State Feedback Control Research Articles

Reliable State Feedback Control for Uncertain Linear Systems With Terminal Reliability Constraint

Reliable State Feedback Control for Uncertain Linear Systems With Terminal Reliability Constraint

Dynamic Coding-Based Control Scheme Under Lossy Digital Network: An Optimized Time-Varying Packet Length Approach.

This work proposes a design scheme of the desired controller under the lossy digital network by introducing a dynamic coding and packet-length optimization strategy. First, the weighted try once-discard (WTOD) protocol is introduced to schedule the transmission of sensor nodes. The state-dependent dynamic quantizer and the encoding function with time-varying coding length are designed to improve coding accuracy significantly. Then, a feasible state-feedback controller is designed to attain that the controlled system subject to possible packet dropout is exponentially ultimately bounded in the mean-square sense. Moreover, it is shown that the coding error directly affects the convergent upper bound, which is further minimized by optimizing the coding lengths. Finally, the simulation results are provided via the double-sided linear switched reluctance machine systems.

Approximation-Based Approach to Adaptive Control of Linear Time-Varying Systems

An adaptive state-feedback control system is proposed for a class of linear timevarying systems represented in the controller canonical form. The adaptation problem is reduced to the one of Taylor series-based first approximations of the ideal controller parameters. The exponential convergence of identification and tracking errors of such an approximation to an arbitrarily small and adjustable neighbourhood of the equilibrium point is ensured if the condition of the regressor persistent excitation with a sufficiently small time period is satisfied. The obtained theoretical results are validated via numerical experiments.

Finite frequency H ∞ control for singular systems with application to a 2-machine 6-bus power network

This paper is concerned with the H ∞ control problem for singular systems over finite frequency ranges. The disturbance is assumed to be within low-/middle-/high-frequency ranges. Both the state feedback and static output feedback controllers are designed. First, by combining the Lyapunov function method and the generalized Kalman–Yakubovich–Popov lemma, a new finite frequency bounded real lemma (BRL) is proposed, which ensures the resulting closed-loop system to be regular, impulse free and stable with given finite frequency H ∞ performance bound. It is shown by theoretical analysis and numerical simulation that the proposed BRL has less conservatism than the existing ones in the literature. With the aid of the Finsler's Lemma, two improved finite frequency BRLs are further presented for the design of controllers. Then, by utilizing convexification techniques, sufficient conditions for solving the desired state feedback and static output feedback gains are presented in terms of linear matrix inequalities, respectively. Finally, the applicability and effectiveness of the proposed control methods are verified by a 6-bus power network system.

Continuous control of structural vibrations using hybrid deep reinforcement learning policy

An efficient active structural control scheme by utilizing the collective concepts of data-driven and physics-inspired reinforcement learning (RL) approaches is being introduced here. The controller is designed to deliver optimal feedback forces based on the full-state information, wherein the training process involves the use of deep neural networks (NNs) and a specially designed gradient descent-based sequence within the RL framework. This integration of algorithms results in a unique active control policy that accelerates the learning process, thereby demands considerably less computational resources to develop an optimal and stable controller as compared to existing data-driven approaches. Most importantly, the mentioned data-driven and physics-inspired approaches encompass deep deterministic policy gradient (DDPG) and an iterative gradient-based state feedback control (SFSC) algorithms, respectively, to establish the architecture of the hybrid control policy, referred to as hybrid RL-controller. Additional advantage of the hybrid RL-controller is its ability to operate in continuous state–action spaces, which allows the designed controller to address diverse structural control problems. The staggered performance of the hybrid RL-controller, both in continuous and discrete time, is evaluated in three case studies that involve structures in linear and nonlinear regimes. The outcomes include a detailed comparison of the hybrid RL-controller with the individually designed DDPG and SFSC RL control strategies, as well as the uncontrolled scenario. Furthermore, this study thoroughly investigates the real-life implementation concerns of control strategies, such as perturbations in model parameters and input forces, and time delays in the feedback loop. Finally, the results from this study corroborate that the designed RL-controller showcases superior performance and faster execution time in the feedback loop, making it suitable for the vibration control of multidimensional complex structures.

Extreme Homogeneous and Heterogeneous Multistability in a Novel 5D Memristor-Based Chaotic System with Hidden Attractors

This paper proposes a novel five-dimensional (5D) memristor-based chaotic system by introducing a flux-controlled memristor into a 3D chaotic system with two stable equilibrium points, and increases the dimensionality utilizing the state feedback control method. The newly proposed memristor-based chaotic system has line equilibrium points, so the corresponding attractor belongs to a hidden attractor. By using typical nonlinear analysis tools, the complicated dynamical behaviors of the new system are explored, which reveals many interesting phenomena, including extreme homogeneous and heterogeneous multistabilities, hidden transient state and state transition behavior, and offset-boosting control. Meanwhile, the unstable periodic orbits embedded in the hidden chaotic attractor were calculated by the variational method, and the corresponding pruning rules were summarized. Furthermore, the analog and DSP circuit implementation illustrates the flexibility of the proposed memristic system. Finally, the active synchronization of the memristor-based chaotic system was investigated, demonstrating the important engineering application values of the new system.

Lag synchronization for coupled neural networks with multistate or multiderivative couplings

In this paper, two kinds of lag synchronization for coupled neural networks (CNNs) are addressed, that is, the cases with multiple state or derivative couplings. Firstly, several lag synchronization criteria for these two CNNs are derived by selecting proper state feedback controllers. Furthermore, since external disturbances in neural network implementation should not be ignored, the impact of external disturbances on the lag synchronization for CNNs are also further investigated. Lastly, the obtained criteria are verified through two illustrative examples.

Aperiodic sampled‐data stabilization of switched singular systems with asynchronous switching: A hybrid control approach

AbstractThis article investigates the sampled‐data stabilization problem for a class of switched singular systems with aperiodic sampling. The asynchronous switching between the plant and the switched controller caused by sampling is considered. Unlike normal systems, the conventional zero‐order hold type sampled‐data control law may be inapplicable to singular systems. This is because under that control law, the algebraic equations of singular systems may have no solutions at sampling instants. To cope with this problem, a new hybrid control law composed of a mode‐dependent impulsive controller and a mode‐dependent sampled‐data state feedback controller is devised. The impulsive controller adjusts the values of differential substate at sampling instants so that the algebraic equations are solvable. Then, by constructing a novel piecewise sampling‐time‐dependent Lyapunov function and using the average dwell time method as well as convex technique, a sampling‐range‐dependent sufficient condition for exponential stability of the resulting closed‐loop system is established. Moreover, an optimization‐based method is proposed to solve the hybrid controller gains. Simulation results on two examples including a mechanical arm system show the effectiveness of the proposed control method.

Exponential stabilization of the class of the switched systems with mixed time varying delays in state and control

This paper presents the problem of exponential stabilization of switched systems with mixed time-varying delays in state and control. Based on the partitioning of the stability state regions into convex cones, a constructive geometric design for switching laws is put forward. By using an improved Lyapunov–Krasovskii functional in combination with matrix knowledge, we design a state feedback controller that guarantees the closed-loop system to be exponentially stable. The obtained conditions are given in terms of linear matrix inequalities (LMIs), which can be effectively decoded in polynomial time by various computational tools such as the LMI tool in MATLAB software. A numerical example is proposed to illustrate the effectiveness of the obtained results.

The chemostat reactor: A stability analysis and model predictive control

This contribution develops the model predictive control for an unstable chemostat reactor. Initially, we analyze the system’s model — a nonlinear first-order hyperbolic partial integro-differential equation (PIDE) — and carry the model linearization around an unstable operating condition. Employing the structure-preserving Cayley–Tustin transformation, we obtain a discrete-time model representation of the continuous model. Subsequently, we solve the operator Ricatti equations in the discrete-time setting to derive a full state feedback controller that stabilizes the closed-loop and design a Luenberger observer for state reconstruction given the system output measures. Finally, we formulate a dual-mode MPC ensuring constraint satisfaction and optimality, integrating the gain-based unconstrained full-state feedback optimal control obtained from the Ricatti equation. This dual-mode strategy describes an optimization problem where the predictive controller acts only if constraints become active within the control horizon. Simulation studies validate the controller performance, where the MPC only takes action if the constraints are predicted to be active within the control horizon while also guaranteeing closed-loop stabilization under only output feedback. This type of controller can be easily implemented with other control strategies and significantly decreases the computational costs of solving the optimal control problems when compared to other MPC approaches.

USV Trajectory Tracking Control Based on Receding Horizon Reinforcement Learning.

We present a novel approach for achieving high-precision trajectory tracking control in an unmanned surface vehicle (USV) through utilization of receding horizon reinforcement learning (RHRL). The control architecture for the USV involves a composite of feedforward and feedback components. The feedforward control component is derived directly from the curvature of the reference path and the dynamic model. Feedback control is acquired through application of the RHRL algorithm, effectively addressing the problem of achieving optimal tracking control. The methodology introduced in this paper synergizes with the rolling time domain optimization mechanism, converting the perpetual time domain optimal control predicament into a succession of finite time domain control problems amenable to resolution. In contrast to Lyapunov model predictive control (LMPC) and sliding mode control (SMC), our proposed method employs the RHRL controller, which yields an explicit state feedback control law. This characteristic endows the controller with the dual capabilities of direct offline and online learning deployment. Within each prediction time domain, we employ a time-independent executive-evaluator network structure to glean insights into the optimal value function and control strategy. Furthermore, we substantiate the convergence of the RHRL algorithm in each prediction time domain through rigorous theoretical proof, with concurrent analysis to verify the stability of the closed-loop system. To conclude, USV trajectory control tests are carried out within a simulated environment.

Adaptive Optimal Terminal Sliding Mode Control for T-S Fuzzy-Based Nonlinear Systems

This study utilizes the Takagi–Sugeno fuzzy model to represent a subset of nonlinear systems and presents an innovative adaptive approach for optimal dynamic terminal sliding mode control (TSMC). The systems under consideration encompass bounded uncertainties in parameters and actuators, as well as susceptibility to external disturbances. Performance evaluation entails the design of an adaptive terminal sliding surface through a two-step process. Initially, a state feedback gain and controller are developed using Linear Matrix Inequality (LMI) techniques, grounded on H2-performance and partial eigenstructure assignment. Dynamic sliding gain is subsequently attained via convex optimization, leveraging the derived state feedback gain and the designed terminal sliding mode (TSM) controller. This approach diverges from conventional methods by incorporating control effort and estimating actuator uncertainty bounds, while also addressing sliding surface and TSM controller design intricacies. The TSM controller is redefined into a strict feedback form, rendering it suitable for addressing output-tracking challenges in nonlinear systems. Comparative simulations validate the effectiveness of the proposed TSM controller, emphasizing its practical applicability.

Tracking control of robot manipulators actuated by brushless DC motors: Elimination of joint velocity measurements with a robust observer

AbstractThis work introduces an innovative robust partial state feedback controller designed for robotic manipulators actuated by brushless DC motors. The proposed controller/observer structure relies solely on actuator current and robot joint position measurements, while effectively compensating for dynamic uncertainties in both the electrical (actuator dynamics) and mechanical (robot's dynamical terms) subsystems. Specifically, a model–based robust observer, eliminating the need of joint velocity measurements, is combined with a backstepping‐type controller design. As opposed to the previous model based observers that depend on the actual model parameters, the proposed observer structure utilizes the best guest estimates of the system parameters supported with a robust compensation term. This approach eliminates the need for precise knowledge of system parameters of the observer design which is a significant improvement over most of the previous results. This approach ensures the semi‐global, uniformly ultimately bounded joint tracking error signal. The overall stability of the closed‐loop system is affirmed through Lyapunov‐based arguments. Experimental studies conducted on an in‐house‐built two‐link robotic manipulator, actuated by brushless DC motors, are presented to demonstrate the effectiveness and feasibility of the proposed method.

On global feedback control of unknown nonlinear systems

The construction of robust state-feedback controllers for unknown nonlinear systems is addressed through the derivation of robust bang-bang state-feedback controllers that lead to global asymptotic stabilisation. It is shown that a relatively simple testing process can provide sufficient information about an unknown system to facilitate the construction of bang-bang state-feedback controllers that guide the system toward global robust asymptotic stabilisation. The methodology presented in the paper applies to a broad class of nonlinear systems.

Exponential stability for switched semi‐Markov jump systems with mode‐dependent average dwell time and generally uncertain transition rates

AbstractThis paper investigates the exponential stability in mean square sense for a class of stochastic switched semi‐Markov jump systems (ssSMJS). In this model, a hierarchical control switched regulation is designed, considering the external stochastic disturbances and generally uncertain transition rates (TRs) of the entire system and making the system more general and practical. By using appropriate Lyapunov–Krasovskii functional, linear matrix inequality, Jensen inequality, and mode‐dependent average dwell time (MDADT), sufficient conditions on the exponential stability in mean square sense for ssSMJS are derived. Furthermore, a nonfragile state feedback controller is designed. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed criterion.

Combined Observer-Based State Feedback and Optimized P/PI Control for a Robust Operation of Quadrotors

This paper deals with a discrete-time observer-based state feedback control design by taking into consideration bounded parameter uncertainty, actuator faults, and stochastic noise in an inner control loop which is extended in a cascaded manner by outer PI- and P-control loops for velocity and position regulation. The aim of the corresponding subdivision of the quadrotor model is the treatment of the control design in a systematic manner. In the inner loop, linear matrix inequality techniques are employed for the placement of poles into a desired area within the complex z-plane. A robustification of the design towards noise is achieved by optimizing both control and observer gains simultaneously guaranteeing stability in a predefined bounded state domain. This procedure helps to reduce the sensitivity of the inner control loop towards changes induced by the outer one. Finally, a model-based optimization process is employed to tune the parameters of the outer P/PI controllers. To allow for the validation of accurate trajectory tracking, a comparison of the novel approach with the use of a standard extended Kalman filter-based linear-quadratic regulator synthesis is presented to demonstrate the superiority of the new design.

Distributed quantized secure bipartite consensus of linear multi‐agent systems with switching topologies and sequential scaling attacks

AbstractThis paper considers the secure distributed control consensus problem of linear multi‐agent systems (MASs) under switching topologies, subject to intermittent sequential scaling attacks, which was compelled to scaling factor, attack frequency, duration and cooperative‐competitive networks. First, the scenario of a fixed topology is considered, and a novel control protocol combined with a logarithmic quantizer and relative state measurements of neighbouring agents is discussed. The sighed graph is utilized to characterize the communication topology determined by the information flow directions and captured by the graph Laplacian matrix. After that, sufficient conditions for effectiveness of the developed control methods in guiding the MASs to secure bipartite leader‐following consensus are constructed. Second, the scenario of switching topologies is considered, and it is derived that the secure bipartite consensus will be achieved if the designed state feedback control protocol with the scaling factor, the attack duration, attack frequency and switching signal are selected properly. At last, to prove the effectiveness of the designed controllers, a simulation example depended on the real‐word actual military is introduced.

A vector decomposition approach to F–M model realization and its application to robust observed-state feedback control

This work presents a novel approach based on vector decomposition for realizing the Fornasini–Marchesini (F–M) model in multidimensional (n-D) systems. The primary focus is on resolving coefficient dependencies within the n-D rational transfer function matrix (TFM) of a multiple-input multiple-output (MIMO) system, aiming to generate a lower-order F–M model. The proposed method is further extended to robust observational state feedback control. To achieve this, we introduce innovative sufficient conditions for designing a low-dimensional resolvent invariant space (RIS) and outline the corresponding low-order F–M model realization process. By incorporating the polynomial vector dependency technique, we comprehensively address coefficient dependencies, leading to the construction of an even lower-order F–M model and contributing to the simplification of analytical complexity. To accommodate generic rational TFMs, we propose a matrix fractional description (MFD)-based process. Finally, we apply the developed F–M model implementation method to derive descriptor multiple affine representations (DMARs). This application proves instrumental in the robust observed-state feedback control design for rationally uncertain systems, enhancing the overall effectiveness of the robust observed-state feedback control design.

Simultaneous thermal zoning and demand control ventilation

Heating, Ventilation, and Air Conditioning systems play a crucial role in controlling indoor air conditions, including temperature, humidity, and CO₂ concentration, to ensure human comfort and safety. Thermal zoning and demand-controlled ventilation have demonstrated enhancements in comfort and energy efficiency. This study aims to develop a mathematical model for a Heating, Ventilation, and Air Conditioning system employing an estimated state-feedback control algorithm to optimize indoor comfort and energy savings while minimizing sensor requirements. The model encompasses dynamic equations governing CO₂ concentration and temperature in a five-room building. Temperature regulation is achieved by adjusting the airflow from a cooling unit with dampers, while CO₂ levels are managed by controlling the proportion of fresh air intake. To showcase the advantages of the proposed control approach, comparisons were made with alternative methods based on response performance, energy consumption, and sensor requirements. The estimated state-feedback control outperformed other approaches, requiring only one temperature and one CO₂ sensor at the mixed return. Additional occupancy sensors in each room are not necessary if an Extended Kalman filter is utilized. This technique exhibits scalability, adaptable to varying output variables and room configurations, and holds promise for broader implementation in the HVAC industry. By optimizing energy consumption and maintaining indoor air quality, this approach aligns with established sustainability metrics, promoting environmental responsibility. Compliance with health and safety audit standards ensures the fulfillment of social targets, while adherence to regulations and laws ensures governance targets are met.

Design stabilisers for multi-input affine control stochastic systems via stochastic control Lyapunov functions

This paper studies the problem of state feedback stabilisation, in the sense of a weak solution, for a class of multi-input affine control stochastic systems whose drift and diffusion terms are dependent on the control and for which classical methods are not applicable. Based on the generalised stochastic Lyapunov theorem and on the technique of stochastic control Lyapunov functions, sufficient conditions for the existence of a continuous stabilising feedback control are proposed, and a state feedback controller can be developed to guarantee the closed-loop system globally asymptotically stable in probability. This work generalises previous results on the stabilisation of single-input affine control stochastic systems. The obtained results are illustrated by a numerical example.