The data-driven sliding mode control (SMC) method proves to be highly effective in addressing uncertainties and enhancing system performance. In our previous work, we implemented a co-design approach based on an input-mapping data-driven technique, which effectively improves the convergence rate through historical data compensation. However, this approach increases computational complexity in multi-input and multi-output (MIMO) systems due to the dependency of the number of online optimization variables on system dimensions. To improve applicability, this paper introduces a novel input-mapping-based online learning SMC strategy with low computational complexity. First, a new sliding mode surface is established through online convex combination of pre-designed offline surfaces. Then, an input-mapping-based online learning sliding mode control (IML-SMC) strategy is designed, utilizing a reaching law with adaptively adjusted convergence and switching coefficients to minimize chattering. The input-mapping technique employs the mapping relationship between historical input and output data for predicting future system dynamics. Accordingly, an optimization problem is formulated to learn from the past dynamics of the uncertain system online, thereby enhancing system performance. The optimization problem in this paper features fewer variables and is independent of system dimension. Additionally, the stability of the proposed method is theoretically validated, and the advantages are demonstrated through a MIMO system. Note to Practitioners—The design of control strategies that reduce the impact of mismatches between practical systems and models on system performance, while also ensuring applicability, is crucial. To address this issue, this paper proposes a low-complexity IML-SMC strategy. This strategy uses historical real input-output information and the mapping relationship with future dynamics to compensate for the impact of unknown dynamics and improve the system’s convergence rate. Notably, the control strategy introduced in this paper significantly reduces online computational complexity, ensuring applicability, and stability is proven. When there is a deviation between the model and the actual system, practitioners can implement the low-complexity IMC-SMC strategy proposed in this paper to more quickly achieve the control objectives in the actual system.

The coordination of traffic flow among regions is necessary for a large-scale road traffic network to avoid local congestions and improve the overall traffic efficiency. In this paper, by incorporating the random characteristic of traffic flow, we formulate the problem of perimeter traffic flow control for a multi-region traffic network as a Markov decision process with adaptive state definition. Based on stochastic macroscopic fundamental diagrams (MFD) of regions, a state transition probability model is proposed to describe the state changes of the multi-region traffic network under different perimeter control policies. With the stochastic MFD-based state transition probabilities rather than counting from the historical data, a policy iteration algorithm with perturbation analysis is introduced to get the optimal perimeter control policy in real-time without the requirement of online or offline learning. The proposed method is compared with the classic perimeter control methods by simulation, which indicates its effectiveness in mitigating the congestion and improving the network throughput, as well as the promising implementation prospect.

The robust iterative learning control (RILC) can deal with the systems with unknown time-varying uncertainty to track a repeated reference signal. However, the existing robust designs consider all the possibilities of uncertainty, which makes the design conservative and causes the controlled process converging to the reference trajectory slowly. To eliminate this weakness, a data-driven method is proposed. The new design intends to employ more information from the past input-output data to compensate for the robust control law and then to improve performance. The proposed control law is proved to guarantee convergence and accelerate the convergence rate. Ultimately, the experiments on a robot manipulator have been conducted to verify the good convergence of the trajectory errors under the control of the proposed method.

Effective traffic signal control strategies are critical for traffic management in urban traffic networks. Most existing optimization-based urban traffic control approaches update the traffic signal at regular time instants, where the length of the fixed update time interval is determined based on a trade-off between the computational efficiency and the control performance. Since event-triggered control (ETC) allows for more flexible and more efficient control than conventional time-triggered control by triggering the control action by events, and since it can refrain from redundant optimization while retaining a satisfactory behavior, we use an ETC scheme for traffic light control. In addition, based on the geographically distributed feature of traffic networks, a distributed paradigm is adopted to reduce the computational complexity for the optimization. We propose a distributed threshold-based event-triggered control strategy, where the independent triggering of agents leads to an asynchronous update of traffic signals in the system. The triggered agent then solves a mixed-integer linear programming problem and updates its traffic signals. The proposed approach is evaluated under various traffic demands by simulation, and is shown to yield the best trade-off between control performance and computational complexity compared to other control strategies.

This paper investigates consensus problem of second-order multi-agent system on matrix-weighted networks. It is shown that when the null space of the Gauge transformed graph Laplacian is spanned by the Kronecker product of an all-one vector and a set of orthogonal vectors, the algebraic multiplicity of eigenvalue zero cannot exceed the nullity of the graph Laplacian, thus admitting a proper blocking of the system matrix's Jordan normal form. Second-order bipartite consensus is thereby achieved independent of the structural balance of the network. Simulation examples are provided to demonstrate the theoretical results.

We present an assume-guarantee contract framework for cyber-physical system design under probabilistic requirements. Given a stochastic linear system and a set of requirements captured by bounded Stochastic Signal Temporal Logic (StSTL) contracts, we propose algorithms to check contract compatibility, consistency, and refinement, and generate a sequence of control inputs that satisfies a contract. We leverage encodings of the verification and control synthesis tasks into mixed integer optimization problems, and conservative approximations of probabilistic constraints that produce sound and tractable problem formulations. We illustrate the effectiveness of our approach on three case studies, including the design of controllers for aircraft power distribution networks.

The corrections given involveEquations (4),(5),(9)in the S-model and Equations (13)–(16) in the integrated flow and emission model introduced in[1]. For details and extensive proofs of the proposed corrections and modifications, we refer the readers to[2]and[3]. Moreover, we propose some extensions/modifications to[1, eq. (9)]that result in a simple formula that can be used to compute the integral in[1, eq. (9)]. Since the S-model was originally introduced in[4], the corrections and modifications given for the S-model in this paper also hold for[4].

Since urban traffic congestion has become a major problem for big cities in recent years, we propose a distributed control scheme for traffic lights in the network. First, a new criterion called traffic process ability which implies the balance between the traffic demand and traffic capacity of each road is introduced. Moreover, the congestion of a road is mitigated by utilizing the traffic process ability of the neighbors more effectively, where different weights are assigned to roads in each agent according to their importance or the real-time traffic conditions. As only local information is needed, a distributed control scheme in which the road network is divided among several agents is proposed. Furthermore, in order to accelerate the congestion dissipation process, the aggregated state of each agent is introduced into the performance index and balanced with its neighboring agents. The control signals are calculated by agents in a parallel way and the optimization problem is solved iteratively to reach a convergence. Finally, the effectiveness of the proposed control scheme is evaluated by simulation under different scenarios and the performance is compared with the traffic responsive control method SCOOT.

Traffic state prediction has been a popular topic, since traffic congestion occurs in most cities and creates inconvenience to human daily life. In this paper, we propose a predicting method for a city's overall traffic state, in order to help people avoid possible future congestion. Based on the variable-order Markov model theory and probability suffix tree, the proposed method makes use of the association rules to improve forecasting performance. Since the association rules are extracted from the historical traffic data and describe the traffic state relations among different regions, the proposed method can improve the predictive accuracy. The traffic system in Shanghai is considered as our experimental case because of its complicated and gigantic coupling transport network. The experimental results indicate more accuracy compared with other methods in long-term traffic status prediction.

This paper examines the event-triggered consensus problem of the multi-agent system on signed networks. An event-based distributed control protocol is proposed for multiagent networks where the dynamics of each agent is characterized by a controllable linear time-invariant system ( A, B). By exploring the Gauge transformation between Laplacian matrix and signed Laplacian matrix, we show that the bipartite consensus and trivial consensus can be achieved, respectively, in terms of the structural balance of the underlying signed network. Furthermore, the Zeno phenomenon of the closed-loop system is shown to be non-existed when employing the proposed event-based control protocol. Simulation results are finally provided to demonstrate our results.

We develop an assume-guarantee contract framework for the design of cyber-physical systems, modeled as closed-loop control systems, under probabilistic requirements. We use a variant of signal temporal logic, namely, Stochastic Signal Temporal Logic (StSTL) to specify system behaviors as well as contract assumptions and guarantees, thus enabling automatic reasoning about requirements of stochastic systems. Given a stochastic linear system representation and a set of requirements captured by bounded StSTL contracts, we propose algorithms that can check contract compatibility, consistency, and refinement, and generate a controller to guarantee that a contract is satisfied, following a stochastic model predictive control approach. Our algorithms leverage encodings of the verification and control synthesis tasks into mixed integer optimization problems, and conservative approximations of probabilistic constraints that produce both sound and tractable problem formulations. We illustrate the effectiveness of our approach on a few examples, including the design of embedded controllers for aircraft power distribution networks.

In the consideration of constraints, repetitive model predictive control is an effective method to track a periodic signal as well as reject a periodic disturbance. However, in practical systems, it is difficult to determine the period of the disturbance; meanwhile, too much information is needed for the design of repetitive controller, thus will make it difficult for the design of controller. In this paper, a system subject to periodic disturbance with Markov jump uncertainty is considered and a new method of repetitive model predictive control with Markov jump model has been introduced to reject the disturbance. The simulation demonstrates that the proposed method is effective.

In this paper, the information flow topology is regulated under the precondition that each agent can only utilize the information restricted by the physical communication channels. The objective topology is investigated under the framework of double-layer structures and is finally described by an optimal problem involving the eigenvalues on each layer. By introducing the element distance of eigenvector corresponding to the eigenvalues, the first layer in hierarchy is constructed, which furthermore induces the algorithm of double-layer topology design. Theoretical analysis and numerical simulations are provided to demonstrate the validity of the developed algorithms.

For automatic train operation (ATO) system, the main object of ATO controller is to control the moving of the train as close as possible to the target curve, so that the demands for time, safety, travelling comfort and energy saving can be met. In this paper, a model predictive control (MPC) strategy is introduced to control the train system due to its good performance and ability to handle system with time-delay and constraints. Model parameters can be identified by measured data. The proposed MPC algorithm with corresponding switching strategy is designed. The simulation results illustrate the feasibility and effectiveness of the proposed control scheme.

To tackle the twin challenges of sustainable energy supply and climate change, numerous efforts have been made to decarbonize the whole energy systems. Control Engineering, which concerns the automated operation of a machine or system to achieve desired target(s) and to avoid unstable or unintended disruptive behavior, has played a key role in modern industry and across the whole energy system. Advanced control technologies, such as optimal control, provide a framework to simultaneously regulate the system performance and limit control energy. However, little has been done so far to exploit the full potential of controller design in reducing the energy consumption while maintaining desirable system performance. This paper for the first time investigates the correlation between control energy consumption and system performance, and shows that this correlation is nonlinear and the controller design should be a delicate synthesis procedure to achieve better trade-off between system performance and energy saving.

Refining transportation mobility and improving the living environment are two important issues that need to be addressed in urban traffic. To reduce traffic delays and traffic emissions for urban traffic networks, this paper first proposes an integrated macroscopic traffic model that integrates a macroscopic urban traffic flow model with a microscopic traffic emission model for individual vehicles. This integrated model is able to predict the traffic flow states and the emissions released by every vehicle at different operational conditions, i.e., the speed and the acceleration. Then, model predictive control (MPC) is applied to control urban traffic networks based on this integrated traffic model, aiming at reducing both travel delays and traffic emissions of different gases. Finally, simulations are performed to assess this multiobjective control approach. The obtained simulation results illustrate the control effects of the model predictive controller.

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 this paper, an advanced control strategy, i.e., model predictive control (MPC), is applied to control and coordinate urban traffic networks. However, due to the nonlinearity of the prediction model, the optimization of MPC is a nonlinear nonconvex optimization problem. In this case, the online computational complexity becomes a big challenge for the MPC controller if it is implemented in a real-life traffic network. To overcome this problem, the online optimization problem is reformulated into a mixed-integer linear programming (MILP) optimization problem to increase the real-time feasibility of the MPC control strategy. The new optimization problem can be very efficiently solved by existing MILP solvers, and the global optimum of the problem is guaranteed. Moreover, we propose an approach to reduce the complexity of the MILP optimization problem even further. The simulation results show that the MILP-based MPC controllers can reach the same performance, but the time taken to solve the optimization becomes only a few seconds, which is a significant reduction, compared with the time required by the original MPC controller.

Target detecting and dynamic coverage are fundamental tasks in mobile robotics and represent two important features of mobile robots: mobility and perceptivity. This paper establishes the constrained motion model and sensor model of a mobile robot to represent these two features and defines the k -step reachable region to describe the states that the robot may reach. We show that the calculation of the k-step reachable region can be reduced from that of 2(k) reachable regions with the fixed motion styles to k + 1 such regions and provide an algorithm for its calculation. Based on the constrained motion model and the k -step reachable region, the problems associated with target detecting and dynamic coverage are formulated and solved. For target detecting, the k-step detectable region is used to describe the area that the robot may detect, and an algorithm for detecting a target and planning the optimal path is proposed. For dynamic coverage, the k-step detected region is used to represent the area that the robot has detected during its motion, and the dynamic-coverage strategy and algorithm are proposed. Simulation results demonstrate the efficiency of the coverage algorithm in both convex and concave environments.

Motion synchronization in mobile robot networks is a fundamental task in distributed multi-robot collaboration. In this paper, we investigate the robustness of synchronous speeds against robot and communication failures, an important feature of distributed systems. A performance metric is proposed to represent the robustness of the robot teams based on complex networks theories. Moreover, a distributed topology control algorithm to improve the robustness metric of groups of robots by increasing the communication range temporarily is presented. When the new topology is constructed and is stable, the range for communicating is reduced to the original value. Finally, the results of simulation experiments have demonstrated the efficiency of the proposed control algorithm while robots and connections fail in a robot group

Using multi-robot has more advantages than using single robot for unknown environment exploration. But it brings a new problem of task allocation. Market-based method is an economic approach to allocating targets for robots through auction. However, it only considers costs in the local map of each robot. We update the local maps through fusion of both the local sensor data and the bid information, and thus the extended parts in maps enable robots to calculate costs of other robots’ targets. No extra communication is needed. The results of real robot experiments and simulations demonstrate that the improved method is more efficient than the original market-based approach and provide a proper improved method for environment exploration.

Developing an open mobile robot has been a hot topic in the AI area. In an open system, a particular component can be easily added and/or replaced. In this paper, a modular and object oriented approach is used to construct an autonomous mobile robot system. IPC (interprocess communication), which is the key mechanism in the Linux/Unix operating system, is applied to the robot design. Based on the Windows operating system, the robot owns an omnivision, and can finish tasks such as navigation and robot soccer. With a modular design, new components can be easily added to the system. Finally, the robot is applied to several tasks. The experimental results show the good performance of the robot.

For the on-line safe path planning of a mobile robot in unknown environments, the paper proposes a simple Hopfield Neural Network (HNN) planner. Without learning process, the HNN plans a safe path with consideration of "too close" or "too far". For obstacles of arbitrary shape, we prove that the HNN has no unexpected local attractive point and can find a steepest climbing path, if a feasible path(s) exists. To effectively simulate the HNN on sequential processor, we discuss algorithms with O(N) time complexity, and propose the constrained distance transformation-based Gauss-Seidel iteration method to solve the HNN. Simulations and experiments demonstrate the method has high real-time ability and adaptability to complex environments.

The environment of a middle-size autonomous robot soccer system (MARSS) is highly dynamic, competitive and partially observed. To decide quickly, behave smoothly and speedily, the proposed MARSS used a behavior-based implicit planning, and select suitable task according to the hidden state. To overcome imprecise perceptions and actions, several subtle but simple goal-driven behaviors are designed. Tightly integrated with the simple perceptions, these behaviors switch flexibly and robustly by continuous feedback. In the competitive environments, spontaneous interleaving of these behaviors implicitly plans effective behavior sequences to fulfill the game's tasks, and exhibits some important emergent behaviors making the system design more simple, robust and competitive. The method's effectiveness is verified by experiments and games.

A fuzzy satisfactory clustering algorithm is presented in this paper. It starts with two cluster centers and increases a new center if necessary. During the clustering process, the former clustering information is fully used so that the convergence rate can be speed up. A system data set can be quickly divided into several satisfactory fuzzy clusters by this algorithm. A Takagi-Sugeno type fuzzy model can then be identified. For three typical pH processes, satisfactory simulation results are obtained. The effective performance of the modified clustering algorithm is quantitatively evaluated.

This paper investigates the use of fuzzy decision making in predictive control. The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. Both equality and inequality constraints can be handled in a unified form, i.e., fuzzy soft constraints. Thus, the traditional constraints predictive control can be transferred to a standard fuzzy optimization problem. An inexact approach is used in this paper to obtain the fuzzy satisfaction optimal solution, instead of finding an exact unique optimal solution. A family of inexact solution with acceptable membership degree are found. Compared to the standard quadratic objective function, with the fuzzy decision making approach, the designer has more freedom in specifying the desired process behavior. Simulation results show the improvement of this approach when taking into account the constraints on the control or output signals.

This paper presents a systematic adaptive control strategy which can accomplish a variety of control objectives (position control, internal force control, constraints,and optimizations) for the generalized high-dimensional robotic systems (GHDRS) without restriction on target systems. Based on the concept of virtual decomposition by which a GHDRS is virtually decomposed into several objects and base-floating open chains, the motion control problem of the original system is converted into that of each object and that of each open chain, individually, while the internal force control as well as the constraint force control may be performed with respect to each object only. This feature makes it possible to implement the control algorithm of each subsystem in modularly structured hardware which can be integrated to form any specific robot controller dedicated to a specific application. In the sense of Lyapunov, it is declared that the dynamic coupling between every two physically connected subsystems can be completely represented by the so-called virtual power flows (VPFs) at the cutting points between them. Asymptotic stability of the complete system can be ensured by choosing the system Lyapunov function as the sum of all nonnegative accompanying functions assigned for the subsystems. Some possible applications based on the proposed approach are discussed. Finally, computer simulations of two PUMA 560 arms transporting a common object along a prespecified trajectory are carried out to verify the stability and robustness issues of the system.

The testing of the reachability of a robot to an object is considered. A novel approach is proposed to test whether a given generalized cuboid is reachable by a robot. The problem is normalized as a nonlinear programming problem. It is solved using the revised generalized reduced gradient algorithm. Two examples (RHINO robot and Planar 2R robot) are included to show that the proposed approach is effective and has good convergence. It is suitable for testing the reachability of a robot to an arbitrary specified generalized cuboid with desired accuracy. The algorithm can also be extended to handle other problems in robotics.>