Adaptive Nonlinear Model Predictive Control Research Articles

Continuous adaptive nonlinear model predictive control using spiking neural networks and real-time learning

Model predictive control (MPC) is a prominent control paradigm providing accurate state prediction and subsequent control actions for intricate dynamical systems with applications ranging from autonomous driving to star tracking. However, there is an apparent discrepancy between the model’s mathematical description and its behavior in real-world conditions, affecting its performance in real-time. In this work, we propose a novel neuromorphic (brain-inspired) spiking neural network for continuous adaptive non-linear MPC. Utilizing real-time learning, our design significantly reduces dynamic error and augments model accuracy, while simultaneously addressing unforeseen situations. We evaluated our framework using real-world scenarios in autonomous driving, implemented in a physics-driven simulation. We tested our design with various vehicles (from a Tesla Model 3 to an Ambulance) experiencing malfunctioning and swift steering scenarios. We demonstrate significant improvements in dynamic error rate compared with traditional MPC implementation with up to 89.15% median prediction error reduction with 5 spiking neurons and up to 96.08% with 5,000 neurons. Our results may pave the way for novel applications in real-time control and stimulate further studies in the adaptive control realm with spiking neural networks.

High-speed trajectory following of a heavy-duty vehicle via adaptive nonlinear model predictive controller

High-speed trajectory following of a heavy-duty vehicle via adaptive nonlinear model predictive controller

Intelligent state estimation for fault tolerant integrated frequent RTO and adaptive nonlinear MPC

Combinations of real-time optimization (RTO) and model predictive control (MPC) have been widely employed in the process industry for tracking the economic optimum in the face of drifting disturbances and parameters. Online update of model parameters is a critical step in the implementation of RTO. In this work, an intelligent state and parameter estimation approach is developed by combining a fault diagnosis approach with a simultaneous state and parameter estimator. Since faults are more likely to develop as slow drifts, a nonlinear generalized likelihood ratio (GLR) approach available in the literature is modified by considering ramp models for the progression of faults with time. When a fault is isolated by the fault diagnosis and identification (FDI) component, the magnitude of the isolated fault is refined using a moving window state and parameter estimator until the fault magnitude continues to change. The estimation of fault magnitudes is carried out only when required and triggered by the fault identification scheme. Thus, the subset of parameters/faults that are being estimated online can change with time. The intelligent state and parameter estimator is further combined with an online optimizing control scheme consisting of integrated frequent RTO and adaptive MPC. The integrated optimizing control scheme has embedded intelligence to auto-correct models used for estimation, control, and optimization and decide whether the detected changes require the invocation of RTO. The proposed approach employs a single model to carry out four different tasks: process monitoring, state and parameter estimation, nonlinear predictive control, and real-time optimization. This eliminates difficulties that can arise due to model mismatch between different components of the online optimizing control scheme. The efficacy of the proposed scheme is investigated using the benchmark Williams Otto reactor and a continuously operated fermenter. The economic optimum operating point of these systems is sensitive to mean shifts in unmeasured disturbances or system parameters. The proposed ramp model based approach successfully isolates the parameter/ unmeasured disturbance/ sensor bias/ actuator bias that has undergone slow drift and tracks the shifting economic optimum without significant delays. Thus, the proposed integrated approach has the ability to handle normal and abnormal operating envelopes of the system.

Dynamic obstacle avoidance of unmanned ship based on event-triggered adaptive nonlinear model predictive control

This article studies trajectory tracking and dynamic obstacle avoidance problems of unmanned ships, and an event-triggered adaptive nonlinear model predictive control (EANMPC) method is constructed to solve the above problems. Firstly, a novel adaptive nonlinear model predictive control is used to track the trajectory of the unmanned ship. And then, an obstacle avoidance mechanism is formulated to deal with the condition of dynamic obstacles that appear during the unmanned ship trajectory tracking process. Secondly, an event-triggered mechanism is established to reduce the computational burden of the controller. Finally, several simulation results are provided for demonstrating the effectiveness and advantage of EANMPC.

Roboat III: An autonomous surface vessel for urban transportation

AbstractIn this paper, we present our novel autonomous surface vessel platform, a full‐scale Roboat for urban transportation. This 4‐m‐long Roboat is designed with six seats and can carry a payload of up to 1000 kg. Roboat has two main thrusters for cruising and two tunnel thrusters to accommodate docking and interconnectivity between Roboats. We build an adaptive nonlinear model predictive controller for trajectory tracking to account for payload changes while transporting passengers. We use a sparse directed graph to represent the canal topological map and then find the most time‐efficient global path in a city‐scale environment using the algorithm. We then employ a multiobjective algorithm's lexicographic search to generate an obstacle‐free path using a point‐cloud projected two‐dimensional occupancy grid map. We also develop a docking mechanism to allow Roboat to “grasp” the docking station. Extensive experiments in Amsterdam waterways demonstrate that Roboat can (1) successfully track the optimal trajectories generated by the planner with varying numbers of passengers on board; (2) autonomously dock to the station without human intervention; (3) execute an autonomous water taxi task where it docks to pick up passengers, drive passengers to the destination while planning its path to avoid obstacles, and finally dock to drop off passengers.

High-speed trajectory following of a heavy-duty vehicle via adaptive nonlinear model predictive controller

High-speed trajectory following of a heavy-duty vehicle via adaptive nonlinear model predictive controller

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.

Fuzzy Dynamic Output Feedback Force Security Control for Hysteretic Leaf Spring Hydro-Suspension With Servo Valve Opening Predictive Management Under Deception Attack

This article addresses the hierarchical resilient event triggered sampled-data security control and valve opening management problem for networked hysteretic leaf spring hydro-suspension with asymmetric actuator under false injected deception attack. For the derived Takagi–Sugeno (T–S) fuzzy model employed to tackle hysteresis nonlinearity, a new full-order dynamic output feedback top control with improved error and threshold functions for resilient event triggered mechanism is proposed to generate the target force so as to suppress the body and tire vibration under limited suspension chatter space. For the asymmetric cylinder hydraulic actuator over actuated by multiple servo valves, optimal servo valve openings are dynamic regulated to track the target force with weight adaptive nonlinear model predictive lower control. Benefiting from saved communication resource while more sparsely generating piecewise target force, as well as precise regulation by reasonably allocating over actuated valve openings to produce required force, the obtained results indicating more comfortable ride performance and less tire dynamic loads within smaller suspension chatter space favorably demonstrate the effectiveness of the proposed control strategy.

Nonlinear disturbance observer-based adaptive nonlinear model predictive control design for a class of nonlinear MIMO system

Model predictive control with less prior knowledge of system uncertainty and external disturbance is a long-standing theoretical and practical problem. In this paper, a solution is presented by proposing a disturbance observer-based adaptive nonlinear model predictive control scheme for a class of nonlinear MIMO systems. Our scheme requires the state and parameterdependent state-space model to linearise the nonlinear system along the prediction horizon. To cope with the unknown system uncertainty, the multiple estimation model and the concept of second-level adaptation [Pandey, V. K., Kar, I., & Mahanta, C. (2014). Multiple models and second level adaptation for a class of nonlinear systems with nonlinear parameterisation. In Industrial and Information Systems (ICIIS), 2014 9th International Conference on (pp. 1–6). IEEE.] technique for the nonlinear system is used. To ensure the boundedness of the estimated parameter, a projection-based adaptive law [Hovakimyan, N., & Cao, C. (2010). L1 adaptive control theory: Guaranteed robustness with fast adaptation. SIAM.] is used. Using a twin-rotor MIMO system (TRMS), the effectiveness of the proposed control algorithm has been verified. Simulation and real-time results show that the proposed control algorithm performs better than the existing control algorithm in the presence of unknown external disturbance and parameter uncertainties.

A Cascaded and Adaptive Visual Predictive Control Approach for Real-Time Dynamic Visual Servoing

In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model Predictive Control (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising.

Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors

Agile quadrotor flight in challenging environments has the potential to revolutionize shipping, transportation, and search and rescue applications. Nonlinear model predictive control (NMPC) has recently shown promising results for agile quadrotor control, but relies on highly accurate models for maximum performance. Hence, model uncertainties in the form of unmodeled complex aerodynamic effects, varying payloads and parameter mismatch will degrade overall system performance. In this paper, we propose L1-NMPC, a novel hybrid adaptive NMPC to learn model uncertainties online and immediately compensate for them, drastically improving performance over the non-adaptive baseline with minimal computational overhead. Our proposed architecture generalizes to many different environments from which we evaluate wind, unknown payloads, and highly agile flight conditions. The proposed method demonstrates immense flexibility and robustness, with more than 90% tracking error reduction over non-adaptive NMPC under large unknown disturbances and without any gain tuning. In addition, the same controller with identical gains can accurately fly highly agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking performance improvements of around 50% relative to the non-adaptive NMPC baseline.

Adaptive Model Predictive Control for Fire Incidents in Water Distribution Networks

The real-time control of water distribution networks for firefighting is an important but little-studied problem. Water utilities in England and Wales reduce hydraulic pressure to a minimum regulatory threshold in order to reduce leakage and avoid financial penalties. However, utilities are not legally bound to guarantee specific flow rates from fire hydrants, which poses a risk for firefighting. This study presents an adaptive nonlinear model predictive control scheme for water distribution networks integrating two modes of control: (1) normal control, when a network operates under normal conditions and the objective is to minimize energy costs and average zonal pressure, and (2) fire control, when fire flows need to be delivered from hydrants without exceeding maximum pressure and customer demand is maintained to the best extent possible. The proposed scheme is applied to an operational network. It demonstrates the combined benefits of reducing costs and leakage with increased fire flows.

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press.

The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.

Adaptive model predictive control design using multiple model second level adaptation for parameter estimation of two‐degree freedom of helicopter model

AbstractThis paper proposed an adaptive nonlinear model predictive control approach for a 2‐DoF helicopter model with both parametric uncertainties and input–output constraints. In the proposed control technique, the nonlinear helicopter model is linearized along the prediction horizon using a state and parameter‐dependent state‐space model. Furthermore, a linear quadratic objective function with constraints is carried out using the developed linearized model. Here, the multiple estimation model and the concept of second‐level adaptation are used to handle the parametric uncertainty of the nonlinear system. To ensure the boundedness of the estimated parameter within a predefined compact region, a projection based adaptive law is used. The adaptive tuning laws for the unknown parameters are derived by Lyapunov stability analysis. An ensemble Kalman filter has been used to observe the unavailable states of the 2‐DoF helicopter model. The effectiveness of the proposed control algorithm has been verified successfully in simulation as well as real‐time experimental setup of 2‐DoF of helicopter model and results are documented in tabular form to show superiority with an existing approach.

On Nonlinear Model Predictive Control for Energy-Efficient Torque-Vectoring

A recently growing literature discusses the topics of direct yaw moment control based on model predictive control (MPC), and energy-efficient torque-vectoring (TV) for electric vehicles with multiple powertrains. To reduce energy consumption, the available TV studies focus on the control allocation layer, which calculates the individual wheel torque levels to generate the total reference longitudinal force and direct yaw moment, specified by higher level algorithms to provide the desired longitudinal and lateral vehicle dynamics. In fact, with a system of redundant actuators, the vehicle-level objectives can be achieved by distributing the individual control actions to minimize an optimality criterion, e.g., based on the reduction of different power loss contributions. However, preliminary simulation and experimental studies – not using MPC – show that further important energy savings are possible through the appropriate design of the reference yaw rate. This paper presents a nonlinear model predictive control (NMPC) implementation for energy-efficient TV, which is based on the concurrent optimization of the reference yaw rate and wheel torque allocation. The NMPC cost function weights are varied through a fuzzy logic algorithm to adaptively prioritize vehicle dynamics or energy efficiency, depending on the driving conditions. The results show that the adaptive NMPC configuration allows stable cornering performance with lower energy consumption than a benchmarking fuzzy logic TV controller using an energy-efficient control allocation layer.

Adaptive Nonlinear Model Predictive Control of NOx Emissions under Load Constraints in Power Plant Boilers

Adaptive Nonlinear Model Predictive Control of NOx Emissions under Load Constraints in Power Plant Boilers

Fuzzy adaptive nonlinear information fusion model predictive attitude control of unmanned rotorcrafts

This study proposes a novel nonlinear information fusion model predictive control (NIFMPC) approach for the attitude control of unmanned rotorcraft subject to external wind disturbance. In the proposed control structure, the fusion estimation of control input is obtained by fusing the soft constraint information and the hard one via the nonlinear information fusion estimation method. The output error convergence of the closed-loop system is analyzed using the Lyapunov stability theory. In order to avoid input saturation, the fuzzy logic system (FLS) is adopted to design the adaptive fuzzy gain-scheduling (AFGS)-NIFMPC, in which the soft constraint information related to control action are adaptively scheduled according to fuzzy rules, with control input and its rate of change as FLS inputs and soft constraint information as FLS outputs. Finally, simulation results are presented to demonstrate the effectiveness and robustness of the proposed control strategies.

Trajectory tracking for autonomous vehicles on varying road surfaces by friction-adaptive nonlinear model predictive control

ABSTRACTWe propose an adaptive nonlinear model predictive control (NMPC) for vehicle tracking control. The controller learns in real time a tyre force model to adapt to a varying road surface that is only indirectly observed from the effects of the tyre forces determining the vehicle dynamics. Learning the entire tyre model from data would require driving in the unstable region of the vehicle dynamics with a prediction model that has not yet converged. Instead, our approach combines NMPC with a noise-adaptive particle filter for vehicle state and tyre stiffness estimation and a pre-determined library of tyre models. The stiffness estimator determines the linear component of the tyre model during normal vehicle driving, and the control strategy exploits a relation between the tyre stiffness and the nonlinear part of the tyre force to select the appropriate full tyre model from the library, which is then used in the NMPC prediction model. We validate the approach in simulation using real vehicle parameters, demonstrate the real-time feasibility in automotive-grade processors using a rapid prototyping unit, and report preliminary results of experimental validation on a snow-covered test track.

A novel adaptive three stages model predictive control based on fuzzy systems: Application in MIMO controlling of MED-TVC process

In the present study, a novel technique is suggested for the adaptive non-linear model predictive control based on the fuzzy approach in three stages. In the presented approach, in the first stage, the prediction and control horizons are obtained from a fuzzy system in each control step. Another fuzzy system is employed to determine the weight factors before the optimization stage of developing new controller. The proposed controller gives the parameters of the model predictive control (MPC) in each control step in order to improve the performance of nonlinear systems. The proposed control scheme is compared with the traditional MPC and Generic Model Control for controlling MED-TVC process. The performances of the three proposed controllers have been investigated in the absence and presence of disturbance in order to evaluate the stability and robustness of the proposed controllers. The results reveal that the novel adaptive controller based on fuzzy approach performs better than the two other controllers in set-point tracking and disturbance rejection with lower IAE criteria. In addition, the average computational time for the adaptive MPC exhibits a decline of 34% in comparison with the traditional MPC.

Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission.