Mapless Underwater Navigation Using Convex Quadratic Programming-Based Geomagnetic–Inertial Fusion

This paper proposes an improved constrained networked model predictive tracking control design for the chamber pressure of a coke furnace under uncertainty and packet losses. Unlike conventional constrained model predictive control (MPC) strategies that have a limitation in the consideration of both set-point tracking and the dynamic process responses, the system state variables and output tracking errors are combined and thus can be regulated simultaneously in the new MPC scheme. Based on such advantages, there are more degrees of freedom for the subsequent controller design and improved system performance can then be obtained. Case studies on the regulation of chamber pressure of a coke furnace under process uncertainties and packet losses are investigated to verify the proposed approach in comparison with typical traditional constrained MPC schemes.

Energy-efficient operation of the thermal management system in electric vehicles via integrated model predictive control

Stability is perhaps the most important property of controllers. A stable controller performs its task reliably in any circumstance; however, an unstable controller may cause system failure with dramatic effects. Unstable controllers in an active vibration control (AVC) system may even cause structural failure. Stability can be guaranteed and studied in traditional control systems by inspecting the location of the closed-loop poles of the transfer function, or equivalently the magnitude of the eigenvalues of the state matrix. Unfortunately, the constrained model predictive control (MPC) law is nonlinear and it cannot be expressed in a closed form. Considering the cost function as a Lyapunov function is the key to understand the stability of the constrained MPC strategy. As long as the cost function is monotonically decreasing, the control process remains stable. It can be proved that the condition for this is to ensure the feasibility of the input sequence from the current time up to infinity. Fortunately, this seemingly formidable task can be implemented with a finite number of optimization variables. An additional constraint checking horizon in infinite horizon cost dual-mode constrained MPC ensures stability and feasibility. The series of additional constraints to ensure stability also allocate a special region of state-space called the region of attraction and its subset, the target set. For an optimal MPC strategy with maximum performance, these have a complex polyhedral shape. In the interest decreasing complexity and increasing computational speeds, these sets can be simplified to low complexity hypercubes or a hyperellipsoids. This chapter reviews the stability properties of the constrained MPC control law, how it can be guaranteed at all times, introduces the reader to maximal invariant target sets and some of its simplified approximations.

Model predictive control has been widely used in the industry. This can control the multivariable system with constraints on input and output variables but it needs online computation solver, and creates the non-convex solution in nonlinear plant due to the parameter uncertainties. The online computational problem and non-convex solution of the model predictive control are achieved via neural network model predictive control. The paper explores the speed control of permanent magnet synchronous motor (PMSM) by using neural network model predictive control (NNMPC) technique. The multi-layer artificial neural network is used to identify the dynamics of PMSM. The set point speed tracking control of PMSM is identified by using neural network model predictive control strategy. By using the set of input and output data obtained from the system, the multi input-output feed-forward neural network model is created. Levenberg-Marquardt algorithm is used to train the process models of the PMSM. That provides future plant output for control optimization of the predictive control. The overall system is developed and tested in the MATLAB/Simulink. To evaluate the efficiency of the controller proposed, it is compared with a constrained model predictive controller through the studies of simulation. The overshoot and settling time of the speed response of the PMSM are measured and analyzed for NNMPC and constrained MPC.

In this paper, a “third generation” benchmark problem that focuses on the control of wind excited response of a tall building, using the Model Predictive Control (MPC) scheme, is presented. A 76 story, 306 m tall concrete office tower proposed for the city of Melbourne, Australia, is being used to demonstrate the effectiveness of MPC. The MPC scheme is based on an explicit use of a prediction model of the system response to obtain the control actions by minimizing an objective function. Optimization objectives in MPC include minimization of the difference between the predicted and desired response trajectories, and the control effort subjected to prescribed constraints. By incorporating input/output hard constraints, the MPC scheme provides an optimal control force that satisfies the prescribed constraints.

This paper presents an event-triggered model predictive control (MPC) strategy with learning terminal cost for robotic manipulators containing model uncertainty and input constraints. In the proposed MPC structure, an adaptive predictive model for the robotic system is established by radial basis function neural networks (RBFNNs) firstly. Then, a terminal cost adjusted by the global learning mechanism is constructed. Both global steady-state optimization and transient fast convergence are achieved by adding the learning terminal cost to the MPC scheme. After that, a triggering condition of the MPC solving is developed based on the predictive model’s weights and the predictive tracking error. Besides, the condition to avoid Zeno behavior is obtained. The recursive feasibility of the proposed MPC strategy is verified, and the ultimately uniformly boundedness (UUB) of all variables is proved according to the Lyapunov theorem. Finally, experiments based on an xMate7 Pro robot are conducted to demonstrate the effectiveness of the presented method. Note to Practitioners—The tracking control of robotic manipulators is a common and important problem in industrial applications, such as grasping, loading, unloading, et al. There exist some limitations in existing control methods. For example, general control strategies such as adaptive control, sliding mode control neglect the balance between control costs and expected performance; existing optimal control approaches rarely consider global steady-state optimization and transient fast convergence under the influence of model uncertainty. This paper is motivated by these limitations of robotic control design and inspired by model predictive control and optimal control theory. It develops a novel strategy for tracking control of robotic manipulators, which includes three following items: (1) adopt an approximation model constructed by neural networks as the predictive model for estimating the robotic system; (2) introduce a critic network into the terminal cost of the objective function of MPC for learning global optimal solution; (3) establish an event-triggered mechanism for solving of the optimization problem. The proposed method is verified by experimental results. In our future work, the proposed control strategy will be applied to more industrial processes.

A closed-loop artificial pancreas based on model predictive control: Human-friendly identification and automatic meal disturbance rejection

A model predictive control for a multi-chiller system in data center considering whole system energy conservation

Modeling and model predictive control of the BioPower combined heat and power (CHP) plant

This paper presents a case study application of constrained model predictive control (MPC) strategy with zone control for a coupled flue gas desulfurization system in Yangtze Petrochemical Thermal Power Plant. This desulfurization system can only be operated by manual due to its large inertia, strong nonlinearity and severe coupling between the two desulfurization towers. In this paper, the data reconciliation technology is firstly used for data processing, then a constrained MPC with zone control based on an augmented state space model is proposed to make sure that the outlet SO2 concentrations meet emission limits even under large changes of the boiler load, while making pH in the circulation tank within specified zone. Simulations are also performed in order to validate the control performance by comparing with conventional dynamic matrix control (DMC). The proposed approach is implemented on the real desulfurization system with developed software. Application results show that satisfactory desulfurization efficiency can be achieved, and the consumption of ammonia is significantly reduced.

This paper proposes a hierarchical hybrid control framework, termed Fuzzy Model Predictive Control with Iterative Learning (FMPC–IL), to enhance load–frequency control (LFC) in single-area hybrid power systems with high renewable penetration. The architecture integrates three layers: (i) a supervisory fuzzy logic controller (FLC) that adaptively tunes the MPC cost-function weights (Qⓜ,R)using the frequency error and its change, improving robustness under operating uncertainties; (ii) a constrained model predictive control (MPC) layer that computes the optimal control action while explicitly enforcing actuator and operational limits; and (iii) an iterative learning control (ILC) layer that exploits repetitive daily patterns in load and renewable profiles to generate a feedforward compensation signal updated cycle-to-cycle. The nonlinear governor–turbine limitations, including valve deadband, actuator saturation, and generation rate constraints (GRC), are represented in the simulation plant, whereas a linearized discrete-time model is used as the internal prediction model for MPC optimization. The proposed FMPC–IL strategy is evaluated in MATLAB/Simulink under stochastic load disturbances, renewable fluctuations, and parametric uncertainties. The results demonstrate improved frequency regulation and robustness compared with MPC, fuzzy-MPC, and MPC–IL baselines, particularly during severe disturbances and uncertainty scenarios.

In this paper, constrained model predictive control (MPC) designs for a class of structured uncertain time-delay systems have been developed, where state delays as well as a random input delay have been taken into account. By replacing the control strategy with a freer one, an improved MPC design with larger initial feasible region has been developed. Furthermore, by analyzing the obtained algorithm, some useful properties of the solutions to the MPC optimization problem have been established. With these properties, a variant of the obtained algorithm with dramatically reduced number of online optimizing variables and hence the online computational burden has also been developed. However, the control performance degrades slightly. The two algorithms haven been proved to stabilize the closed loop system in the mean square sense and to guarantee the satisfaction of constraints. Finally a numeric example is given to illustrate the proposed results.

In order to account for hard limits on state and actuator range, a nonlinear flight control method combined hierarchy-structured dynamic inversion (HSDI) and constrained model predictive control (MPC) was proposed for a researched unmanned aircraft (RUA). First, the HSDI control law linearizes the nonlinear dynamic model of the aircraft. Then, constrained MPC is designed in accordance with Pseudo-linear system to guarantees HSDI and determines online constrained optimal inputs. Finally, the robust performance of the combined controller is improved by employing on-line identification for uncertainties based on adaptive estimation approaches. The nonlinear model was updated with adaptive parameter T-S fuzzy model to improve the fidelity of the model used for controller synthesis. Simulation results show satisfactory performance of the presented controller for attitude command tracking control, and the robustness to parameters variations and the disturbance rejection are successfully accomplished.

The constraints of output variables, input variables and intermediate variables exist widely in chemical process control. The inconsistency in different constraints may make constrained model predictive controller have no feasible solutions, which will bring harmful effect to practical production. To ensure the implementation of model predictive control, using its global optimization performance and constraint handling mechanism, a new particle-swarm optimization algorithm with the function of constraint handling, was proposed in this article. Taking into account the form of constraints and the constraints characteristics of MIMO (multi-input multi-output) predictive control system, this thesis, based on convex polyhedron geometry, discuss the feasibility of constrained model predictive control. Combined with duality theorem, the output constraints of system are transformed into constraints of input. After that, the constraints form which meets the requirements of control algorithm is obtained. Finally, particle swarm optimization algorithm is used to conduct the optimization of predictive control system. The simulation results of MIMO model with constraints showed the advantages and effectiveness of this algorithm.

This paper presents a general formulation of the model predictive control (MPC) scheme for controlling in real time the response of structures under earthquakes. The MPC scheme is based on an explicit use of a prediction model of the system response to obtain the control actions by minimizing an objective function. Optimization objectives in MPC include minimization of the difference between the predicted and desired response trajectories, and the control effort subjected to certain constraints. In this study, the prediction model is formulated using both feedforward (FF) and feedback (FB) components to increase the effectiveness of the MPC scheme. The FF loop in the prediction model is formulated using two types of input. First, it is designed using the established Kanai–Tajimi‐type model to represent the earthquake input. Second, a real‐time FF loop is introduced using an auto‐regressive (AR) model for earthquake ground motion which is constantly updated using real‐time on‐line observations. The real‐time FF loop certainly promises to add predictive and adaptive features to the control actions to account for any unusual features in the ground motion. The structural response with and without the FF and FB loops are compared. Examples are used to demonstrate the efficacy of the proposed methodology. These examples show that the MPC‐based controller is effective in reducing the structural response under different earthquakes which contain distinct features in their spectral and non‐stationary descriptions. The performance of the MPC scheme is then compared to H2 control strategies for different time horizons. The effectiveness of MPC is shown to be equivalent to the optimal control. This paper lays a foundation for capturing the main strengths of MPC, i.e. computational expediency, real‐time application, intrinsic compensation for time delays, treatment of constraints and potential for future extensions in structural control. Copyright © 2001 John Wiley & Sons, Ltd.