee the robust asymptotic stability and state-feedback stabilization with strict dissipativity and cast the results in linear matrix inequality (LMI) framework. Next employing a probabilistic-based delay partitioning method to deal with random delay, we establish novel LMI criteria for strict dissipative stability analysis and feedback synthesis. The efficacy of the ensuing techniques is demonstrated by numerical solution of typical examples and Mont Carlo simulation.     

Data-driven subspace predictive control: Stability and horizon tuning

Data-driven Subspace Predictive Control (SPC) is an advanced model-free process control strategy in the presence of system constraints. Efficient implementation of SPC requires appropriate tuning of the controller horizons, which are called Prediction Horizon and Control Horizon. This tuning is a critical step to guarantee the SPC closed-loop stability and to enhance the closed-loop performance and robustness. In this paper we propose an optimal tuning method for unconstrained SPC, which can guarantee stability, computational efficiency and optimality of the unconstrained SPC closed-loop system and is applicable to non-minimum phase open-loop stable or marginally stable systems. Derivation of general form of closed-loop transfer function for unconstrained SPC, and providing a necessary and sufficient condition of the closed-loop stability is the primary contribution of this work. In addition, the stability analysis enabled us to propose an algorithm to determine the shortest-feasible-prediction-horizon and the feasible range of prediction horizon. Consequently, these results are used in proposing a new algorithm to determine the SPC horizons in optimal manner. Simulation results illustrate effectiveness and importance of our proposed stability analysis and horizons tuning algorithm for unconstrained SPC.     

Subspace predictive control with the data-driven event-triggered law for linear time-invariant systems

In this paper, a subspace predictive control (SPC) method with a novel data-driven event-triggered law is proposed for linear time-invariant systems with unknown model parameters. Based on the conventional SPC method, the event-triggered law is introduced to substitute the typical receding horizon optimization, which reduces the data computation load of the traditional SPC method. The key parameters of the event-triggered law are derived by the Q-learning method via system data and the input-to-state stability of the system can be ensured with the designed event-triggered law. The simulation results illustrate the effect and merits of the proposed method with comparisons.     

Model predictive control for pre-compensated power converters: Application to current mode control

Current Mode Control (CMC) is the standard approach to regulate DC-DC power converters in high performance applications, allowing to obtain a faster time-response and better closed-loop stability if compared to Voltage Mode Control (VMC). In the last decade, several algorithms have been proposed to improve standard CMC, most of them requiring to replace the original controller. However, it is common to have either analog or embedded CMC controllers which cannot be replaced easily in commercial power converters. Inspired by very recent results in the topic, this paper proposes a Model Predictive Control (MPC) external loop aimed at optimally modifying the set-point of a CMC loop to improve converter performance. The proposed configuration is directly applicable to any pre-compensated converter as it does not require changes on the already-in-place controller. Moreover, by leveraging a multi-rate implementation, the benefits of MPC are introduced in power conversion without affecting much the computational cost of the over-all control system, contrary to what would happen for a direct MPC implementation. Simulation and experimental results on a synchronous DC-DC buck converter, controlled by a standard CMC algorithm, confirm the benefits of the approach.     

Robust sensorless observer-based adaptive sliding modes control of synchronous motors

Using a nonlinear complete order model of a synchronous motor, a robust second order sliding mode observer based control scheme is proposed. For that, a generalized super-twisting 3rd order observer is proposed for nonlinear systems. Based on the proposed observer scheme, a robust rotor flux observer is designed, then, a stator current observer is proposed using a classical super-twisting algorithm for extracting information of the rotor speed by means of the equivalent control method. The control design for the output tracking of a desired reference signal for the rotor speed is carried out with a classical super-twisting sliding mode algorithm and adaptive backstepping techniques. Due to the number of inputs, the flux in the excitation winding, and the direct component of the stator currents are also regulated. Numeric simulations predict a good performance of the closed-loop synchronous motor with parameter variations.     

Stabilization of artificial gas-lift process using nonlinear predictive generalized minimum variance control

Artificial gas-lift (AGL) is one of the most widely used methods in oil production to maintain acceptable oil flow to the processing equipment and sales when the reservoir pressure is not high enough. In spite of its popularity, the AGL process is prone to casing-heading instability, which is revealed as significant flow oscillation. This is undesirable as it results in production losses and unstable behavior that has negative impact on the downstream equipment. Controller design for such a process is very challenging as it exhibits highly nonlinear dynamics. In this work, the predictive generalized minimum variance control (NPGMV) is employed to derive a robust controller based on the state estimation to stabilize AGL process when casing-heading phenomenon occurs. A closed-form optimal control law is obtained based on the Taylor series approximation. Further, a nonlinear state observer is produced and combined with the controller to ensure closed-loop control through variables that are most beneficial to the system performance, which are unmeasurable and can be obtained only via estimation. Through simulation studies, the effectiveness of the proposed controller is demonstrated.     

Dynamic output-feedback control for nonlinear continuous-time systems based on parametric uncertain subsystem and interval type-2 fuzzy model

This paper addresses the interval type-2 fuzzy robust dynamic output-feedback control problem for a class of nonlinear continuous-time systems with parametric uncertainties and immeasurable premise variables. First, the parametric uncertainties are assumed to be a subsystem based on the control input matrix and output matrix, and described as a linear fractional. Secondly, the nonlinear continuous-time systems are described by the interval type-2 fuzzy model. Thirdly, the new dynamic output feedback controller is designed based on the interval type-2 fuzzy model and the linear fractional (parametric uncertainties), the sufficient conditions for robust stabilization are given in the form of linear matrix inequalities (LMIs). Compared with previous work, the developed methods not only have abilities to handle the fuzzy system with immeasurable premise variables but also can deal with the parametric uncertainties effectively. The results are further extended to a mobile robot case and a chemical process case. Finally, two simulation examples are performed to show the effectiveness of the propose methods.     

Robust stability criteria for uncertain systems with interval time-varying delay based on multi-integral functional approach

This paper is concerned with the robust stability analysis for uncertain systems with interval time-varying delay. In order to make full use of the delay information, a novel Lyapunov–Krasovskii functional (LKF) containing single, double, triple and quadruple integral terms is introduced, and a triple-integral state variable is also used. Then, by using the Wirtinger-based single and double integral inequality, introducing some positive scalars, the derivative of the constructed LKF is estimated more accurately. As a result, some stability criteria are derived, which have less conservatism and decision variables. Numerical examples are also given to show the effectiveness of the proposed method.     

Robust stabilization of uncertain 2-D discrete-time delayed systems using sliding mode control

This paper aims to solve the problem of sliding mode control for an uncertain two-dimensional (2-D) systems with states having time-varying delays. The uncertainties in the system dynamics are constituted of mismatched uncertain parameters and the unknown nonlinear bounded function. The proposed problem utilizes the model transformation approach. By segregating the proper Lyapunov–Krasovskii functional in concert with the improved version of Wirtinger-based summation inequality, sufficient solvability conditions for the existence of linear switching surfaces have been put forward, which ensure the asymptotical stability of the reduced-order equivalent sliding mode dynamics. Then, we solve the controller synthesis problem by extending the recently proposed reaching law to 2-D systems, whose proportional part is appropriately scaled by the factor that does not depend on some constant terms but rather on current switching surface’s value, which in turn ensures the faster convergence and better robustness against uncertainties. Finally, the proposed results have been validated through an implementation to a suitable physical system.     

Robust adaptive visual tracking control for uncertain robotic systems with unknown dead-zone inputs

This paper is concerned with the image-based visual servoing (IBVS) control for uncalibrated camera-robot system with unknown dead-zone constraint, where the uncertain kinematics and dynamics are also considered. The control implementation is achieved by constructing a smooth inverse model for dead-zone-input to eliminate the nonlinear effect resulting from the actuator constraint. A novel adaptive algorithm, which does not require a priori knowledge of the parameter intervals of dead-zone model, is proposed to update the parameter values online, and the dead-zone slopes are not required the same. Furthermore, to accommodate the uncertainties of uncalibrated camera-robot system, adaptation laws are developed to estimate the uncertain parameters, simultaneously avoiding singularity of the image Jacobian matrix. With the full consideration of unknown dead-zone constraint and system uncertainties, an adaptive robust visual tracking control scheme together with dead-zone compensation is subsequently established such that the image tracking error converges to the origin. Based on a 3-DOF manipulator, simulations are conducted to verify the tracking performance of the proposed controller.