High-Order Control Barrier Function

Modify control actions to satisfy high-order control barrier function (CBF) constraints and action bounds

Since R2026a

Libraries:
Simulink Control Design / Constraint Control

Description

The High-Order Control Barrier Function block computes the modified control actions that are closest to specified control actions subject to high-order control barrier function constraints and action bounds.

A control barrier function defines a safety set h(x) ≥ 0 based on the system state x. If the initial system start within the safety set, the High-Order Control Barrier Function block adjusts the control actions to keep the states within the safety set. The block uses a quadratic programming (QP) solver to find the control action u that minimizes ${| u - u_{0} |}^{2}$ in real time. Here, u₀ is the original control action from the controller.

The solver applies constraints based on the barrier function relative order.

Barrier Function Relative Order	Constraint Equation
First	$\begin{array}{l} L_{f} h + L_{g} h u + γ_{1} h^{β_{1}} \geq 0 \\ u_{\min} \leq u \leq u_{\max} \end{array}$
Second	$\begin{array}{l} L_{f}^{2} h + L_{g} L_{f} h u + γ_{1} h^{β_{1}} + γ_{2} (^{L_{f} h) β_{2}} \geq 0 \\ u_{\min} \leq u \leq u_{\max} \end{array}$
Third	$\begin{array}{l} L_{f}^{3} h + L_{g} L_{f}^{2} h u + γ_{1} h^{β_{1}} + γ_{2} (^{L_{f} h) β_{2}} + γ_{3} (^{L_{f}^{2} h) β_{3}} \geq 0 \\ u_{\min} \leq u \leq u_{\max} \end{array}$
Fourth	$\begin{array}{l} L_{f}^{4} h + L_{g} L_{f}^{3} h u + γ_{1} h^{β_{1}} + γ_{2} (^{L_{f} h) β_{2}} + γ_{3} (^{L_{f}^{2} h) β_{3}} + γ_{4} (^{L_{f}^{3} h) β_{4}} \geq 0 \\ u_{\min} \leq u \leq u_{\max} \end{array}$

Here:

f(x) and g(x) are the drift and control vector fields of the plant dynamics equation $\dot{x} = f (x) + g (x) u$ , respectively.
h(x) is the barrier function.
L_fⁱh is the i-th Lie derivative of h_x with respect to x, with $L_{f} h (x) = \frac{\partial h}{\partial x} f (x)$ and $L_{f}^{i} h (x) = \frac{\partial L_{f}^{i - 1} h (x)}{\partial x} f (x)$ .
L_gL_fⁱh is given by $L_{g} L_{f}^{i} h (x) = \frac{\partial L_{f}^{i} h (x)}{\partial x} g (x)$ .
γ_i and β_i are parameters for controlling the effect of the barrier certificate.
u_min is a lower bound for the control action.
u_max is an upper bound for the control action.

The High-Order Control Barrier Function block requires Optimization Toolbox™ software.

For more information on control barrier function enforcement, see Enforce Safety Constraints with Control Barrier Functions.

Examples

expand all

Compute Lie Derivatives and Configure High-Order Control Barrier Function Block

This example uses:

Open Live Script

This example shows how to compute Lie derivatives for a quadruple integrator and apply constraints using the High-Order Control Barrier Function block.

Consider the quadruple integrator system defined as:

$\begin{array}{l} {x_{}^{˙}}_{1} = x_{2} \\ {x_{}^{˙}}_{2} = x_{3} \\ {x_{}^{˙}}_{3} = x_{4} \\ {x_{}^{˙}}_{4} = u \end{array}$

Writing in the control-affine form, you get:

$\begin{array}{l} x_{}^{˙} = f (x) + g (x) u, \\ f (x) = [\begin{array}{c} x_{2} \\ x_{3} \\ x_{4} \\ 0 \end{array}], g (x) = [\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}] \end{array}$

This example provides the model quadIntegratorCBF preconfigured with two variant subsystems, one for baseline state-feedback control and the other that imposes CBF constraints.

Baseline Simulation

Design a baseline state-feedback controller using pole placement to drive all system states to zero.

A = [0 1 0 0;
     0 0 1 0;
     0 0 0 1;
     0 0 0 0];
B = [0;0;0;1];
C = eye(4);
D = zeros(4,1);
sys = ss(A,B,C,D);
initcond = [4;0;0;0];
p = [-0.5+1.5j, -0.5-1.5j,-5,-6]; 
K = place(A,B,p);

Simulate the model with the baseline controller.

mdl = "quadIntegratorCBF";
open_system("quadIntegratorCBF")
set_param([mdl+"/Select Input"], 'LabelModeActiveChoice','nominal')
sim(mdl,15);
open_system("quadIntegratorCBF/Scope")

Safety Set and Lie Derivatives

Now, define a control barrier function (CBF) for this system that ensures $x_{1}$ stays above the boundary $x_{1} \geq 0.5$ .

$h (x) = x_{1} - 0.5$

To determine the relative degree, compute successive time derivatives of $h (x)$ until $u$ appears explicitly.

$h_{}^{˙} = {x_{}^{˙}}_{1} = x_{2}$

$h_{}^{¨} = {x_{}^{˙}}_{2} = x_{3}$

$h_{}^{⃛} = {x_{}^{˙}}_{3} = x_{4}$

$h_{}^{⃜} = {x_{}^{˙}}_{4} = u$

The control input term $u$ appears in the fourth derivative of $h (x)$ , so the relative degree for this system is 4. Compute the required Lie derivatives for relative degree 4.

$L_{f} h = [\begin{array}{cccc} \frac{\partial}{\partial x_{1}} h (x) & \frac{\partial}{\partial x_{2}} h (x) & \frac{\partial}{\partial x_{3}} h (x) & \frac{\partial}{\partial x_{4}} h (x) \end{array}] f (x) = [\begin{array}{cccc} 1 & 0 & 0 & 0 \end{array}] [\begin{array}{c} x_{2} \\ x_{3} \\ x_{4} \\ 0 \end{array}] = x_{2}$

Similarly,

$L_{f}^{2} h = (\frac{\partial}{\partial x} L_{f} h) f (x) = [\begin{array}{cccc} 0 & 1 & 0 & 0 \end{array}] [\begin{array}{c} x_{2} \\ x_{3} \\ x_{4} \\ 0 \end{array}] = x_{3}$

$L_{f}^{3} h = (\frac{\partial}{\partial x} L_{f}^{2} h) f (x) = [\begin{array}{cccc} 0 & 0 & 1 & 0 \end{array}] [\begin{array}{c} x_{2} \\ x_{3} \\ x_{4} \\ 0 \end{array}] = x_{4}$

$L_{f}^{4} h = (\frac{\partial}{\partial x} L_{f}^{3} h) f (x) = [\begin{array}{cccc} 0 & 0 & 0 & 1 \end{array}] [\begin{array}{c} x_{2} \\ x_{3} \\ x_{4} \\ 0 \end{array}] = 0$

${L_{g} L}_{f}^{3} h = (\frac{\partial}{\partial x} L_{f}^{3} h) g (x) = [\begin{array}{cccc} 0 & 0 & 0 & 1 \end{array}] [\begin{array}{c} 0 \\ 0 \\ 0 \\ 0 \end{array}] = 1$

Simulation with Constraints

To impose the CBF constraints, use the High-Order Control Barrier Function block. In the Simulink model, the CBF Constraint variant of the Select Input subsystem shows how to provide the required inputs: the baseline controller output, the control barrier function, and the computed Lie derivatives.

By default, the order (relative degree) parameter is set to 2, so you must configure the block parameter as follows.

Enable the CBF Constraint variant and run the simulation.

x1min = 0.5;
set_param([mdl+"/Select Input"], 'LabelModeActiveChoice','enableCBF')
sim(mdl,15);
open_system("quadIntegratorCBF/Scope")

Additionally, you can tune the Poles for exponential decay rate parameter to speed up convergence. For example, set the decay to to -2 and simulate the model. $x_{1}$ converges faster to the boundary but causes more overshoot in other states.

set_param([mdl+"/Select Input/CBF Constraint/High-Order Control Barrier Function"],"DecayPoles","-2")
sim(mdl,15);
open_system("quadIntegratorCBF/Scope")

Extended Examples

New

Safe PID Controller for Two Link Robot using High-Order Control Barrier Function

Apply high-order CBF constraints to ensure safety for a two-link robot.

Since R2026a
Open Live Script

Add Safety Constraint to Simulate Two-Link Robot with SAC Agent

Add high-order barrier function to safely simulate a two-link robot model with a SAC agent.

(Reinforcement Learning Toolbox)

Ports

Input

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u0 — Control actions
scalar | vector

Unmodified control actions, specified as a scalar or a vector.

If the Number of actions parameter is 1, connect u0 to a scalar signal. Otherwise, connect u0 to a vector signal with length equal to Number of actions.

h_x — Control barrier function
scalar | vector

Control barrier function, defined as the following safety set for plant states.

${x : h (x) \geq 0}$

Connect hx to an N_c-by-1 signal, where N_c is equal to the Number of barrier certificates parameter.

L_fh — First Lie derivative of h along f
scalar | vector

First Lie derivative of h(x) along the f(x), defined as:

$L_{f} h (x) = \frac{\partial h}{\partial x} f (x)$

Connect this port to an N_c-by-1 signal, where N_c is equal to Number of barrier certificates.

L_gh — First Lie derivative of h along g
scalar | vector | matrix

First Lie derivative of h(x) along the g(x), defined as:

$L_{g} h (x) = \frac{\partial h}{\partial x} g (x)$

Connect this port to an N_c-by-N_u signal, where N_c is equal to Number of barrier certificates and N_u is equal to Number of actions.

Dependencies

To enable this port, set Order to 1.

L_f²h — Second Lie derivative along f
scalar | vector

Second Lie derivative of h(x) along the f(x), defined as:

$L_{f}^{2} h (x) = \frac{\partial L_{f} h (x)}{\partial x} f (x)$

Connect this port to an N_c-by-1 signal, where N_c is equal to Number of barrier certificates.

Dependencies

To enable this port, set Order to 2, 3, or 4.

L_gL_fh — Lie derivative of L_fh along g
scalar | vector | matrix

Lie derivative of L_fh(x) along the g(x), defined as:

$L_{g} L_{f} h (x) = \frac{\partial L_{f} h (x)}{\partial x} g (x)$

Connect this port to an N_c-by-N_u signal, where N_c is equal to Number of barrier certificates and N_u is equal to Number of actions.

Dependencies

To enable this port, set Order to 2.

L_f³h — Third Lie derivative along f
scalar | vector

Third Lie derivative of h(x) along the f(x), defined as:

$L_{f}^{3} h (x) = \frac{\partial L_{f}^{2} h (x)}{\partial x} f (x)$

Connect this port to an N_c-by-1 signal, where N_c is equal to Number of barrier certificates.

Dependencies

To enable this port, set Order to 3.

L_gL_f²h — Lie derivative of L²_fh along g
scalar | vector | matrix

Lie derivative of L²_fh(x) along the g(x), defined as:

$L_{g} L_{f}^{2} h (x) = \frac{\partial L_{f}^{2} h (x)}{\partial x} g (x)$

Connect this port to an N_c-by-N_u signal, where N_c is equal to Number of barrier certificates and N_u is equal to Number of actions.

Dependencies

To enable this port, set Order to 3.

L_f⁴h — Fourth Lie derivative along f
scalar | vector

Fourth Lie derivative of h(x) along the f(x), defined as:

$L_{f}^{4} h (x) = \frac{\partial L_{f}^{3} h (x)}{\partial x} f (x)$

Connect this port to an N_c-by-1 signal, where N_c is equal to Number of barrier certificates.

Dependencies

To enable this port, set Order to 4.

L_gL_f³h — Lie derivative of L³_fh along g
scalar | vector | matrix

Lie derivative of L³_fh(x) along the g(x), defined as:

$L_{g} L_{f}^{3} h (x) = \frac{\partial L_{f}^{3} h (x)}{\partial x} g (x)$

Connect this port to an N_c-by-N_u signal, where N_c is equal to Number of barrier certificates and N_u is equal to Number of actions.

Dependencies

To enable this port, set Order to 4.

umax — Action signal upper bounds
scalar | vector

To specify run-time upper bounds to the action signals, enable this input port. If this port is disabled, the block does not apply any upper bounds to the control actions.

If the Number of actions parameter is 1, connect umax to a scalar signal. Otherwise, connect umax to a vector signal with length equal to Number of actions.

Dependencies

To enable this input port, select the Use external source for upper bound parameter.

umin — Action signal lower bounds
scalar | vector

To specify run-time lower bounds to the action signals, enable this input port. If this port is disabled, the block does not apply any lower bounds to the control actions.

If the Number of actions parameter is 1, connect umin to a scalar signal. Otherwise, connect umin to a vector signal with length equal to Number of actions.

Dependencies

To enable this input port, select the Use external source for lower bound parameter.

Output

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u* — Modified control action
scalar | vector

Modified control action returned by the QP solver.

If the solver finds a solution before reaching the maximum number of iterations, u* outputs this optimal solution.

If the solver reaches the maximum number of iterations, optimization stops and u* outputs a suboptimal solution.

If the initial optimization problem is infeasible, the returned control action depends on the whether the block is configured to ignore constraint or action bounds. For more information, see the exitflag parameter.

If the Number of actions parameter is 1, u* outputs a scalar signal. Otherwise, u* outputs a vector signal with length equal to Number of actions.

exitflag — Optimization status
`1` | `0` | negative integer

Optimization status of the QP solver. The following table shows the possible status values.

Exit Flag Description

1 The solver converged to an optimal solution with all constraints and bounds active. In this case, u* outputs the optimal control actions.

0 The solver reached the maximum number of iterations. The control actions output in u* might be suboptimal.

negative integer

Exit Flag	Description
`1`	The solver converged to an optimal solution with all constraints and bounds active. In this case, u* outputs the optimal control actions.
`0`	The solver reached the maximum number of iterations. The control actions output in u* might be suboptimal.
negative integer	The initial optimization problem was infeasible and one of the following scenarios applies. Rerunning the optimization without action bounds did not produce a feasible solution. Rerunning the optimization without constraint bounds did not produce a feasible solution. In this case, the control actions output in u* are zero.

The initial optimization problem was infeasible and one of the following scenarios applies.

Rerunning the optimization without action bounds did not produce a feasible solution.
Rerunning the optimization without constraint bounds did not produce a feasible solution.

In this case, the control actions output in u* are zero.

Dependencies

To enable this output port, select the Optimization status parameter.

Parameters

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To edit block parameters interactively, use the Property Inspector. From the Simulink^® Toolstrip, on the Simulation tab, in the Prepare gallery, select Property Inspector.

Number of states — Number of plant states
`1` (default) | positive integer

Specify the number of plant states.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`nx`
Values:	`"1"` (default) \| positive integer in quotes

Example: set_param(gcb,"nx","3")

Number of actions — Number of control actions
`1` (default) | positive integer

Specify the number of control actions to apply bounds to and optimize.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`nu`
Values:	`"1"` (default) \| positive integer in quotes

Example: set_param(gcb,"nu","2")

Number of barrier certificates — Number of barrier certificate constraints
`1` (default) | positive integer

Specify the number of barrier certificate constraints to enforce.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`nc`
Values:	`"1"` (default) \| positive integer in quotes

Example: set_param(gcb,"nc","2")

Order — Relative degree
`2` (default) | `1` | `3` | `4`

Specify the barrier function order. This is the number of time derivatives of h you must take before the control term u appears explicitly

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`Order`
Values:	`"2"` (default) \| `"1"` \| `"3"` \| `"4"`

Example: set_param(gcb,"Order","2")

Use exponential high-order CBF — Flag to enable exponential high-order CBF
`on` (default) | `off`

Enforce strict state-dependent high-relative degree safety for nonlinear systems. When you enable this option, the software automatically computes the constraint factor gains γ_i based on the poles specified in the Poles for exponential decay rate parameter.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`ExponentialCBF`
Values:	`"on"` (default) \| `"off"`

Example: set_param(gcb,"ExponentialCBF","off")

Poles for exponential decay rate — Pole values
`-1` (default) | real negative scalar | `nc`-by-`no` matrix

Pole values for exponential decay rate, specified as a real negative scalar or matrix of size nc-by-no, where nc is the Number of barrier certificates and no is the Order.

Dependencies

To enable this option, select Use exponential high-order CBF.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`DecayPoles`
Values:	`"-1"` (default) \| real negative scalar in quotes \| `nc`-by-`no` matrix in quotes

Example: set_param(gcb,"DecayPoles","[-1,-2;-3,-4]")

Constraint factor (gamma) — Constraint factor
`10` (default) | positive scalar | `nc`-by-`no` matrix

Constraint factor γ_i in the barrier certificate constraint, specified as a positive scalar or matrix of size nc-by-no, where nc is the Number of barrier certificates and no is the Order.

Dependencies

To enable this option, disable Use exponential high-order CBF.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`gamma`
Values:	`"10"` (default) \| positive scalar in quotes

Example: set_param(gcb,"gamma","4")

Constraint power (beta) — Constraint power
`1` (default) | positive scalar | `nc`-by-`no` matrix

Constraint power β in the barrier certificate constraint, specified as a positive scalar or matrix of size nc-by-no, where nc is the Number of barrier certificates and no is the Order.

Dependencies

To enable this option, disable Use exponential high-order CBF.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`beta`
Values:	`"1"` (default) \| positive scalar in quotes

Example: set_param(gcb,"beta","2")

Use external source for upper bound (umax) — Add upper action bound input port
`off` (default) | `on`

Select this parameter to add the umax input port for external upper action bounds.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`external_umax`
Values:	`"off"` (default) \| `"on"`

Example: set_param(gcb,"external_umax","on")

Use external source for lower bound (umin) — Add lower action bound input port
`off` (default) | `on`

Select this parameter to add the umin input port for external lower action bounds.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`external_umin`
Values:	`"off"` (default) \| `"on"`

Example: set_param(gcb,"external_umin","on")

Data type — Data type
`double` (default) | `single` | `<data type expression>`

Specify the output data type.

The Data Type Assistant helps you set data attributes. To use the Data Type Assistant, click .

You can specify the following data types.

Built in — single or double data types.
Expression — Use a MATLAB expression that specifies the type.

For more information, see Specify Data Types Using Data Type Assistant.

Sample time (seconds) — Optimization sample time
`0.1` (default) | positive scalar

Specify the sample time for running the optimization.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`Ts`
Values:	`"0.1"` (default) \| positive scalar in quotes

Example: set_param(gcb,"Ts","0.2")

Maximum iterations — Maximum optimization iterations
`200` (default) | positive integer

Specify the maximum number of optimization iterations.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`maxiter`
Values:	`"200"` (default) \| positive integer in quotes

Example: set_param(gcb,"maxiter","300")

Constraint tolerance — Tolerance for constraint violations
`1e-6` (default) | nonnegative scalar

Specify a tolerance value for constraint violations.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`tol`
Values:	`"1e-6"` (default) \| nonnegative scalar in quotes

Example: set_param(gcb,"tol","1e-4")

Optimization status (exitflag) — Add exit flag output port
`off` (default) | `on`

Select this parameter to add the exitflag output port for the optimization status of the QP solver.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

Parameter:	`output_exitflag`
Values:	`"off"` (default) \| `"on"`

Example: set_param(gcb,"output_exitflag","on")

Extended Capabilities

expand all

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

Version History

Introduced in R2026a

High-Order Control Barrier Function

Description

Examples

Compute Lie Derivatives and Configure High-Order Control Barrier Function Block

Extended Examples

Safe PID Controller for Two Link Robot using High-Order Control Barrier Function

Add Safety Constraint to Simulate Two-Link Robot with SAC Agent

Ports

Input

u0 — Control actions scalar | vector

hx — Control barrier function scalar | vector

Lfh — First Lie derivative of h along f scalar | vector

Lgh — First Lie derivative of h along g scalar | vector | matrix

Dependencies

Lf2h — Second Lie derivative along f scalar | vector

Dependencies

LgLfh — Lie derivative of Lfh along g scalar | vector | matrix

Dependencies

Lf3h — Third Lie derivative along f scalar | vector

Dependencies

LgLf2h — Lie derivative of L2fh along g scalar | vector | matrix

Dependencies

Lf4h — Fourth Lie derivative along f scalar | vector

Dependencies

LgLf3h — Lie derivative of L3fh along g scalar | vector | matrix

Dependencies

umax — Action signal upper bounds scalar | vector

Dependencies

umin — Action signal lower bounds scalar | vector

Dependencies

Output

u* — Modified control action scalar | vector

exitflag — Optimization status 1 | 0 | negative integer

Dependencies

Parameters

Number of states — Number of plant states 1 (default) | positive integer

Programmatic Use

Number of actions — Number of control actions 1 (default) | positive integer

Programmatic Use

Number of barrier certificates — Number of barrier certificate constraints 1 (default) | positive integer

Programmatic Use

Order — Relative degree 2 (default) | 1 | 3 | 4

Programmatic Use

Use exponential high-order CBF — Flag to enable exponential high-order CBF on (default) | off

Programmatic Use

Poles for exponential decay rate — Pole values -1 (default) | real negative scalar | nc-by-no matrix

Dependencies

Programmatic Use

Constraint factor (gamma) — Constraint factor 10 (default) | positive scalar | nc-by-no matrix

Dependencies

Programmatic Use

Constraint power (beta) — Constraint power 1 (default) | positive scalar | nc-by-no matrix

Dependencies

Programmatic Use

Use external source for upper bound (umax) — Add upper action bound input port off (default) | on

Programmatic Use

Use external source for lower bound (umin) — Add lower action bound input port off (default) | on

Programmatic Use

Data type — Data type double (default) | single | <data type expression>

Sample time (seconds) — Optimization sample time 0.1 (default) | positive scalar

Programmatic Use

Maximum iterations — Maximum optimization iterations 200 (default) | positive integer

Programmatic Use

Constraint tolerance — Tolerance for constraint violations 1e-6 (default) | nonnegative scalar

Programmatic Use

Optimization status (exitflag) — Add exit flag output port off (default) | on

Programmatic Use

Extended Capabilities

C/C++ Code Generation Generate C and C++ code using Simulink® Coder™.

Version History

See Also

Topics

u0 — Control actions
scalar | vector

h_x — Control barrier function
scalar | vector

L_fh — First Lie derivative of h along f
scalar | vector

L_gh — First Lie derivative of h along g
scalar | vector | matrix

L_f²h — Second Lie derivative along f
scalar | vector

L_gL_fh — Lie derivative of L_fh along g
scalar | vector | matrix

L_f³h — Third Lie derivative along f
scalar | vector

L_gL_f²h — Lie derivative of L²_fh along g
scalar | vector | matrix

L_f⁴h — Fourth Lie derivative along f
scalar | vector

L_gL_f³h — Lie derivative of L³_fh along g
scalar | vector | matrix

umax — Action signal upper bounds
scalar | vector

umin — Action signal lower bounds
scalar | vector

u* — Modified control action
scalar | vector

exitflag — Optimization status
`1` | `0` | negative integer

Number of states — Number of plant states
`1` (default) | positive integer

Number of actions — Number of control actions
`1` (default) | positive integer

Number of barrier certificates — Number of barrier certificate constraints
`1` (default) | positive integer

Order — Relative degree
`2` (default) | `1` | `3` | `4`

Use exponential high-order CBF — Flag to enable exponential high-order CBF
`on` (default) | `off`

Poles for exponential decay rate — Pole values
`-1` (default) | real negative scalar | `nc`-by-`no` matrix

Constraint factor (gamma) — Constraint factor
`10` (default) | positive scalar | `nc`-by-`no` matrix

Constraint power (beta) — Constraint power
`1` (default) | positive scalar | `nc`-by-`no` matrix

Use external source for upper bound (umax) — Add upper action bound input port
`off` (default) | `on`

Use external source for lower bound (umin) — Add lower action bound input port
`off` (default) | `on`

Data type — Data type
`double` (default) | `single` | `<data type expression>`

Sample time (seconds) — Optimization sample time
`0.1` (default) | positive scalar

Maximum iterations — Maximum optimization iterations
`200` (default) | positive integer

Constraint tolerance — Tolerance for constraint violations
`1e-6` (default) | nonnegative scalar

Optimization status (exitflag) — Add exit flag output port
`off` (default) | `on`

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.